blog.banast.as

John Lear and the Alien Cover-Up

Tue, 20 Oct 2026 00:00:00 GMT

John Lear was my favorite guest on Art Bell’s Coast to Coast AM. He believed aliens had helped create humans, that the government had agreed to let them abduct people, and that a machine on the moon collected souls after death.

He also came to believe that the public should not be told the full story. After years of arguing that people deserved to know, he decided they had little idea what they were asking to learn.

His reasons involved almost everything he believed about the visitors, the government, and what humans were for.

Aircraft and their occupants

Lear was the son of Bill Lear, the inventor associated with the Learjet, and a commercial pilot himself. He discussed unusual aircraft in terms of propulsion, flight characteristics, and test schedules.

Bob Lazar said he had worked on recovered alien craft at a facility called S-4 in Nevada. Lear described meeting him through Gene Huff, who had come to appraise Lear’s house and brought Lazar along to help with the measuring. According to Lear, Lazar was initially dismissive of his UFO interests. Later, he arrived at Lear’s home and told him he had seen a disc himself.

Lear also described going into the desert with Lazar and Huff to watch a test flight. He struggled to focus an eight-inch telescope, finally saw a glowing disc, and then bumped the tripod as he stepped aside to let Huff look.

Lear believed many kinds of aliens were visiting Earth. Some were friendly, some hostile, and many indifferent. He described the little gray beings associated with abductions as manufactured organisms working for others whose identities and purposes remained unknown.

Technology in exchange for abductions

Lear outlined the government’s involvement in a statement he began circulating in December 1987. In the August 1988 revision preserved by UFOmind, he described a secret group called MJ-12 negotiating with extraterrestrials. The government would tolerate abductions in exchange for advanced technology, with the aliens supplying lists of the people taken.

According to Lear, the aliens took far more people than they reported. The technology was less useful than officials expected, too. They could not operate the equipment independently or use it against its makers.

At Dulce, an underground facility he described near the Colorado–New Mexico border, humans and aliens conducted genetic experiments together. Lear said a deadly confrontation began after a security officer entered an area carrying a conventional weapon. The aliens killed the officer, and other security personnel went in after him. Lear described heavy losses among the humans.

In Lear’s account, the government could keep secrets from its own population but could not enforce its agreement with the visitors. Officials continued the cover-up partly to conceal the consequences of their own decisions.

Abductions and human origins

Lear said Budd Hopkins’s Missing Time and Whitley Strieber’s Communion frightened him. He visited Hopkins and listened to recordings of people describing abductions. Partway through, he asked Hopkins how he could live with hearing these accounts. Hopkins told him he got used to it.

His explanation for the abductions was that humanity was an ongoing experiment. The visitors were checking on something they had made.

Lear attributed this idea to documents Lazar said he had read. Humans had undergone 65 separate genetic corrections and were referred to as “containers.” Lear considered several possibilities for what the containers held, including biological substances, before concentrating on the soul.

Lear connected religion to the same experiment. He believed the beings responsible had introduced religious teachings and demonstrations of miraculous power to give humans rules to live by. Those rules helped keep people from harming one another while the experiment continued.

He said he did not know the experiment’s ultimate purpose or whether it was succeeding.

The machine on the moon

By 2004, Lear had an explanation for how souls moved between lives. Richard Hoagland’s interpretation of lunar imagery had drawn his attention to a tall structure with a large cube at the top, facing Earth. Lear believed it was an antenna for receiving and transmitting souls.

In his explanation, the machine received a soul when a person died and transmitted one when a person was born. He also believed that most people could not access memories of previous lives, preventing them from consciously using what they had learned before.

He suggested that people who died underwater could become lost souls because the machinery could not retrieve them.

Lear also believed the moon had been brought into its present position. It housed the equipment he thought was managing human souls.

Secrecy and public expectations

Lear believed the organization managing alien information withheld it even from presidents and other elected officials. Politicians came and went. The people running the operation stayed.

He believed information from space missions was altered before it reached the public. Images concealed lunar structures, and official accounts misrepresented conditions on other planets. He described Venus as inhabited and much more like Earth than people had been told.

He described E.T. and Close Encounters of the Third Kind as part of an effort to prepare people for contact by showing them friendly aliens.

Lear believed the public was being encouraged to expect friendly visitors while officials were discovering that the aliens would not honor their agreement.

Why he came to oppose disclosure

By the time Lear was arguing against disclosure, the information he wanted withheld included human origins, the purpose of religion, and government participation in abductions. An announcement would tell people that they had been made for reasons they did not understand and that institutions they trusted had helped conceal it.

He wrote a hypothetical government briefing to explain his position. The recipient would be shown the entire history, including the government’s involvement, and then asked whether it should all be released. Lear wanted people to consider the contents of the announcement before deciding that they were ready for it.

Lear distinguished between discussing these ideas publicly and having them officially confirmed. People could hear his account and go about their lives. A government announcement would make the information harder to dismiss.

Lear thought the effect on religious believers would be particularly severe. In his account, the beings people might have understood as gods were conducting an experiment, and the humans involved did not know its purpose. He believed that officially confirming this would cause more harm than continuing to withhold it.

Action Movies Are Floor Plans Under Stress

Sat, 06 Jun 2026 00:00:00 GMT

An action scene is easier to follow when the audience understands the space and what makes it dangerous.

Without that understanding, an elaborate action sequence can be difficult to follow. The stunt can be expensive. The camera can shake in all the right places. The edit can cut fast enough to imply danger. Someone can jump, crash, punch, run, shoot, fall, recover, and keep going.

But if the audience does not understand the space, it is hard to judge whether a character is escaping danger or getting closer to it.

A good action scene establishes exits, obstacles, blind spots, and time limits before characters have to act on them.

That is why Die Hard still works as the clean example. Nakatomi Plaza is not just a tall building where action happens. Its layout determines what the characters can do.

Establishing Nakatomi Plaza

The party matters because it establishes the building as social space before it becomes tactical space. Elevators are convenience. Offices are status. The vault is corporate infrastructure. The roof is just the roof.

Then the takeover changes the meaning of every piece.

The elevators become routes and traps. The floors become layers of risk. The roof appears to offer an escape, then becomes dangerous. The vault becomes the real objective. Radios turn distance into conversation. Glass turns a hallway into a weapon.

The script establishes the building, then changes which routes and spaces are safe.

John McClane is not interesting because he is invincible. He is interesting because he does not fully know the building. He is moving through spaces that Hans Gruber thinks he controls. The usefulness and danger of each location keep changing. A stairwell is safe until it is not. A radio is power until it exposes you. A hostage floor is stable until one person is missing. A roof is salvation until it becomes a killing field.

Knowing the layout helps us understand how little time McClane has to act.

Containment is not just geography

Contained action does not mean small. It means rules.

The Nostromo in Alien is a ship, but the ship is not the point by itself. The point is quarantine. A closed door is not just a closed door. It is a moral test. In the Kane airlock sequence, the decision is whether to open the door: if the contaminated body stays outside, the crew has a chance. If the body comes in, everyone aboard is exposed to the danger.

The scene works because safety protocol collides with fear, loyalty, labor, and corporate logic. The conflict concerns who can override the safety rule and put the crew at risk.

Jaws does the same thing in the open air, which is harder. Amity is not a claustrophobic spaceship. It is a town, a beach, a season, a tourism economy, a mayor, a police chief, and officials reluctant to acknowledge a threat to tourism. The shark works before it is seen because the town is built to disbelieve danger.

The beach is geography, but the pressure is civic. Where can people swim? What can be closed? Who has authority? What counts as evidence? What would closing the beach cost this weekend?

That is why the first half of Jaws is not filler before the boat. It establishes why the town has failed to protect people. By the time the movie moves onto the Orca, the characters must deal with the shark while relying on limited equipment and conflicting judgments, far from help.

Jurassic Park earns its spectacle the same way. The park is sold as control: fences, locks, tour cars, maps, paddocks, visitor routes, command rooms, and protocols. Then the storm and Nedry's sabotage disable the systems visitors depend on for protection. The T. rex attack is not only a dinosaur set piece. It works because the movie has already established the importance of the fence, the stalled cars, the glass, the mud, the drop, the missing radio contact, and the staff’s mistaken confidence in their control of the park.

Architecture and authority

The best version of this idea is not limited to traditional action movies.

Parasite uses a house to show differences in wealth and power. Stairs, windows, smell, rain, and basement access are not production design decoration. They show how differently the residents and workers experience the same place, and who has privacy, comfort, and protection.

When the basement is revealed, the house does not simply gain a new location. We have to reconsider who lives there, who depends on the owners, and what the visible rooms conceal.

The Menu does it with service. Hawthorn is a restaurant, but each course advances the plot. Each course teaches a new rule. Each ritual makes it harder for the guests to leave. The room starts as luxury and becomes containment. Service becomes surveillance. The chef's authority is not abstract; it is built into where people sit, when food arrives, who may leave, and how the staff acts together.

Worldbuilding helps the audience understand the rules that constrain the characters.

The rules of a place become dramatic when violating or obeying them has consequences.

Teaching the audience the mission

Top Gun: Maverick is useful because it is so explicit about this. The mission works because the movie makes the audience rehearse it before the final run.

Altitude. Turns. Target. Climb. G-force. Timing. Wingman survival. The impossible route is explained so many times that by the end we are not just watching jets go fast. We are reading the attempt against rules we understand.

That is why the final sequence has pressure. We know what too high means. We know what too slow means. We know what missing the climb means. We know why one pilot's success can still doom another pilot. The unlikely success is satisfying because we understand what the pilots had to accomplish.

Bad spectacle skips that step. It adds scale without establishing what the audience needs to understand. More vehicles. More debris. More bodies. More cuts. More money on screen. None of that matters if the viewer cannot tell what changed.

The audience needs to recognize progress, setbacks, and danger.

Scripts are systems

Plot summary flattens this into what happens. Script analysis should be able to show how the pressure is built.

A script is not just dialogue and scene order. It establishes what characters can attempt and what they risk. It tells you what the arena is, which institution controls it, what rule gets violated, what the cost is, which entrance changes the room, when a safe location becomes dangerous, and what the audience has to learn before a reversal makes sense.

That is the direction of the OverBlack work: scripts as systems you can query by craft function, not just title, decade, genre, or writer.

Can I find scenes where a house becomes an institution? Where a route must be completed within a time limit? Where a body becomes evidence? Where service gives one character control over another? Where evidence of danger conflicts with a town’s economic interests? Where a mission briefing prepares the audience to follow a later action sequence?

That is more useful to me than another archive of famous lines. Lines matter. Plot matters. But craft often lives in the relations between place, rule, pressure, and cost.

The test

Can you describe the location’s rules before an action scene changes them?

Where are we? Who controls access? Which route is safe? What happens if a door opens or stays shut? What becomes harder as time passes?

If the script answers those questions, the audience can understand why a character’s next move matters. That understanding gives the stunt, chase, or escape its suspense.

Netflix Lighting Is a Failure to Choose

Tue, 26 May 2026 00:00:00 GMT

The complaint is usually phrased badly: "movies don't look like movies anymore." That is not quite right. Some of them do. Some old movies looked terrible. Some new ones look gorgeous. The useful complaint is narrower: too many images show everything equally, with little control over where the viewer looks.

That distinction matters because otherwise the argument turns into lazy nostalgia. The movies you saw between 10 and 20 tend to become your permanent definition of what movies look like. Robert Tolppi makes a version of this caveat in his video on "Netflix lighting," and it is worth considering. Nostalgia explains some of the reaction. It does not explain all of it.

Production norms changed.

Tolppi's useful point is not that digital cameras ruined movies or that LED fixtures are fake light. That version is too easy. Modern sensors capture more color and contrast than older cameras could. LED units are brighter, cooler, lighter, more efficient, easier to rig, easier to hide, and easier to control. The tools got better.

Those improvements also make it easier to defer visual decisions.

What the complaint describes

"Netflix lighting" is not a company style guide. It is a production behavior: high-key, low-contrast, front-filled, evenly exposed images where most of the frame is clearly visible and little distinguishes the subject from its surroundings.

Blaming Netflix alone is too convenient. This shows up anywhere production culture prizes safe flexibility over visual authorship. Flat light is not always ugly. High-key images can be beautiful. The issue is not brightness. The issue is non-commitment.

Correct exposure does not establish where the viewer should look.

Lighting is not just illumination. It is shape. A backlight separates a body from the background. Negative fill gives a face structure. A motivated source connects the light to something in the scene. Contrast and shadow direct attention by making some details more visible than others.

The point is not to make every frame noir. The point is to decide where the eye should go and what can remain unseen.

Flatness is rational

Of course productions drift toward flat lighting. It keeps everything usable. It protects coverage. It helps VFX. It survives HDR delivery, streaming compression, executive notes, uncertain downstream decisions, reshoots, pickups, changing edits, and the client who suddenly notices the background actor in the corner. If the set is broadly filled, you can turn around faster, cover more angles, save more takes, and leave more decisions for post.

Those practical benefits can make a visually undistinguished image easier to approve.

The problem is not that nobody knows how to make the image more interesting. The problem is that interesting images often require someone to make a decision before everyone who reviews the footage has had a chance to request changes.

A specific lighting choice limits later options. If one side of the face is allowed to fall off, that is a decision. If the sun is backlighting the scene, the schedule has to respect the sun. If the key is motivated by a lamp, blocking has to respect the lamp. If the frame is built around shadow, somebody has to defend the shadow when the note comes back asking whether we can see more.

Blanket coverage feels safer because it preserves options.

Preserving all those options can weaken the composition.

Cinematography is partly the art of making irreversible decisions before the movie is fully known. That is uncomfortable under modern production pressure, because production workflows often encourage teams to defer decisions. More data. More takes. More notes. More recovery. More flexibility.

If every setup serves that flexibility, the images may begin to look alike.

Why Wicked is a useful example

Tolppi uses Wicked because fantasy musicals should be the easiest place to justify stylized, committed lighting. Oz is not a documentary location. If there is a genre where color, shape, theatricality, and motivated unreality should have permission, it is this one.

That does not mean Wicked as a whole is bad. This is not a review of the movie, and a trailer clip or selected scene is not a full artistic judgment. The useful point is narrower: when even a huge fantasy production can produce examples that feel broadly filled and ordinary, the issue is not lack of available technology.

The daylight problem Tolppi highlights is a good operational example. Put actors into harsh front sun and the image flattens: squinting faces, flat exposure, no separation. Put the sun behind them, then shape the face with bounce or soft fill, and the frame gets depth.

The crew still needs to ask: where is the sun? Where is the key? What side of the face is allowed to fall off? What remains black? What is the viewer supposed to look at?

Those are specific choices the crew must make.

The production-tech pattern

The lighting argument connects to VFX, virtual production, and AI without turning into a complaint about any of them. Virtual production can restore intention by letting directors, DPs, production designers, VFX supervisors, and producers see context earlier. That was the useful point in When Game Engines Meet Hollywood: real-time tools matter when they let teams assess decisions earlier and make changes more cheaply.

But the same tool can become another reason to use general, low-contrast lighting. A volume can give actors and camera a real environment to respond to, or it can become a dim wraparound wall that encourages soft, general, noncommittal light because that is the easiest thing to keep from breaking.

AI image tools have the same trap. They can generate polished images quickly. They can be useful for reference, planning, mood, iteration, and disposable exploration. But they do not know which constraint should remain. They do not know what should be dark. They cannot decide which imperfection matters to this scene.

These tools help when they make a chosen look easier to achieve. They become less useful when they allow the team to keep postponing that choice.

That is also the point of Friction Is About to Become a Luxury. Not all friction is waste. Some time and effort are necessary to make a visual choice.

Lighting has always understood that. Waiting for the sun is friction. Cutting light with flags is friction. Reblocking around a motivated source is friction. Defending a dark frame in review is friction. Some of that friction is the work.

What still requires judgment

When tools get better, taste matters more, not less.

That is the through-line from Phil Tippett to Toy Story to NVIDIA. The tool changes. The interface changes. The cost of output changes. The crew still has to decide what suits the scene.

The scarce thing is no longer exposure. The camera can see into the shadows. The lights can fill the room. Post can recover, isolate, mask, track, relight, denoise, compress, expand, and rescue more than ever. The machine can preserve more information than the old one could.

The cinematographer still has to decide which details the viewer should see.

Knowing when not to fill. Knowing what to leave black. Knowing where the viewer should look. Knowing when a face needs softness and when it needs structure. Knowing when the background should disappear. Knowing when a beautiful image is wrong for the scene and when an unusual image best expresses what is happening.

Better equipment cannot make those decisions for the crew.

Make the choice earlier

Better cameras, lights, planning tools, and post tools should help teams see a proposed look, assess it, and maintain it through production.

That requires agreement about what matters in the image: where the viewer should look, what they need to see, and what can remain dark. Resolve those questions early enough that the schedule, blocking, and lighting can support the answers.

Friction Is About to Become a Luxury

Sat, 11 Apr 2026 00:00:00 GMT

I've bought more vinyl records, physical books, and 4K Blu-rays in the past year than in the previous decade combined. Not because the content is different. Because I spend time with it differently. A vinyl record takes twenty minutes a side. A book requires hours. A disc means committing to a movie from opening titles to end credits. I’m less inclined to skip between things when I use these formats.

I still want software that works efficiently. Bad friction wastes time. Useful friction protects judgment. The problem is that software usually cannot tell the difference.

For years, the entire direction of software has been to remove the space between wanting something and having it. One-click buying. Instant streaming. Same-day delivery. Autofill, auto-play, auto-complete, auto-generate, auto-summarize. Fewer steps. Fewer questions. Fewer places where a human has to stop and decide.

Much of that was good. Broken forms, pointless approvals, and duplicated data entry should be fixed or removed. Bad search, missing files, platform lock-in, unclear ownership, and bureaucracy that protects nobody should all be treated as bugs.

But when friction disappears everywhere, other things disappear with it: review, deliberation, ownership, memory, source checking, taste, consent, privacy, accountability, and the moment where someone in the room asks, "Should we?"

As routine work becomes faster, deliberate review may account for more of the time and cost of doing it well.

Bad friction and useful friction

Bad friction is re-entering the same production data into five systems.

Useful friction is having one person verify the call sheet before it goes out.

Bad friction is waiting three days because nobody knows who approves the deck.

Useful friction is making the approval owner explicit before the client sees it.

Bad friction is manually hunting through old folders because search is broken.

Useful friction is keeping source files and version history so the team can prove what changed.

Bad friction is adding legal review to everything by default because nobody wants to think.

Useful friction is adding legal review when rights, likeness, confidential material, or client exposure are involved.

A delay is useful only if it serves a purpose. Waiting for an unidentified approver or repeating work lost by a broken form does not improve the result.

Useful friction does something. It protects quality, safety, rights, privacy, accountability, or memory. It creates a deliberate pause before risk. It gives the work an owner. It leaves evidence.

The bad kind should be automated out of existence. The useful kind should be designed on purpose.

AI makes the distinction unavoidable

AI removes friction from generating, summarizing, searching, drafting, editing, coding, and publishing. That is useful. I use it. I want the machine to handle routine work where it can: first drafts, cleanup passes, search, comparison, transcription, formatting, rough code, alternate phrasing, early concept work, and other small tasks that take up much of a day.

The mistake is treating every removed step as progress.

AI makes it easier to publish without understanding, summarize without reading, generate without sourcing, imitate without permission, decide without context, accept confident wrong answers, and skip the human who owns the consequence. A fluent answer can obscure missing reasoning. It can make a deck look client-ready before anyone has checked whether the plan is feasible. It can make a legal-looking paragraph before anyone has checked whether the claim is true. It can make an image look usable before anyone has asked who owns the style, likeness, reference, or source.

Review the output according to how it will be used.

If AI drafts the brief, a human still owns the promise. If AI summarizes the research, a human still checks the source. If AI generates the image, a human still clears the rights. If AI writes the code, a human still tests the behavior. If AI produces the answer, a human still has to decide whether the answer belongs anywhere near the real world.

The value is not in preserving every manual step. The value is in knowing which steps require a person to evaluate the result.

Platforms remove friction too

Platforms remove friction from distribution. That is also useful. A producer can find a director on Instagram. A VFX supervisor can discover an artist on Vimeo. A writer can test an idea on LinkedIn. A small tool can find its first users because a post traveled farther than the homepage ever would have.

That makes platforms useful for finding an audience.

That convenience can also make it easy to become dependent on them. They make it easy to publish instantly somewhere you do not control, build an audience you cannot move, accept default formats, let the feed decide what gets remembered, and trade ownership for convenience.

I wrote the longer version of this in Build on Your Own Land: use platforms for reach, but keep the canonical work, archive, URLs, and audience somewhere you control. That is another version of useful friction. Owning the domain, keeping the markdown file, backing up the media, saving the source, maintaining redirects, and preserving an exportable list all add steps.

They are not glamorous steps. They are not the fun part. They are the reason the work survives when a platform changes rules, search gets worse, an API shuts down, or a company decides your archive is not worth maintaining.

Those extra steps preserve your ability to move the work elsewhere.

Production already knows this

Production people understand useful friction because the job is full of real-world consequences. A shoot day is expensive. A rights problem can prevent a campaign from running. A skipped handoff conversation can cause a week of corrections in post. A beautiful mockup can become a budget problem if nobody asked how it gets made.

Generating a deck does not complete pre-production. The team still needs to agree on what the proposed work requires, clear the references it uses, and identify the approved version.

Production requires reviews of feasibility, safety, budget, and rights, along with records of what was approved.

These steps can be annoying and still be necessary. A call sheet records information the crew needs to coordinate safely. Version history shows what changed. A documented client approval confirms which version the client accepted before the team proceeds.

Resolving a disagreement, revising an inadequate version, and questioning a default all take time. Removing those steps may speed up delivery at the expense of the work.

Cameras like the ARRI Alexa became production standards partly because they make the right decisions easier and the wrong ones harder. They do not win by chasing every possible feature. They win by supporting intentional decisions under real production pressure. That reliability is part of their value.

Why I choose slower formats

When a quick version is easy to produce, people may value evidence that someone took time to consider the details.

I'm not the only one noticing. Bookstores have value even while Amazon dominates retail. Film photography keeps finding people while phone cameras have never been better. Theaters charge premium prices for movies that will eventually stream at home. People pay more to do certain things the hard way. The additional effort can be part of what they enjoy.

A handwritten note in a world of AI-generated messages. A meal cooked from scratch when delivery is fifteen minutes away. A photograph composed on film when you could spray a hundred digital frames. A website you built yourself when a template would have been faster. These choices cost something, and the cost is what makes them register.

The process alone does not establish quality. A website built with AI can still reflect careful decisions. A film photo can still be lazy. A handwritten note can still be manipulative. A long process can still produce mediocre work. Time and effort do not guarantee a good result.

It is only valuable when it makes the work better, clearer, safer, more accountable, or more yours.

The friction test

Before removing a step, ask:

Does this step protect quality, safety, rights, privacy, or accountability?
Does it make someone think before creating risk?
Does it preserve memory or evidence?
Does it clarify who owns the decision?
Does removing it save time without creating hidden cost?
Is this friction serving the work, or only protecting a habit?
What breaks if we remove it?
Who pays when it breaks?

If the step protects nobody, remove it. If it only exists because a tool is bad, fix the tool. If it hides ownership, redesign the process. If it turns one person's fear into everyone else's delay, name the decision owner and move on.

But if the step is where the source gets checked, the client approves, the rights get cleared, the version is saved, the safety issue is caught, the editor pushes back, the producer asks what this costs, or someone finally says "wait, should we?" then be careful.

Removing that step may remove a necessary check.

Keep the steps that improve the result

I expect careful review to become more valuable as generating a plausible first version gets cheaper. That does not justify every delay. Keep a step when you can explain what it protects or improves, and remove it when you cannot.

The useful question is whether the faster process still produces work someone has checked and is willing to take responsibility for.

Build on Your Own Land

Tue, 07 Apr 2026 00:00:00 GMT

I was Foursquare user number 14,488. I signed up on June 2, 2009, and for the next ten years I "checked in" almost everywhere I went. Not for points or mayorships, but for the location history that synced to my calendar. The big moments were there: my wedding in Palm Springs, my son's birth at Sint Lucas Andreas in Amsterdam. But the real value was the small ones. A restaurant I'd forgotten about. A bar that closed years ago. A random Tuesday afternoon in a neighborhood I hadn't been back to since. The record became more useful the longer I kept it.

Then Foursquare killed the calendar feed.

They'd warned it could happen. The feeds page had carried that disclaimer for years. But I'd been logging so consistently, for so long, that the data felt like mine.

The calendar feed still depended on them.

One day, they decided it wasn't worth maintaining. My personal history became a feature they no longer wanted to support.

So I built my own.

Where/When is an iOS app that does one thing: saves your current location as a calendar event with GPS coordinates and a reverse-geocoded place name. All on-device. No account. No server. No data shared with anyone, ever. It's been in the App Store for years now, and the thing I value most about it isn't the design or the features. It's that my location history no longer depends on Foursquare maintaining a feed.

A platform can help people find your work without being responsible for keeping the only copy.

A dozen websites

I have a dozen websites. Not for clients. For me.

A film collection tracker for my film collection, now 3,200+ titles across digital and physical discs. A vinyl collection app that tracks value via Discogs. A comic book tracker. A movie poster database with 70,000 pages. A blog. A portfolio. A meeting cost calculator. A margin calculator. A photography portfolio. A bookshelf. A LEGO instruction browser that freed up so much space in my son's closet. An interactive archive of VFX shot cards from a film I worked on in 2002.

Every one runs on Cloudflare. Every one is backed by git. Every one is something I can move, modify, or shut down without asking anyone's permission. The data lives in databases and repositories I control. The domains point where I tell them to point.

This sounds like overkill. It probably is. But I didn't build them all at once, and I didn't build them to prove a point. Each one started because I wanted a tool that worked the way I wanted it to work, displayed the data the way I wanted to see it, and didn't depend on a company's roadmap, business model, or continued existence.

That is what I mean by your own land: control of your domain, source files, archive, backups, and exportable content. You can use commercial services while keeping enough of the work to move elsewhere when necessary.

Publish on your domain first

The conventional wisdom is that you should go where the audience is. Post on Instagram. Publish on Substack. Put the reel on TikTok. Upload to YouTube. Sell through Shopify. Keep a LinkedIn page alive because some clients still live there. And there's truth in that. Distribution matters. Discovery matters. Conversation matters. A site you control still needs ways for people to find it.

But there is a difference between using a platform and depending on one.

It is easy to neglect your own archive. You publish the important thing on a platform you do not control, then maybe link back to a neglected site. A photographer posts the work to Instagram and leaves the portfolio out of date. A small production company puts every update on LinkedIn and treats its own site like a PDF brochure from 2017. A writer sends the real essay to a newsletter platform, then keeps a thin archive somewhere else if they remember. A filmmaker uploads years of work to a channel and assumes the channel is the archive.

It should be the other way around.

Publish the canonical version on your own domain. Then syndicate outward. Put the clip on the platforms. Post the thread. Send the newsletter. Share the carousel. Join the conversation. Link back to the complete version on your site.

I publish a newsletter on Substack. I'm not naive about the tradeoff. Substack gives me distribution, a subscriber list, a reading experience I didn't have to design, and a place where people already understand the behavior. I also depend on their decisions. If Substack changes its economics, export rules, politics, search visibility, or product direction, I have to deal with it.

The difference is that everything I write also exists in a markdown file in a git repository on my machine. The subscriber list is exportable. The words are mine. If Substack disappears tomorrow, the writing survives. Substack helps distribute it; the repository preserves my copy.

What platforms are for

A site you control and a platform with an existing audience serve different purposes.

Platforms are good for:

distribution
discovery
conversation
testing ideas
lightweight posting
social proof
networking
finding collaborators
sending people back to the canonical version
promotion

That is real value. A producer can find a director on Instagram. A VFX supervisor can discover an artist on Vimeo. A writer can test an idea on LinkedIn. A tiny tool can find its first users through a short post that happens to travel. A production company can remind clients that it exists without launching a whole campaign.

Those are good reasons to use platforms.

But platforms are bad as the only home for:

your archive
your identity
your business
your client-facing proof
your long-term work
your audience relationship

Keep independent copies of material you need to retain.

What breaks

Routine product changes can disrupt an archive.

An account gets suspended. An API shuts down. Search gets worse. A platform pivots. A company gets acquired. A product gets sunset. A feed changes. A paywall moves. Exports become incomplete. Old posts become impossible to find. Media gets recompressed, locked down, or detached from its context. You may be unable to contact your followers outside the platform. Someone else controls the canonical URL, so when the platform changes, the citation breaks with it.

None of this requires a villain.

Twitter becoming X is the obvious example: API access changed, and years of assumptions around search, embeds, verification, and third-party workflows suddenly felt less stable. Reddit's API changes wiped out major third-party clients and reminded everyone that "community" still sits behind a business decision. Myspace is a particularly severe example: years of uploaded music disappeared after a server migration, and independent copies became essential.

Most failures are less dramatic than that. Instagram does not need to delete your work to make it hard to search, sort, cite, or revisit. A newsletter platform does not need to trap you forever to make leaving annoying enough that you postpone it. A streaming catalog does not need to lose a film permanently to make a version vanish from the place everyone assumed it lived. A social page does not need to crash to make a business harder to find, quote, trust, or archive.

A platform’s priorities may differ from your need to preserve the work.

What you should own

If your work has long-term value, you should control the files and services needed to preserve and publish it.

You should own:

your domain
your website
your canonical post and page URLs
your source files
your images, video, and audio masters where possible
your email list or RSS feed
your customer or contact list where appropriate
your backups
your redirects
your analytics and export history
your project archive

You can use a hosting provider and still retain the ability to move. A domain can point somewhere else. A static site can be rebuilt. Markdown can be converted. Images can be re-exported. A mailing list can move providers. A redirect can preserve a link. A backup can become the starting point when a service fails.

Control of those files and addresses gives you practical options when a provider changes its service.

The ownership test

Check what you could recover or move:

If this platform disappeared tomorrow, what would I lose?
Can I export the work?
Can I export the audience or contact list?
Do I control the canonical URL?
Can someone cite this five years from now?
Can I redirect old links?
Do I have the original files?
Is the public version backed up somewhere I control?
Can search engines find it outside the platform?
Does this platform own the relationship, or do I?

Apply those questions to the work and records you need to retain, including material held in services you rarely check.

Start with the material you could not currently recover.

A practical setup

The minimum viable version is not complicated.

Buy a domain. Publish canonical posts and pages on your own site. Keep the source files somewhere backed up. Syndicate to social platforms after publishing, not instead of publishing. Maintain RSS, email capture, or both. Keep a media and archive folder. Back up images and source assets. Use redirects when URLs change. Periodically export platform data. Do not rely on DMs and comments as your only record of important relationships.

That is most of it.

You can make it fancier if the work justifies it. A static site in git. A private archive in cloud storage. A local backup. A searchable notes system. A small database. A media library with real filenames instead of whatever the platform gave you. A habit of saving the original file before posting the compressed version. A quarterly reminder to export data from the places where the export button still exists.

For producers, creative directors, small production companies, freelancers, artists, writers, and archive builders, this is not abstract. Your archive shows prospective clients what you have done. Your future opportunities often come from someone finding, sharing, remembering, or citing something you made years ago.

Work stored only in a platform’s feed may be difficult to find or retrieve later.

Keeping control of AI workflows

The next version of this problem is already here, and it is bigger than social platforms.

Most people building with AI are doing it through cloud APIs. OpenAI, Anthropic, Google, whoever has the best model this month. You send your data up, you get your answers back, and your workflow depends on a company's pricing, rate limits, moderation rules, retention policies, uptime, and continued willingness to serve you.

I use cloud AI because the models are useful. I still need access to my files if I change providers.

If AI becomes part of how you search a production archive, summarize a meeting, organize a media library, compare treatments, build a lookbook, query old notes, or remember what happened on a job three years ago, then the archive matters even more. The model is only as useful as the material it can reach. If the material is scattered across dead links, closed accounts, platform inboxes, compressed uploads, and half-remembered threads, the model cannot reliably retrieve the records you need.

Local models are already good enough for useful private work: search, summarization, classification, transcription cleanup, light coding, file organization, and asking questions of your own notes. They do not replace every cloud model. They do not need to. The point is that more of the workflow can live closer to the files, closer to the archive, and closer to your control.

The same principle applies: use the cloud for what it is good at, but keep your records in a form you can use with another service.

Your archive is infrastructure

Production records are easy to neglect until someone needs a specific file: an approved bid, a location photograph, a client note, or a final master.

The same is true for public work.

Your website can preserve a record of your work. Your domain can remain the same when you change tools or providers, and stable URLs let other people cite and revisit individual projects.

That matters for a freelancer trying to show a client what they have done. It matters for a small studio whose best case study is five years old but still relevant. It matters for an artist whose old experiments explain the new work. It matters for a writer whose ideas compound across posts. It matters for anyone building tools, collections, libraries, projects, or proof over time.

Keeping an accessible archive takes deliberate maintenance.

Keep the option to move

I built these sites because I wanted tools I could modify and records I could keep. Each reduces my dependence on a service whose priorities may change.

You do not need a dozen websites or a local language model. Start with the material you would be most upset to lose. Save the originals, back them up, and make sure you can publish or use them elsewhere. Then use platforms to help people find the work.

Pace Regulators

Sat, 28 Feb 2026 00:00:00 GMT

I stayed up way too late last night. Claude got added to Xcode, and before I knew it I was three hours deep into refactoring four old app projects I’d almost abandoned. I was returning to things I’d walked away from months ago because the coding problems that stopped me had become easier to solve. That’s the part about this moment that’s hard to explain. It’s not just that AI makes new things possible. It’s that it makes everything feel urgent again.

Something shifted in the last couple of months. Coding agents became much more useful to me. Tasks that would have taken a weekend now take 30 minutes. You describe what you want in plain English, an agent goes off and builds it, runs into problems, solves them, and comes back with a working thing. That has changed how much I can attempt in an evening.

I’m finding that pace hard to sustain. When every idea is 30 minutes from existing, you never run out of reasons to stay up. I’ve kept this pace for weeks now, and I’m restless even when I stop working. It’s hard to sit still or sleep when I keep thinking of another thing to try. My Apple Watch started flagging some health concerns a few months back, which was its own kind of wake-up call. I've since lost weight and closed my exercise ring every day for the past three months, something I should have prioritized a long time ago. I’m watching what I eat. I’m getting healthier, but I’m still having trouble switching off.

Here’s what I’ve noticed, though. Over the past year, I’ve bought more vinyl records, 4K Blu-rays, physical books, and comics than I have in the past decade combined. The pace has been accelerating alongside everything else. At first I didn’t think much of it. Then I realized what I was doing.

I was choosing activities that would make me slow down.

A vinyl record takes about 20 minutes a side. You put it on, you sit with it, you don’t skip tracks. A 4K disc means committing to a movie from opening titles to end credits. A book requires hours. These formats encourage me to stay with one thing. I could skip a track or stop a film, but I’m less inclined to do that than I am to switch between tasks on a screen.

The irony isn’t lost on me. I’ve used AI to build custom tools that track all of these analogue collections. Vinyl, comics, films, books, each with its own little web app, each connected to APIs and LLMs so I can interact with my shelves in new ways. The apps make it easier to use the collections. I enjoy building them, and I also need time away from building anything.

I’m not going to stop using these tools. They’re too powerful, too useful, too genuinely exciting. Last night’s Xcode session was thrilling in a way that programming hasn’t felt in years. But I need a way to stop, even when I’m excited. Otherwise I keep starting things and give myself very little time to enjoy them.

This morning I made an espresso, put on a record, and sat in the garage for 20 minutes before opening my laptop. For those 20 minutes, I just listened.

That felt like the most important thing I did all day.

The VFX Industry: Part 3

Sat, 31 Jan 2026 00:00:00 GMT

This is part of a three-part series on the VFX industry: how the work changed, why the business became harder, and how those pressures affected artists.

Series: Part 1 · Part 2 · Part 3

This final part is about my experience with crewing, scheduling, impossible delivery pressure, and the moment I realized I could not keep standing between artists and decisions I did not believe in. When creative ambition and late changes exceed the time available, artists are often expected to make up the difference through longer hours.

Crewing and scheduling

By 2007, I'd moved from recruiting into crewing and scheduling on the artist management team. In recruiting, I'd been the first face people saw coming in. Now I had to explain staffing and scheduling decisions that affected them.

Spider-Man 3 dominated the schedule. Cutting-edge VFX against a hard delivery date. Things were being invented on that show that turned out to require months of render time. Time that didn't exist.

Working hours increased.

8-hour days became 10. Then 12. Five days a week became six. Then seven. Every top mind from every show got pulled in to find solutions.

Meanwhile, fully CG shows in the same building were running normal hours. Two realities under one roof.

Artists cried in my office. Their spouses were going to leave them if they didn't stop working late. Animators showed me photos of their kids like I was the one who decided if they stayed or went.

I wasn't. But I was the face they saw.

The moment

One morning I sat in a meeting about headcount. We were figuring out who to keep and who to let go as shows wound down.

I asked about a veteran animator. I'd grown up watching movies he worked on. He was between projects, but we had something coming up that could use him.

I got approval to keep him in "wait mode." Take your PTO now, we'll bring you back when the next show ramps up. It wasn't ideal, but it was a path forward.

I found him and delivered the news. I remember his face. Relief.

A few hours later, I heard his department head had let him go.

I found the guy and asked what happened. "You told me this morning we'd keep him around. I told him he was safe."

He agreed that's what he said that morning.

Then he added: "Things change."

I left in 2007.

Some of the team I'd worked with over the years stopped by to say goodbye. They asked where I was going.

"I'm going to be a Web Producer."

They looked confused. "Like, Spider-Man?"

Years later, I ran into that animator. I told him what happened that day was the reason I left. I told him I was sorry.

He looked at me with a somewhat confused expression. Like he couldn't understand why I'd been holding onto it for so long.

"It's okay," he said. "That's the industry."

What I learned

Imageworks was one of the best jobs I've ever had. I loved the people. I still do.

But the industry creates impossible situations. Facilities pass studio pressure down to artists. And somewhere in those five years, I learned something about myself.

I work hard. I can handle pressure. But I couldn't stay in an industry that, as much as I still love it, put me in a position that compromised my integrity.

The VFX Industry series

Photography Workflow 2026

Sat, 17 Jan 2026 00:00:00 GMT

What changed this year

I started these posts in 2018 to document changes in apps, backups, exports, and culling. This year I changed both my editor and my backup service.

The difference this year is that the editor is no longer the whole question. The editor matters, but the more important question now is whether the archive stays portable, backed up, and publishable without being trapped inside one company's app, cloud library, or subscription plan.

Photomator Was Close

I still love Photomator. The desktop/mobile workflow is genuinely good. Moving between the Mac, iPad, and Apple Photos library with so little friction solved a real problem. The auto-masking is fast: it makes local adjustments quick.

I also still like the idea of Photomator, especially after Apple acquired Pixelmator. The first photo editor I used seriously was Aperture, so I still hope Apple will make something that suits me as well. That has not happened yet.

The problem was not that Photomator became bad. It did not. It is a good app. The problem was that the results stopped feeling strong enough to make it the center of the whole workflow.

The Capture One Detour

After that, I tried Capture One. I signed up for the trial, liked a lot of what I saw, then realized it was not the right fit about a week into a yearly subscription I had already paid for.

Some of this is taste, some of it is irritation, and some of it is the ongoing daydream that if I buy the Hasselblad X2D II 100C, my photography will somehow improve. Buying a camera will not settle which editor I prefer.

Capture One made one useful thing clear: I had been over-weighting file syncing as a requirement. If an editor did not have a way to sync files, I dismissed it. Once that rule loosened, other options became possible.

Reprocessing the Catalog, Again

There is a ridiculous pleasure in re-processing the catalog of selects every time the software changes.

It is also a pain in the ass.

The 2025 Photomator move already forced this realization. Losing old Lightroom edits sounded awful until I treated it as a chance to revisit the images with my current eye. Taste changes. What I liked ten years ago is not always what I like now.

That part is worthwhile. Re-editing old work is a way to see your taste changing in public. The practical problem is different: when edits live inside app-specific systems, switching costs become archive costs. I do not want the archive to depend on my memory of which app contained the latest edit.

Why DxO PhotoLab 9 Stuck

With the syncing requirement relaxed, I tried DxO PhotoLab 9.

Fast-forward to now: I am using it exclusively for photo processing.

The AI masking is not as fast as Photomator's, but it works. I need it to help me dodge and burn without making the adjustment difficult.

The part that stuck is the RAW processing. DxO's camera and lens profiles, the DxO Modules, give me results I like. I spend less effort correcting noise, lens distortion, color, and detail.

The multi-export workflow helps too. One processed file can move out in the sizes and formats I need without repeated manual exports. DxO fits because it makes the working file look good and lets me get finished images out cleanly.

Keeping edits beside the RAW files

The sidecar files matter as much as the editing tools.

DxO stores edits in .dop sidecar files beside the original RAW files. They are still DxO-specific. I am not pretending some other editor can open a .dop file and magically recreate the edit.

The point is that the instructions live next to the source file.

That changes how much I trust the archive. With Photomator, edits lived in a specific iCloud folder, and in my experience that was not always as reliable as I wanted. The archive felt too dependent on storage behavior I could not easily inspect.

With DxO, the RAW and the edit sidecar travel together. If I move the folder, the edit instructions move with it. If I back up the RAW, I back up the .dop file beside it. If I restore a folder later, I am restoring both the original file and its edit instructions.

Keeping the files together makes the archive easier to understand and reduces the chance that I’ll have to recreate a lost edit.

Cold Storage Got Simpler

The backup side changed too.

I moved the RAW archive from Google Cloud to S3 Glacier Deep Archive. Right now, it is costing a little over $5/month for almost 6TB of backups.

That is absurdly cheap, with the obvious caveat that Glacier Deep Archive is not a fast working drive. I am not using it as an active photo server. It is cold storage. The point is that if the local files are lost, the files that should survive have a durable offsite copy.

The Archive Has a Public Home Again

I used Claude Code to build a custom version of Sam Becker's EXIF Photo Blog and now have it running at photo.banast.as.

Claude Code is not the point here. It was useful, but the important part is that I have a public archive I control again. The selected images do not only live inside Instagram, Apple Photos, Lightroom, DxO, or an old archive system I stopped maintaining.

A public photo archive gives the finished work a stable address outside an editor or social feed. It connects back to why I keep building these little personal systems: I like having my own infrastructure for the things I care about.

Current Snapshot

So this is the 2026 version.

Photomator was close. Capture One was an expensive detour. DxO PhotoLab 9 is the editor that stuck. The RAW files now stay with their .dop sidecars. The cold archive sits in S3 Glacier Deep Archive. The selected images have a public home at photo.banast.as.

This records what works for me now, including the compromises. My preferences will probably change again.

The useful shift is that the workflow is finally less about chasing the perfect app and more about keeping the archive understandable and recoverable: RAW files, sidecars, cold storage, and a place to publish.

(Previously: Photo Workflow 2025)

How Around the Fur Found Its Cover

Sat, 10 Jan 2026 00:00:00 GMT

I was a huge Deftones fan growing up. Around the Fur was in heavy rotation. But even before you get to the music, the cover gets your attention.

It is not a clean rock pose or some grand concept. It is a flash-lit, off-kilter party image: a woman in a jacuzzi, someone else's feet in the frame, an expression you can't quite read, and a whole social situation happening just outside the edges. It always felt like you had walked in halfway through a story.

This week I stumbled on a video from Jenkem that tracks down the photographer, the art director, and Lisa Hughes, the woman on the cover. The details below come from the Jenkem video unless otherwise noted.

I enjoyed finding out how an unplanned photo became such a familiar cover.

The Image

The photo was shot by Rick Kosick, who was shooting for Big Brother magazine at the time. If that name sounds familiar, it's because he'd later become one of the main cameramen on Jackass.

Rick had become friends with the Deftones, and they invited him to Seattle during the recording of Around the Fur. No assignment to shoot a cover. He was there to hang out and take photos.

"I had no idea that this would make it to the album cover at all," Rick said in the video. "Maybe that's why it worked, you know, because there was no intention."

The band was in their party phase. They went back to the condo they'd rented during recording, and there was a woman hanging out in the jacuzzi. Rick went up and took two photos. That's it. Walked away.

A few weeks later, he got a call from the label asking him to come see something.

He wasn't even looking through the lens when he shot it. Just hung over the jacuzzi using his bulb technique.

"It's lightning in a bottle," he said. "You can only get one."

Why It Stuck

Kevin Reagan was the art director at Maverick Records, the label Madonna ran. He'd worked on albums for Prodigy, Sonic Youth, Tupac. When the Deftones project landed on his desk, he wasn't super familiar with the band.

Rick’s photo immediately interested him.

"There was nothing close," Kevin said. "I was like, hm, this is an eye opener."

The image was a long vertical, which isn't typical for album covers. But Kevin knew there was a story in it. And then he noticed the feet at the bottom of the frame (they were Rick's feet), adding another layer to the composition.

"If you take that out, it becomes good," Kevin explained. "But it's definitely not the same dynamic of like, who is that? It starts to tell a story and god knows who else is in the picture."

Then came the decision that I think made the cover what it is. The photo had imperfections. A blemish. A tooth catching the light at an odd angle. Kevin had to decide how much to retouch.

"If this was Madonna, definitely would have cleaned up a lot of this," he said. "But I decided to leave it on. I'm going for it. I'm going to have less retouching."

Leaving those details intact made the image more unsettling, as Kevin describes it.

"It's like, what's the fuck going on there?"

Rick has shot thousands of photos, he helped create Jackass, but the Deftones cover? That's the only album cover he's ever done.

"I've only done one. But hey, I guess that's all I need to do."

What Album Covers Used To Do

This is the part I miss about album covers as cultural objects. They used to sit around in bedrooms, CD binders, record-store bins, dorm rooms, cars. You had time with them.

You could stare at a cover without knowing much. No instant explainer, no comments, no tagged photographer, no explanation supplied alongside it. Just an image, repeated until it became part of the music.

Around the Fur had that quality for me. The cover was not asking you to solve it exactly. It showed a night I knew nothing about. That ambiguity is part of why it lasted.

Why Provenance Matters

The video also gives Lisa a chance to tell her part of the story.

Twenty-eight years later, Lisa still lives in Seattle. She met up at a local skate park, vinyl in hand. She's been signing copies for her daughter's friends lately.

Back in '97, she was a young woman from Auburn who knew a lot of people around the area. She'd go to after-parties after the bars closed because she never wanted to go home.

She wasn't tracking who the Deftones were. She'd just run into them at parties, and this one happened to be at the pool at their apartment.

"I was kind of oblivious of who they were," she said. "To me, it was just a bunch of people there and we're just having fun."

Her drink of choice at the time: vodka and peach schnapps. She and her friend called it "silk panties." It's in the glass at the bottom-left of the frame.

When the album came out, her life didn't really change. Her friends knew it was her, but she wasn't announcing it. She got some cool opportunities. The band would invite her to shows. But she kept it quiet.

About ten years ago, someone asked if she ever looked herself up online. She hadn't. When she did, she found articles calling her a groupie.

So she started a page just to set the record straight. "So people know that I'm Lisa. I'm just this awesome chick from Auburn and I like to have fun. And there's no groupie action going on here. Just me having a kick-ass time."

People have pointed out the blemish on her face over the years. She doesn't care.

"Who gives a shit? I'm a human being. I'm not a model."

The video ends with Lisa taking the creator to the actual spot where the photo was taken. She hadn't been back in years, but her memory was sharp.

After a bit of searching, they find it.

They return to the spot where Rick took the photograph 28 years earlier.

"What's really happening is someone having a good time, living their best life in their 20s," Lisa said.

That's the whole story. A photographer who wasn't trying to shoot a cover. An art director who chose the photograph and left the imperfections in. And a woman who was just out having fun with her friends.

The photograph was unplanned. Making it the cover—and deciding how little to change—required a separate set of choices.

James Cameron Sold The Terminator for $1

Sat, 27 Dec 2025 00:00:00 GMT

James Cameron sold the rights to The Terminator for one dollar to secure his role as director. The payment is the memorable part of the story, but the arrangement with producer Gale Anne Hurd is what interests me.

The legal and economic details can blur in retelling. His later success is not a reason for other artists to give away rights; the useful question is what the agreement allowed each person to do.

The dollar was not the point

Cameron was early in his feature directing career. He had a messy prior directing credit behind him, had no real power in the business, and was trying to make the jump from capable genre craftsman to actual feature director. He had an idea, a script, visual-effects experience, and a clear sense of how to make the movie cheaply enough to be plausible.

What he did not have was the thing that mattered: permission to direct it.

In his recent Jay Shetty interview, Cameron describes making a rights deal with Gale Anne Hurd, who was also coming up through the low-budget Roger Corman world. He sold her the rights for a dollar. In return, the deal was tied to a promise that the movie would not proceed without him directing, and that he would not make it without her producing.

Hurd bought the rights and produced the film. She is credited by AFI as producer and co-writer. AFI also records The Terminator as a Hemdale production and an Orion release, which identifies other organizations essential to making and releasing the film. Financing, production, distribution, casting, crew, locations, effects, and other practical arrangements had to be in place.

Securing the directing role

Cameron accepted a nominal payment for the rights in exchange for a role in a film that still needed financing, a cast, and distribution. The opportunity eventually changed his career, but that outcome was not guaranteed when he and Hurd made the agreement.

Without that control, The Terminator could have become a different movie. It could have been reassigned, softened, overexplained, made expensive in the wrong places, or buried in development. A strong script can lose its edge one reasonable note at a time. Changes intended to reduce risk can also raise the budget or weaken the original concept.

Control is not always ego. Sometimes a specific approval right protects a necessary creative decision.

Hurd’s producing role included resolving the financial, logistical, and creative problems that could prevent the intended film from reaching an audience.

Hurd has talked about studio pressure on the original ending. In a 2018 Screamfest Q&A reported by Slashfilm, she recalled notes that would have ended the film earlier, before the endoskeleton returned. Her point was not that she and Cameron were infallible. It was that they had to know which notes would make the film better and which would break it.

Their authority mattered because it allowed them to defend a specific part of the film.

Ideas are not movies

The Terminator is easy to describe now because everyone knows what it became: killer robot from the future, Sarah Connor, tech-noir, stop-motion endoskeleton, Austrian bodybuilder as death machine, time travel used to set up an affordable present-day chase movie.

After the fact, it sounds inevitable.

Before the fact, it was a risky package. A script still needed financing. Financing needed confidence. Casting had to work. Locations had to be chosen. The effects had to be achievable. The tone had to remain consistent. The distributor had to believe there was a movie in it. The director had to make a low-budget production feel like a nightmare instead of a compromise.

Cameron understood that because he did not write The Terminator as a fantasy detached from production. He wrote toward constraints: present-day streets, available environments, limited but meaningful visual effects, a future war suggested more than shown, and a monster whose design could make it threatening with limited screen time.

Those choices connected the script to a feasible production.

Directing gave Cameron authority over how those choices were executed. The finished film then demonstrated his ability to direct, which mattered in subsequent negotiations.

Assessing the concession

A famous director’s success is a poor reason to accept a symbolic payment for your own work. A concession needs to secure a benefit you can identify and enforce. Giving away ownership does not necessarily buy control, distribution, or another opportunity.

Cameron secured the chance to direct, but the later rights history involved lasting costs as well as benefits. Consider what the agreement would mean if the project became much more valuable.

Ownership and authority

Copyright, directing attachment, producing role, final approval, and distribution are different interests. An agreement can exchange one for another. The question is which rights each person needs to make the work and benefit if it succeeds.

For Cameron, the immediate priority was directing. For Hurd, the rights and producing role gave her the basis for organizing and protecting the production. Financiers and distributors needed a film they could release.

Why it matters now

AI makes polished pitch material easier to generate. It does not establish that the production is feasible or that the proposed agreement protects the people making it.

The Cameron, Tippett, and Toy Story stories all concern abilities that a polished pitch cannot demonstrate by itself: making the work, judging its quality, and obtaining the authority needed to finish it.

What are you really trading?

Before accepting a concession, ask:

What am I giving up?
What do I get that I could not get otherwise?
Is the concession buying access, control, distribution, proof, speed, financing, or credibility?
Is the thing I am keeping actually the thing that matters?
What happens if this works?
What happens if it fails?
Who owns the upside?
Who controls the next decision?
Can I live with this deal if the project becomes much more valuable later?

Bad deals often feel tolerable when the project is small. The regret arrives when the project succeeds. That is why you have to negotiate with the possibility of success in mind, even when getting started is difficult.

This does not mean squeezing every dollar out of every deal. Sometimes the smart move is taking less money to get the right partner, the right distribution, the right credit, the right timeline, the right degree of control, or the first piece of proof that lets the next deal happen.

You should be able to explain what you are giving up, what you receive in exchange, and why that benefit is worth the cost.

Cameron secured the directing role; Hurd secured the rights and producing role. Their agreement helped them make the film together. That specific exchange is more useful to understand than the dollar amount alone.

Watch the full Jay Shetty interview with James Cameron here.

The Ghost of Bourdain's Lists

Sun, 14 Dec 2025 00:00:00 GMT

Photo: Vladimir Weinstein

Someone recovered a chunk of Anthony Bourdain's old li.st posts, including his recommendations, complaints, memories, and fascination with antique tools for drilling holes in skulls.

Li.st, if you don't remember it, was a 2015-ish app from that brief window when tech bros believed every human activity deserved its own social platform. BJ Novak was involved somehow. The servers went quiet.

Sandy Uraz went looking through public crawl archives and put together what survived: posts that would otherwise be difficult to find after the app closed.

Bourdain, being Bourdain, did not make lists of productivity hacks or "10 Books Every Entrepreneur Should Read." He made lists about trepanning instruments, dead New York bars, spy novels, sandwiches, Persols, and the cheese crust on onion soup gratinée.

What He Left Behind

There is something almost uncomfortably intimate about reading these now. Not because they reveal a secret Bourdain. Anyone who watched Parts Unknown knew the man's obsessions. They matter because these brief entries let him record an interest without developing it into an essay.

His Objects of Desire list includes things he wants without trying to explain the appeal:

"Vintage Persol sunglasses... I wear them a lot. I collect them when I can."

"19th century trepanning instruments... I don't know what explains my fascination with these devices, designed to drill drain-sized holes into the skull often for purposes of relieving 'pressure' or 'bad humours'. But I can't get enough of them."

There is no pitch here. No essay trying to justify the want. The same man who could write beautifully about the ethics of eating also wanted antique skull-drilling tools and could not explain why. I like that he included an interest he could not explain.

Television and spy novels

His desert island TV picks are exactly what you would expect from someone who read le Carré the way other people read beach novels:

The Wire
Tinker Tailor Soldier Spy and Smiley's People
Edge of Darkness

All three concern distrust and failing institutions.

His spy novel recommendations follow the same logic. Not Clancy. Not Fleming. The quiet stuff:

"If the main character carries a gun, I'm already losing interest. Spy novels should be about betrayal."

That's the whole philosophy in two sentences. He liked stories where the damage happened in conference rooms, safehouses, and the silences between old colleagues who had sold each other out.

The Food Lists, Obviously

His Steaming Hot Porn list is just food. Which is the correct use of the word porn:

Bun Bo Hue
Kuching Laksa
Jamon
Oily Little Fish
Meat on a Stick

The I'm Hungry and Would Be Very Happy to Eat Any of This Right Now list is more revealing:

"A big, greasy double cheeseburger. No lettuce. No tomato. Potato bun."

"A street fair sausage and pepper hero would be nice. Though shitting like a mink is an inevitable and near immediate outcome."

"Some uni. Fuck it. I'll smear it on an English muffin at this point."

These are immediate cravings rather than carefully considered restaurant recommendations.

The Hotels

"CHATEAU MARMONT (LA): if I have to die in a hotel room, let it be here."

I'll leave that one alone.

His hotel list reads like a Graham Greene itinerary: Le Continental in Saigon, The Metropole in Hanoi, Hotel Olofsson in Port au Prince, "sagging, creaky and leaky but awesome." He did not want comfort exactly. He liked places with a distinctive atmosphere and history.

The Dead Bars

There is a list of Great Dead Bars of New York that functions as a kind of elegy:

"SIBERIA in any of its iterations. The one on the subway being the best."

"BILLY'S TOPLESS (later, Billy's Stopless) an atmospheric, working class place, perfect for late afternoon drinking where nobody hustled you for money and everybody knew everybody. Great all-hair metal jukebox. Naked breasts were not really the point."

One Hawaii Kai line includes the sort of dated Bourdain language that belongs to the archive but does not need help from me:

"THE BAR AT HAWAII KAI. tucked away in a giant tiki themed nightclub in Times Square with a [dated description of the doorman] and a floor show. Best place to drop acid EVER."

These places are gone. Most of them have been gone for decades. He knew it when he wrote the list. The point was that they were gone: places close, and a description can preserve something of what people enjoyed about them.

The Observations

There is a list called Observations From a Beach that I will quote sparingly:

"In which my Greek idyll is suddenly invaded by professional nudists."

"T-shirt and no pants. Leading one to the obvious question: why bother?"

Some of the rest has aged exactly as you would expect. That is also part of the record. Not every observation is appealing. The archive includes his less considered remarks alongside the recommendations.

Preserving the posts

I don't know that it does, in the grand sense. People have written a lot about Bourdain since June 2018. Most of it is either hagiographic or uncomfortably speculative. I'm not interested in adding to either pile.

What interests me is simpler: somewhere between the shows and the books and the public persona, there was a guy making low-stakes lists on a forgotten app. Lists about jiu-jitsu gis and R. Crumb collections and Captain Crunch. Lists nobody was going to study.

I’m glad someone preserved them. Small posts on a short-lived app can disappear easily, even when they tell us something about a writer whose larger work remains available.

These lists show what Bourdain noticed and enjoyed when he did not have to turn it into a book or an episode. That is enough reason to read them.

The preserved li.st archive lives here. Go read the whole thing.

Before AI, It Was Just Graphics

Thu, 04 Dec 2025 00:00:00 GMT

I watched Jensen Huang sit down with Joe Rogan, and the easy version of the story is the one everybody already knows: NVIDIA made graphics cards, AI got huge, and suddenly the graphics card company became the obvious hardware name in the room.

That account leaves out the software and developer support that made the hardware useful beyond graphics.

Gaming provided a mass market for parallel computation and real-time image generation. NVIDIA also developed the tools that let programmers use that hardware for other tasks.

The supporting technology often attracts little attention outside its users. It looks like drivers. APIs. Compilers. Developer relations. Memory bandwidth. Thermal envelopes. Chip packaging. Render pipelines. Software libraries. Technical decisions that later applications may depend on.

AI did not make NVIDIA important out of nowhere. AI benefited from NVIDIA’s existing hardware and software work.

It was never just games

For a long time, "graphics card" sounded like a gamer purchase. It was the thing you bought so Quake, Doom, Unreal, flight simulators, and later every other visually ambitious thing could run better.

That made it easy to underestimate.

But graphics has never really been trivial. Real-time graphics is a demanding computing problem. You are trying to generate convincing reality many times per second, under hard latency constraints, on a machine somebody can fit under a desk. The system must calculate each frame within a strict time limit. Lighting, geometry, textures, shadows, simulation, physics, animation, and camera movement all have to resolve fast enough that the illusion does not fall apart.

Games created a consumer market for that computing capability.

In the Rogan conversation, Huang keeps returning to this idea: computer graphics was an early application for a different style of computation. NVIDIA's CUDA guide describes the GPU as starting out as hardware for parallel operations in real-time 3D rendering, then becoming more programmable over time. CPUs are built around fast serial work. GPUs are built around doing huge numbers of related operations in parallel.

Games gave consumers a reason to buy faster graphics hardware.

The long bet was that this kind of computation would matter far beyond the screen.

Hardware needed software

Supporting systems become more valuable as more people depend on them.

NVIDIA built a business around both hardware and the software needed to use it. The company was founded in 1993 around 3D graphics for gaming and multimedia. The visible product was the card. The business also depended on hardware, software, drivers, developer tools, game relationships, and eventually a programming model that let people treat the GPU as something more general than a graphics device.

The hardware matters, but the hardware is only part of its competitive advantage. A chip without a software stack is a component. A chip with libraries, tooling, developer habits, training material, examples, installed base, and years of weird edge cases already solved becomes infrastructure.

This is why CUDA matters. NVIDIA introduced CUDA in the mid-2000s, giving developers a way to use GPU throughput outside graphics APIs. The same basic hardware originally justified by games and graphics could now be used for scientific simulations, image classification, databases, analytics, machine learning, and eventually generative AI.

That did not look obvious at first. Near the end of the Rogan conversation, Huang says NVIDIA added CUDA before customers were asking for it, increasing cost before the market knew how to value it. That is the uncomfortable part of a long bet: the costs arrive before the additional revenue.

This is where I think the NVIDIA story is more useful when we focus on the investment rather than the founder’s personality. The point is not "one genius saw the future." The point is that a company spent years building a technical layer before the mainstream use case arrived.

Timing, not destiny

The clean narrative says NVIDIA built the GPU, deep learning arrived, and the plan worked.

The real narrative is messier.

Neural networks were not new. AI had already spent decades cycling between promise and disappointment. Then researchers made significant progress in computer vision. In the 2012 AlexNet paper, Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton describe training a deep convolutional network on two NVIDIA GTX 580 GPUs, a result that became one of the canonical moments in modern deep learning.

The tools built to make games faster turned out to be useful for teaching machines to see. The work done for rendering, pixels, shaders, and parallel math became relevant to neural networks because neural networks also thrive on large amounts of parallelizable computation.

That is timing. But it is not pure luck.

Luck is AlexNet showing up when it did. Luck is researchers finding the hardware. Luck is the world finally having enough data, compute, and technique in the same place.

The non-luck part is that NVIDIA had already spent years making the hardware programmable, available, and supported. Researchers could use that existing support when they found a new application for the hardware.

Why production people should care

From my perspective, if you come from VFX, production, post, photography, games, virtual production, or creative technology, this story should feel familiar.

You already know that supporting systems often determine what a team can deliver.

Production depends on systems for rendering, storage, color, and delivery. A failed overnight render or missing plate can disrupt the next day’s work.

Problems with storage, rendering, or delivery can prevent a creative plan from being completed.

Modern production is already a stack of specialized compute. VFX depends on rendering, simulation, compositing, color pipelines, asset management, and increasingly real-time engines. Virtual production workflows moved game-engine technology into previs, pitchvis, techvis, postvis, live compositing, in-camera VFX, virtual scouting, and earlier design review. AI is now sliding into the same stack: search the archive, generate references, build temp boards, rough out shots, denoise plates, assist roto, synthesize voices, summarize footage, organize notes, test looks, and speed up routine coordination and file handling.

Across these uses, more production tasks can be represented and manipulated in software.

The team still needs to judge which results are useful.

When the tool makes rough output cheap, the valuable person is not the one who can generate the most options. The valuable person is the one who understands which options can be made and will serve the finished production.

VFX people should understand NVIDIA because they have been living inside this transition longer than most people. They know that a faster render does not automatically make a better shot. A real-time preview does not eliminate judgment. A beautiful synthetic frame can still be useless if it cannot be approved, tracked, licensed, matched, rendered, versioned, or delivered.

The tool is never the whole system.

The platform was the product

One of the least interesting ways to talk about NVIDIA is to obsess over any single chip as if the story is just a comparison of benchmarks.

The better question is what becomes possible once enough people build around the same toolchain. That is what happened with games. Developers optimized for the hardware. Engines absorbed the assumptions. Players bought the cards. The cards justified better games. Better games justified better cards. A market became a loop. Huang describes this as turning a technology business into a platform business. That is the important sentence.

The same pattern shows up again in AI. Researchers write against the libraries. Frameworks support the hardware. Cloud providers buy the systems. Startups assume the stack. Engineers learn the tools. Models are designed around the available compute.

Shared tools affect what researchers and developers can build.

This is why shared technical dependencies influence production and business decisions. The API influences what gets built. The engine influences how a shot can be planned. The render budget limits its complexity. The available compute influences the model. The energy constraint influences the geography.

Physical requirements

The deeper NVIDIA goes into AI, the less the story is about a chip in isolation.

It becomes about fabs, packaging, memory, interconnects, data centers, cooling, power, supply chains, and export controls. It also becomes about customers trying to turn investment into working systems before model requirements change.

AI at scale is industrial infrastructure. It needs land, power, water, network capacity, specialized labor, long-range planning, and giant purchasing commitments. The International Energy Agency estimates data centers used about 415 TWh of electricity in 2024 and projects that demand could roughly double by 2030, with AI accelerating deployment of high-performance servers. The broader numbers back that up. The limiting factor is not just whether somebody can imagine a bigger model. It is whether somebody can power, cool, connect, finance, and operate the machine that trains or serves it.

The creative question is always attached to the physical one. Can we make the shot? Can we render it by Friday? Can we store the plates? Can we move the data? Can the stage handle it? Can the crew turn it around? Can the budget absorb the change?

What to watch next

I am watching the tools that look too specialized for mainstream people to care about: real-time engines used earlier in design, local AI tools for private footage search, simulation systems for synthetic training data, render pipelines that combine previs and review, and agents that handle routine coordination among files, calendars, notes, transcripts, shot lists, asset databases, and delivery specs.

Not all of these tools will prove useful. I’m interested in those that solve a specific problem reliably enough for teams to depend on them.

That is what makes the graphics-to-AI history relevant to production. A tool can become useful beyond its original market when other people can program it, support it, and incorporate it into their work.

What Jobs Learned Making Toy Story

Sat, 22 Nov 2025 00:00:00 GMT

I watched a 1996 interview with Steve Jobs about the early days of Pixar, recorded not long after Toy Story came out and before the company’s later successes shaped accounts of its early years.

Toy Story demonstrated that computer animation could sustain a feature-length story people cared about. Pixar needed more than the ability to generate impressive images: its teams had to develop the characters, revise the story, and complete and release the film.

Pixar before Toy Story

Pixar came out of Lucasfilm's computer division. Lucasfilm's own history notes that the division was sold to Jobs in 1986 and became Pixar, Inc. Pixar's official timeline says the new company had about 40 people at the time.

Jobs had left Apple. Ed Catmull had a long-running dream of making the first computer-animated feature film. John Lasseter was making shorts that showed what character animation could feel like in a new medium. The company sold hardware. It built software. It did commercials. It made shorts. It built tools and then tried to survive long enough for the tools to become a business.

For years before Toy Story, Pixar combined research, hardware and software sales, and commercial animation.

In the interview, Jobs is clear about his own role. He was not the filmmaker. He did not direct the movie. He did not solve the story. He describes himself as someone trying to create the conditions where the people who actually knew the work could do it well.

Jobs was the owner, backer, operator, negotiator, and financial supporter during years when Pixar’s eventual success was uncertain. The creative success belonged to the filmmakers, artists, animators, technologists, production people, and the process that let them question and revise the work.

The demo was not the movie

Computer animation had already produced impressive images before Toy Story. Pixar's shorts mattered because they showed capability, personality, and promise. Luxo Jr. made a desk lamp feel alive. Tin Toy helped prove that computer animation could carry character, not just chrome balls and flying logos.

A feature has to hold attention beyond the novelty of its images. It also needs financing, casting, story development, editorial, rendering, music, marketing, and distribution. Pixar’s technical demonstrations could not establish that the company could complete all of that work.

The audience had to care about the story

The most useful line in the interview is Jobs repeating what he says John Lasseter taught him: no amount of technology can turn a bad story into a good story.

Pixar needed the machines. It needed RenderMan. It needed the animation tools. It needed the scientists, engineers, technical directors, modelers, lighters, and pipeline people. The film could not exist without the stack.

Jobs contrasts the enduring audience for Snow White and the Seven Dwarfs with the obsolescence of technology products.

Nobody rewatches Toy Story because a frame took hours to render. Nobody cares which workstation generated the shot. Nobody takes a kid through the movie explaining why the texture mapping was significant in 1995.

The same interview has an instructive detail about rendering time. Jobs says Luxo Jr. took about three hours per frame to render in 1986. A decade later, computers were dramatically faster, but Toy Story still took about three hours per frame because the frames became more complex. The team used the additional computing power to increase complexity.

That is how production works. Better tools do not automatically make the work easier. Often they let teams attempt harder work in the same available time.

Toys were a smart subject for early CG because they could be stylized, plastic, manufactured, and believable without asking the technology to solve every hard problem of human realism. Choosing toys let the filmmakers use the technology’s strengths while avoiding some of its limitations.

The Disney relationship mattered

Pixar needed what Disney knew: feature animation discipline, story development habits, distribution experience, marketing, and the institutional memory of making animated films for a real audience. Disney needed what Pixar had: a technical and creative capability that was not available inside the old system in the same way.

Pixar's timeline says Disney and Pixar announced an agreement to make and distribute at least one computer-generated animated movie; Pixar's later SEC filings describe the feature-film agreement as a 1991 deal. Work began on what became Toy Story while Pixar was still making commercials and shorts.

The agreement helped make the feature possible.

Shorts proved a capability. Commercials gave the team production experience. Disney's relationship helped force the question a feature has to answer: can this become a finished thing with a release date, an audience, and enough emotional clarity to justify all the machinery behind it?

The distribution agreement gave Pixar a way to release the finished film.

What Jobs learned

We should be careful here. Jobs was not giving a private diary entry. He was doing an interview. He was also a very good storyteller about his own life.

The interview is evidence of how he described the experience, rather than a complete account of what he privately learned.

The film gave Pixar a product audiences would pay to see.

Pixar’s technical work became part of a film that families bought tickets for and children rewatched. Pixar's timeline notes that Toy Story was nominated for Best Original Screenplay, a recognition of the writing as well as the film’s technical achievement.

Jobs also describes management as supporting the people with the expertise to make the film.

He describes management as creating the environment, removing obstacles, putting teams together, keeping the quality bar high, and handling relationships like Disney.

Creative teams need time, constraints, trust, argument, and protection from bad interference. They also need delivery discipline. "Leave artists alone" is not a production strategy. "Micromanage the thing you do not understand" is worse.

The trick is organizing the work so that creative judgment informs decisions about schedule, budget, technology, and distribution.

AI has made demos cheap

AI can produce images, animatics, voice tests, code prototypes, and other pitch material quickly. Some of it helps a team explore a direction; some is inaccurate, unusable, or legally uncertain. Its finished appearance does not establish that it is ready for use.

A generated concept image can help a production designer explore a direction without supplying a buildable set. A coding agent can produce a prototype without establishing that it will work reliably for users. The team still needs to evaluate the result, complete the remaining work, and check permissions, handoffs, and revisions under actual production conditions.

What to ask after a demo

For a new production tool, I want to know who it helps, what it makes possible, and what remains to be done before the result can be used. Does it reduce cost or time? Does it make a decision easier to evaluate? Which judgments and checks does it still require?

Jobs helped keep Pixar operating long enough to make a feature. Catmull, Lasseter, Stanton, Docter, Ranft, the animators, story artists, engineers, technical directors, production teams, and many others made the film. Disney helped release it.

The audience responded to the toys and their story. The technical achievement mattered because the filmmakers made something people wanted to watch again.

Watch the full 1996 interview here.

When Game Engines Meet Hollywood

Wed, 22 Oct 2025 00:00:00 GMT

I watched a PGA/VES panel from Nant Studios on using Unreal Engine as a narrative tool.

The panelists described where the engine helps, where it fails, and how they use it within a production schedule. Fulvio Sestito and Paul "Pizza" Pianezza from Realdream Studio, Eri Hawkins from Jim Henson Company, Emanuel Druckmann from Lightspeed LA, and Zach Staenberg, the Oscar-winning editor from The Matrix trilogy, were not selling a fantasy version of production. They were talking about dailies, lenses, mocap suits, facial rigs, LiDAR scans, line producers, ADs, budgets, post handoffs, and the old problem of getting everyone to understand the same shot before it costs a fortune.

Game engines let teams test proposed shots against practical requirements before a set is built or a crew arrives. That makes some decisions possible earlier.

A shared scene for planning

The old version of this conversation was about previs. That is still part of it, but it is too small a frame now.

Real-time engines can help with previs, techvis, postvis, virtual scouting, LED volume work, in-camera VFX, set design review, camera blocking, lens choices, lighting planning, VFX planning, location planning, stage planning, and client approvals. They can also become the place where assets move between departments instead of getting rebuilt five different ways by five different vendors.

The shared scene can help departments coordinate their work.

On a real job, the problem is rarely that nobody can imagine something cool. The problem is that the director's idea, the agency deck, the production designer's model, the DP's plan, the VFX supervisor's assumptions, the line producer's budget, the client's approval, and the post team's delivery path are often living in separate places.

A shared real-time scene can expose the gaps earlier.

Can the camera physically be where the board says it is? Does that lens make sense in that space? Does the LED wall need a different background plate? Is the practical set tall enough? Is the extension doing more work than anyone budgeted for? Does the lighting direction match the location? Does the actor have somewhere to move? Does the VFX team need a scan before the stage is struck? Is the client approving scale, mood, or actual geography?

Those are not abstract questions. Finding the answers late can require expensive changes.

Postvis on The Killer

Zach told the best practical story of the night. He was working on John Woo's The Killer when the SAG strike shut down the actor side of the production. They were shooting in Paris. The actors had to leave, but the French crew was still contracted for more time, so the team shot what they could with stunt performers: wide shots, back-of-head material, action pieces that could still be useful.

That left Zach and Woo with a pile of footage and missing shots needed to connect it.

Fulvio came into the editing suite with a mocap suit and built a couple blocks of Paris in Unreal based on the location. They could work directly with him in the editing suite. The director and editor could pick lenses, choose camera rigs, check what those rigs could actually do, look at the height off the floor, test a move, change the lens, adjust the blocking, and get material into Avid quickly enough to cut against the live-action footage.

In a traditional previs workflow, Zach said, you would give notes and get shots back a week later. Here, if they were pushing, they could get useful material the same day.

That time difference changes the conversation. The team can assess and change the shot together rather than wait for the next delivery.

One Universal exec looked at the cut with postvis mixed into live action and asked which was which. That does not mean the Unreal material was final. It means it was good enough, in context, to let people make editorial and production decisions instead of staring at a blank spot and guessing.

The planning tool can create planning problems

The same The Killer example also showed the trap.

Zach described the postvis becoming a kind of bible for what they wanted to shoot. That is powerful. It gave everyone a clear target. But because the workflow came together under pressure, some production voices saw the work late. The line producer and AD looked at shots and said, essentially, that one is too expensive for the budget.

The problem was who participated in the review, and when.

If the engine is being used to make production decisions, production has to be in the room early enough to shape them. Otherwise, you can create a clear depiction of a shot the production cannot afford. The director loves it, the editor can cut it, the client can understand it, and then the people responsible for the day have to explain why the shot cannot happen.

Who is reviewing for budget? Who is reviewing for schedule? Who is checking rigging, lenses, locations, safety, crew time, LED constraints, art department implications, VFX handoff, and post delivery? What is accurate in the model, and what is only a storytelling placeholder?

The tool moves decisions earlier. It does not make those decisions automatically correct.

Digital puppetry, not just digital pictures

Eri's work at Jim Henson Company pushed the conversation into a different category. They built a fully digital virtual production environment that works more like a three-camera sitcom. No live-action plate. No compositing in the normal sense. Motion capture performers handle bodies while Henson puppeteers perform faces. Virtual cameras sit on physical pedestals. Performers can see themselves as digital characters in the environment while they are performing.

That matters because puppetry is brutally sensitive to latency. If the feedback is late, the performance suffers. Earlier versions of the workflow had limits: low-resolution assets, flatter lighting, more meetings about what the final thing might look like. Unreal let them bring more of the creative conversation to the front of the process.

Lighting was a real example. If you cannot see dramatic lighting during the performance, you hedge. You keep it flat because nobody wants the character to disappear into a shadow no one could judge on the day. Once the performers and the team can see the scene working in real time, lighting becomes part of the performance and direction instead of a downstream correction.

The newest Henson example was real-time dynamic interactive fur on a project called Monster Jam, with monsters dancing. The fur reacts to the performance in real time, so performers can see it and incorporate that motion back into the work. That changes the performance: the simulation is not just a visual layer added later. It becomes part of what the performer reacts to.

Real-time feedback lets departments and performers respond to effects that previously arrived after the performance.

Small teams, more options, more danger

Pizza talked about helping friends win a commercial pitch. The ask was basically an animatic. Realdream brought it into Unreal and used the same time to generate multiple shot ideas and approaches instead of one fixed version.

That is the obvious appeal. A smaller team can move through more options faster. You can test a camera idea, a lighting idea, a piece of action, or a location approach without waiting for a traditional render-review cycle. Emanuel made the same point from the game side: in a real-time scene, moving a light or moving a camera can become a two-minute conversation instead of a next-day review.

The good version of that is better exploration. The risk is continuing to revise without deciding.

Eri said the best thing and the hardest thing about Unreal are the same thing: you can iterate pretty much endlessly. The question is when you make it stop.

Traditional pipelines impose waiting periods. You send work out. You wait. A render takes time. A vendor needs a brief. A review happens tomorrow. Those delays are annoying, but they also encourage teams to resolve questions before sending the next brief. Real-time tools remove some of that friction, which means the team needs a different kind of discipline.

Sometimes the right answer is to make the image look worse on purpose. Pizza mentioned reviewing in grayscale so people do not get distracted by the wrong issue. If the question is blocking, do not let everyone argue about color. If the question is geography, do not let everyone chase wardrobe texture. If the question is whether the camera move works, label the set dressing as approximate and keep the room on task.

Real-time does not remove review structure. It makes review structure more important.

The Quarry and the asset question

The most obvious film/game boundary case was The Quarry, the choose-your-own-adventure horror game. They shot roughly 33 hours of performance capture with 13 actors. That is an amount of performance material comparable to a miniseries, produced for a game.

The director could look at dailies, evaluate performances, and place cameras with a level of freedom that does not map cleanly to a traditional shoot. Actors in mocap suits could see themselves as digital characters inside the actual game environments on monitors on the stage. The team could move the center of the stage around and keep working in different parts of the virtual set.

This is where producers should pay attention to asset reuse. A film asset, a game asset, a previs asset, a VFX asset, and a marketing asset are not automatically the same thing. They have different fidelity, topology, texture, rigging, lighting, file format, performance, and licensing requirements.

But there is a practical reason to try: departments are going to want to reuse more of the same world. Not because reuse is always clean, but because rebuilding the same idea in every department wastes time when the schedule is tight and the client is already confused.

If the model is going to move across departments, someone has to own it. Someone has to maintain version control. Someone has to know which asset is reference, which is approved, which is final pixel, and which is an approximation used to help the director make a decision.

Without that maintenance, departments may receive files whose status and purpose they cannot identify.

When not to use it

The panel was refreshingly clear that not every project needs this.

A small dialogue film may not benefit from building a real-time production environment. A simple commercial may not need virtual scouting. A stage job may not need an LED volume. A beautifully built Unreal scene can still be the wrong answer if the budget, schedule, crew, and approval path do not justify it.

The technical limits are real too. Some data transfers readily into Unreal; other data requires conversion or additional work. Getting material out into a traditional VFX pipeline can be painful. Passes and mattes are not always what compositors want. Depth of field is a post-process effect, not physically correct in the way a VFX pipeline may require. Axis and scale differences can cause errors when moving between Maya and Unreal. Windows-centric workflows can run into the reality that many VFX pipelines are Linux-based.

There are ways to exchange data. USD matters. Larger studios can build tools. A team with TDs can solve problems a small run-and-gun team cannot. But the point stands: you have to know what job the engine is doing.

Is it previs? Techvis? Postvis? Final pixel? Reference? A virtual scout? A camera planning tool? A client approval tool? A live stage system? A source of assets for downstream VFX?

Those are different promises.

Review the scene with production

A virtual scout can resolve eyelines, scale, and background direction before construction. Postvis can help departments agree on the purpose of a missing shot. For example, spending $5,000 on planning may be worthwhile if it prevents a $50,000 correction on shoot day.

Bring the AD, line producer, art department, VFX, camera, and post into the review early enough to check assumptions. Label what is accurate, what is approximate, and what remains unverified. Give the review a specific purpose, such as assessing the camera move or deciding what must be built.

The producer test

Before spending real money on the workflow, I would ask:

What decision does the engine help us make earlier?
Who owns the model and assets?
Is this previs, techvis, final pixel, or reference?
What departments need to trust it?
What assumptions are baked into the scene?
Does camera, lens, scale, and lighting match the real shoot?
Can art department, VFX, camera, and post all use the same assets?
What happens if the director changes the blocking?
Is the workflow saving time, or just moving cost into a different department?
What needs to be approved before shoot day?
What can break on stage?
Who maintains version control?

If the team cannot answer those questions, the engine may still be useful. But it is not yet a production plan.

The labels are behind the work

Someone asked whether a short film made in Unreal should be submitted to the Academy as animation or live action. The answer from the room was basically animation.

That sounds like a classification issue, but it points to the bigger thing happening. The work is moving faster than the categories. Game people are thinking more cinematically. Film people are using game engines to plan and sometimes make shots. Editors are cutting postvis that can fool an executive for a second. Puppeteers are performing digital characters with real-time fur. Directors are scouting virtual sets. VFX is asking whether the LiDAR scan can move into the engine. Producers are trying to figure out what is real, what is approved, and what is about to become expensive.

Those overlapping practices make the categories less straightforward.

What producers need to understand

Real-time engines introduce their own costs, specialists, dependencies, and versioning problems. Producers need enough understanding to judge whether the workflow helps a particular job: which decisions it makes possible earlier, who must participate, and what the model can reliably establish.

When Several Systems Change at Once

Tue, 21 Oct 2025 00:00:00 GMT

I watched Peter Leyden's Big Think video and it gave me a useful name for a feeling I have not been able to shake.

I distrust the claim that we can identify a single decisive year while living through it.

What interests me is how many systems are changing at the same time.

Changes in AI, software, media, and work affect one another. It is difficult to adjust to them at once.

The exact year matters less to me than the difficulty of adapting to several changes at once.

The Useful Part of Leyden's Frame

Leyden's big historical pattern is that America periodically goes through periods when institutions fail, people contest their authority, and new institutions are built. In the video, he points to the founding era around 1787, the post-Civil-War era after 1865, and the post-World-War-II era after 1945.

I don't know if the 80-year-cycle thing is "true" in any clean historical sense. A recurring interval does not establish a reliable historical rule. These frameworks can make contingent human choices sound predictable.

But as a mental model, it helps.

Existing institutions are struggling at the same time new tools are becoming usable. That is the part of his account I recognize.

You can see it everywhere. Media businesses are struggling to adapt. Search is changing. Trust in institutions is shot. Hollywood is trying to figure out whether AI is a tool, a legal exposure, a labor threat, or all three. Software teams are trying to decide what junior work even means when an agent can scaffold a feature, write tests, and explain the code back to you.

Meanwhile, individuals are rebuilding their own personal infrastructure because public information is difficult to evaluate and keep track of.

The Convergence Is the Story

Leyden talks about three big technology tipping points: AI, clean energy, and bioengineering. OpenAI introduced ChatGPT on November 30, 2022. The 2020 Nobel Prize in Chemistry went to Emmanuelle Charpentier and Jennifer A. Doudna for developing a method for genome editing. The International Energy Agency describes solar PV and lithium-ion batteries as learning-curve technologies, with costs falling as cumulative capacity doubles.

The effects extend into several areas of everyday work.

AI is not just a chatbot. It changes software, research, design, writing, customer support, search, scheduling, legal review, coding, editing, and the basic expectation of how fast a thought can become a draft.

Media used to be easier to talk about as "content," which was always a dumb word but at least pointed at something. Now it is distribution, trust, identity, recommendation systems, fandom, propaganda, newsletters, podcasts, YouTube clips, TikToks, Discord servers, and whatever the hell X is today.

Work gets flattened into remote versus office because that is the argument people know how to have. The real shift is deeper: meetings, email, project management, hiring, training, documentation, review cycles, approvals. The routine processes that organize work are changing too.

Economics is the same. Inflation and interest rates matter, obviously, but the deeper question is whether people believe the deal still works. Housing, healthcare, education, subscriptions, software rent, creator income, job security, retirement, the cost of being a functioning adult. Many people find those costs difficult to manage.

Trust is not just misinformation. It is the loss of shared reality. Screenshots can be fake. Audio can be fake. Expertise is politicized. Institutions overplayed their credibility. Random people on the internet are sometimes right before the official channels are. That makes it harder to decide whom to trust.

Creative tools are crossing old boundaries. Unreal Engine is not just for games. It is previs, post-vis, virtual production, digital puppetry, and real-time filmmaking. AI image tools are not just making pretty mood boards. They are becoming part of pitch decks, look development, location planning, costume visualization, and concept exploration.

Then there is personal infrastructure, which has become more important to how I keep track of information. Notes, archives, backups, automations, password managers, local files, RSS feeds, read-it-later queues, personal websites, media libraries, AI assistants, private search across your own stuff. When the outside world gets noisier, your own system matters more.

People have to adopt or assess the tools while dealing with existing workload, financial, and institutional problems.

Adopting AI is harder when the organizations expected to use it are already under pressure.

Production Is a Good Example

Production makes this easier to see because production requires technology to meet practical constraints.

On a real show, nobody cares that your AI tool can make a beautiful impossible room. Can construction build it? Can the camera move through it? Can VFX use it? Does it match the budget? Does legal approve the training data? Does the union care? Does it save the art department time, or does it create a thousand new little cleanup tasks?

The answers determine whether the tool is useful on that job.

The practical stuff is much less glamorous and much more important: searching a video archive with natural language, roughing out a shot list, tone-checking an email, generating a first-pass lookbook, turning a location photo into a rough 3D reference, testing furniture in a virtual set, cleaning plates, doing roto prep, comparing schedule options, summarizing production notes.

These uses reduce the time needed for particular tasks.

The distance between idea, reference, test, revision, and decision gets shorter. That changes the work even when the crew size does not change. It changes expectations. It changes what "fast" means. It changes who can make a credible pitch. It changes how much polish people expect before a thing is even real.

Same with Unreal. The interesting part is not that a game engine can make pretty images. The interesting part is that film people, game people, editors, directors, VFX supervisors, and production designers can all start touching the same virtual object earlier in the process.

That changes how departments coordinate, as well as which software they use.

Software Is the Same Pattern

Software is going through its own version of this.

AI is shortening the time between proposing a software change and reviewing a working version.

You can ask an assistant to inspect a repo, find the relevant files, make a scoped change, write the tests, run the checks, explain the diff, and leave documentation. Not perfectly. Not magically. But well enough that the shape of the job changes.

The bottleneck moves.

It used to be "can I produce the code?" More and more, it becomes "do I understand the system well enough to ask for the right change, review the result, and know what failure would look like?"

That requires understanding the system and evaluating changes to it.

The same thing is happening in media. The bottleneck used to be access to publishing. Then it was attention. Now it is trust, taste, verification, and persistence. Anyone can publish. Anyone can generate. Anyone can clip. That makes actual judgment more valuable, not less.

Existing Institutions Still Matter

This is where Leyden's historical frame is helpful without treating it as a prediction.

When old systems stop working, the people who benefited from them do not usually say, "Fair enough, let's redesign this thoughtfully." They defend the old system. Sometimes because they are cynical. Sometimes because their whole identity is built on it. Sometimes because the replacement really is undercooked and dangerous.

You can see it in media companies trying to defend distribution models that no longer make sense. You can see it in studios trying to use new tools without triggering labor blowback. You can see it in universities trying to respond to AI with plagiarism panic instead of redesigning assignments. You can see it in software orgs pretending nothing has changed while individual engineers quietly rebuild their workflows. You can see it in politics, where institutions built for a slower information environment are getting battered by rapid cycles of online outrage.

Existing institutions retain power, while the proposed replacements remain unproven.

What I'm Doing Differently Because of This

I am taking my own infrastructure more seriously.

I want to choose how I store and evaluate information rather than simply accept each service’s defaults.

I want my notes searchable. I want my files backed up. I want my important work in formats I can move. I want my website to be mine. I want my media library to survive platform churn. I want AI in the workflow, but I also want receipts, sources, diffs, tests, and human judgment around it.

I am paying more attention to production workflows than AI demos.

The demo is usually the least interesting part. The real question is where the tool lands in the pipeline. Who uses it? What does it replace? What does it make faster? What new mess does it create? What approval process does it need? What happens when it is wrong?

I am treating trust like a design problem.

Screenshots, citations, provenance, version history, reproducible checks, clear authorship, local archives. These records help me check claims and recover earlier work.

I am trying to stay flexible without becoming gullible.

Some of this is hype. Some of it is real. The trick is not to pick a team and defend it forever. The trick is to keep updating as I see how the tools perform in practice.

What I Take From It

Leyden’s proposed historical cycle may be too neat. I do not need to accept it to find his account of simultaneous change useful.

AI affects software; software changes how work is organized; changes in work and media affect whom people trust. Those relationships make it difficult to assess one technology in isolation.

For my own work, the response is practical: keep recoverable records, check sources and outputs, and judge tools by what they help a team complete. Those habits remain useful even if the historical prediction is wrong.

What AI Is Actually Doing on Set

Thu, 09 Oct 2025 00:00:00 GMT

This came out of a PGA Physical Production Committee conversation about where AI is actually showing up in the work, not where demo reels claim it will show up.

Public discussion often emphasizes generated films and replacement of entire departments. The panel’s examples were more specific: tools used to support particular production tasks.

The uses that interested me most were retrieving production records, comparing documents, preparing references, and drafting routine paperwork.

That sounds less exciting than "make the whole campaign," but it is much closer to how production actually works.

Production is not one big creative act. It is a thousand decisions moving through approvals, rights, locations, budgets, call sheets, vendors, crew availability, client notes, agency notes, legal concerns, weather, parking, talent, post handoffs, and the sentence nobody wants to hear at 5:47 p.m.: "Which version are we looking at?"

AI is going to matter in physical production because production is full of searchable, comparable, summarizable, versioned, half-forgotten information. The opportunity is to make that information easier to find and share between departments.

The panel’s production experience

The panel had real production credibility. Leland Krane is a New York-based DP with more than 100 credits. Alex DiGerlando is an Emmy-nominated production designer. Jacquelyn Ryan-Krichevsky is Disney Entertainment Television's manager of production innovation and one of the committee co-chairs. Deirdra Govan is a costume designer and vice president of United Scenic Artists, Local USA 829.

They discussed approval processes, union positions, pre-production, lookbooks, costuming, VFX, budgeting, scheduling, and what works on a set.

The mood was pretty clear: AI is useful. AI is overhyped. AI is risky in ways that are not theoretical. AI is already in the work whether people admit it or not.

That is a more useful starting point than panic or boosterism.

The stigma is real, but it is uneven

Everyone agreed there is still a stigma around admitting you use AI tools. The higher up the production chain you are, the less likely you may be to say it out loud. Leland mentioned users making major streaming shows who do not want people to know they are using AI-assisted shot-list tools.

That stigma makes sense in context. AI landed in the middle of strike anxiety, job-loss anxiety, copyright lawsuits, and a lot of very bad salesmanship from tech companies. Nobody wants to look like they are sneaking a tool into the process to cut a crew member, bypass a union role, or use somebody else’s work in a pitch deck without permission.

But the stigma is already getting more complicated, because the actual uses are not all the same thing. Midjourney images for a pitch deck are one kind of use. A budgeting and scheduling tool is another. A searchable video archive is another. An on-device color tool in post is another. A costume pre-fit system is another.

"AI" is too broad a word to be useful by itself.

Alex made the obvious comparison: computers changed filmmaking. Photoshop changed filmmaking. Movie Magic changed filmmaking. Nobody now talks about "computer use in production" as one category. We talk about the actual tool and the actual job.

That is where the AI conversation needs to go.

Generating references

The image and video tools get the attention because they are easy to demo. The result is visible immediately.

But the panel kept coming back to a less dramatic point: generative AI is often more useful as disposable reference than as production work.

Alex was blunt about Midjourney. It makes beautiful images, but that does not mean you can build a set from them. The physics may not work. The details may not resolve. The result may be more like a tone reference pulled from a magazine than actual design documentation.

Working with a concept illustrator who uses AI as part of their process can be useful. Sitting alone in Midjourney, fishing for a production-ready answer, is usually not the same thing.

An impressive demo can obscure the difference by showing a result that looks finished to a non-specialist. Production people immediately start asking different questions.

Can we build it? Is that window load-bearing? Where does the door swing? Is that lens real? Is the period detail right? Does the wardrobe work on an actor's body? Can we clear the location? Does this match the approved creative? Who signed off on it?

An image-generation demo generally does not answer those questions.

Supporting routine production tasks

Leland distinguished generative AI from process-oriented uses such as scheduling, budgeting, and search. Mistakes in this routine coordination can be expensive: a script change that does not reach wardrobe, conflicting vendor assumptions, or a VFX requirement discovered after the shoot.

Where AI actually helps

AI can turn meeting transcripts into action items, open questions, department-specific follow-ups, and a clean list of decisions that still need approval.

It can compare two bids and identify differences in assumptions: different prep days, different kit fees, different travel language, different overtime exposure, different exclusions.

It can summarize script changes by department. Not just "new draft attached," but "new night exterior on page 14, additional background, wardrobe continuity affected, insert shot added, animal reference removed."

It can organize references and treatments so the team is not hunting through old decks, boards, PDFs, links, notes, and Slack threads trying to remember where the approved thing went.

It can search prior jobs for useful precedent: location decks, vendor docs, past estimates, agency notes, casting references, weather plans, permit language, safety memos, post specs, delivery checklists.

It can create first-pass call sheet and checklist drafts from approved source material, then leave the human team to verify, adjust, and own the result.

It can help production teams find what changed between versions: the script, the bid, the boards, the deck, the agency note, the legal language, the delivery spec.

It can rough out visual references early enough that the creative team can react before money is spent. That is very different from making final creative.

It can help post teams search footage, transcripts, selects, plates, and archive material using natural language instead of somebody trying to remember the exact file name.

It can keep institutional memory from disappearing into Slack, email, Google Drive, Dropbox, Frame.io, text threads, and the brain of one coordinator who is already on the next job.

These are existing information-management tasks that better search and comparison tools could make easier.

The examples that stuck

Alex told the best story in the conversation. On Black Rabbit, they built a virtual model of a set in Unreal Engine while the physical set was still under construction. The construction schedule was compressed, so the director could not spend enough time in the finished space before needing to plan shots.

The Unreal model let the director understand the space and start shot-listing before the build was complete.

A year later, Alex said the workflow had already moved. A set decorator could send a photo of a sofa they were considering, turn that photo into a 3D model in seconds, drop it into the virtual set, and see how it worked in the space before buying it.

That lets the designer test a choice before committing to a purchase.

Deirdra talked about costume design in a way producers should pay attention to. Actors often cannot come in until two days before they are on camera. Tools like Clo3D, The Fabricant, and Maison Meta can help with pre-fits and visualization before the actor is physically available.

Again: not replacing the designer, the fitting, the actor conversation, or the human part of the work. It is pre-work. It lets the department try things earlier, save time, and prepare for the actual fitting.

Leland described using AI as a spellcheck for ideas. He uses it to thought-check concepts and tone-check emails before sending them. That may sound small, but production runs on communication. A badly phrased email can lead to hours of clarification. A clear one can prevent that confusion.

Jacquelyn talked about using AI to help craft better prompts for other AI tools, which is funny but practical. If the tool is going to be part of the workflow, learning how to get to the usable output faster matters.

She also mentioned TwelveLabs for natural language video search. That is exactly the kind of use I expect to become normal. If you have a large archive of footage and you need every shot of a specific object, place, setup, or action, natural language search can save time.

The tools people are actually using

The conversation was useful because it stayed close to actual tools and actual workflows.

For image and video generation, people mentioned Adobe Firefly, Moonvalley Marey, Runway, and Midjourney. The important distinction was not which one makes the prettiest image. It was training data, permission, guardrails, and whether the output is appropriate for the use.

For workflow and process, people mentioned ChatGPT, Claude, RivetAI, Filmustage, Movie Magic, Version Zero, TwelveLabs, ShotKraft, ShotDeck, and Microsoft Copilot.

That list is messy because production is messy. There will not be one AI tool for production. There will be a bunch of narrow tools, some general assistants, some studio-approved systems, some vendor-specific workflows, and additional work to connect them.

The best tools will fit the way production already works. Leland made that point clearly. Tools built by filmmakers tend to understand the pain better than tools built by technologists guessing at the workflow.

That is why integration matters. A scheduling tool that integrates with Movie Magic has a better shot than a shiny new system that requires every line producer to abandon the way they already work.

Where it gets dangerous

The risks are not abstract. They show up in the normal places production already has risk: rights, labor, confidentiality, accuracy, approval, and source material.

Rights and likeness are the obvious ones. If a tool can generate something that looks like a person, a performer, a protected character, a brand, a photographer's style, or a designer's work, that does not mean you have permission to use it. Technical capability is not clearance.

Union and crew implications matter. Deirdra was direct about this: the tool is useful when it helps visualize and work more efficiently. It becomes a problem when it is used as a way to eliminate positions or undermine the work covered by agreements.

Client confidentiality matters. Production teams handle unreleased scripts, treatments, boards, celebrity information, budgets, locations, schedules, security details, vendor bids, agency strategy, and legal language. You cannot dump that into a random system because the summary would be convenient.

Training and data leakage matter. Studios care about what goes into a system and what comes out of it for good reason. Some tools are built around licensed or compensated data. Others are not. Some tools retain inputs. Some do not. Some give admins control. Some provide little visibility into how they handle data.

Hallucinated details matter. AI is very good at sounding organized while being wrong. In production, fake certainty is dangerous. A hallucinated permit rule, wrong travel time, bad rate assumption, invented source, or mistaken delivery spec can cost real money.

Synthetic references can be mistaken for approved creative. That is a quiet but serious problem. A generated reference can enter a deck, get forwarded, lose its label, and suddenly become "the thing the client liked." Now the team is chasing an image that was never designed, cleared, buildable, shootable, or approved.

Bias matters in casting, locations, wardrobe, and reference workflows. If a tool clusters options or proposes references based on biased source material, it can narrow the imagination of the team while pretending to broaden it.

Source resemblance matters. If you use an AI output without knowing what it resembles, you may miss a resemblance that makes the output unsuitable. That is especially true in pitch materials, lookbooks, character references, brand work, and anything that will be shared outside the team.

The point is not "do not use AI." The point is: identify the risks of the specific use.

The studio approval process is a preview of the future

Jacquelyn described the studio side in practical terms. At Disney, she said AI use cases go through a specific review process. They are checked against responsible, ethical, human-centric use. If a proposed use eliminates union positions or creates other obvious problems, it gets vetted early.

Netflix has published guidance for generative AI use in content production. Most studios are not that public. But the direction is clear: productions will increasingly have approved tools, restricted tools, and use cases that require review.

That may feel slow to independent producers who can sign up for a tool and start working in ten minutes. But studios have specific responsibilities to consider. They have to worry about rights, security, labor, confidentiality, PR, legal exposure, guild agreements, and the fact that one careless workflow can create problems throughout a production.

Independent productions have more flexibility, but they do not get a pass on judgment. The absence of a studio approval panel does not make a tool safe. It just means the producer has to ask the questions directly.

The producer test

Before using an AI tool on a production, I would ask:

Does it save time on a task we already do?
Does it reduce confusion between departments?
Can we verify the output quickly?
Does it preserve source links, citations, file references, or evidence?
Can it work with private material safely?
What happens to the material we input?
Who can see the output?
Does this create a rights, likeness, union, or client-confidentiality issue?
Is the output clearly labeled as draft, reference, internal, or approved?
Who is responsible if it is wrong?
Does it help the crew, or just impress the person buying the software?
Does it fit the tools the team already uses?
Does it work with approvals, vendors, legal, accounting, post, and delivery?

Lots of tools are impressive in a demo. Very few are impressive after the agency has notes, the client wants a change, the location shifts, the director swaps the shot order, legal needs backup, the vendor has a different assumption, the crew is moving, and the delivery spec changed last night.

Production tools need to remain useful when plans change.

What has not changed

Nobody on the panel said AI had reduced their crew size on set.

Leland said the camera department still looks like the camera department: operator, AC, second AC, loaders. Alex said he has not reduced his crew and would not trust AI to produce construction drawings without human verification. One bad measurement and you are building the wrong thing.

The pressure to work faster is real, but the panel did not attribute it to AI. The pressure is coming from tighter budgets, compressed schedules, and wider market pressures.

That distinction matters. AI will get blamed for things that are really budget decisions. It will also be used to justify budget decisions people already wanted to make.

Producers should be honest about that.

If a tool helps a department get 20 percent closer to the answer faster, great. If that savings gets turned into less prep, fewer people, and the same expected quality, then the tool did not solve the production problem. It just gave management a new excuse.

Verify according to the consequences

Can you trust AI?

Not by itself. Everyone basically landed there.

The useful comparison was Wikipedia or Google. It can get you started. It can point you toward sources. It can help you think. But you still have to check the work.

Jacquelyn mentioned cross-referencing between ChatGPT and Claude. Alex talked about using it for research, while still checking the answer. Leland framed it around stakes. Using AI to create rough lookbook images so a director can respond to a tone is low stakes. Using AI for final pixel, legal clearance, construction, safety, budget authority, or delivery is another category.

Use it to prepare a draft, then assign someone to verify the result before it is relied on.

The more expensive the consequence of being wrong, the more formal the review has to be.

Making production records searchable

The part I keep coming back to is memory.

Every production generates a private archive: decks, estimates, bids, shot lists, scripts, boards, scout photos, casting links, wardrobe references, VFX notes, post specs, call sheets, wrap documents, releases, invoices, client feedback, agency comments, legal notes, email threads, Slack channels, and meeting transcripts.

Most of that memory is badly stored. Some is in Google Drive. Some is in email. Some is in Frame.io. Some is in Dropbox. Some is in a vendor portal. Some is in a coordinator's head. Some disappears when the job wraps.

AI could help a production company retrieve answers from its own records:

Have we solved this location problem before?
Which vendor handled the last tabletop job with similar constraints?
What changed between the first estimate and the approved one?
Which client notes keep recurring?
What did legal flag last time we used this kind of footage?
Which releases are still missing?
What did the director approve in the meeting versus what made it into the deck?
What does post need that production has not captured yet?

Answering those questions requires reliable retrieval from records the team has permission to use, with source references that someone can check.

It is also where private, controlled systems matter. The more sensitive the production, the less comfortable I would be putting records into a public service whose data handling I could not verify. The smart version of this lives close to the source material, respects permissions, tracks evidence, and gives humans something verifiable.

My take

The panel’s examples concerned testing furniture before buying it, visualizing costumes before the actor is available, searching footage faster, thought-checking ideas, tone-checking emails, building better references, and bringing VFX conversations earlier so the production team is not discovering unplanned VFX requirements in post.

Those are specific improvements a team can evaluate. Do they save time? Can the output be checked? Does the tool preserve the source material and make responsibility clear?

I expect the most useful adoption to come from teams that understand their own process well enough to answer those questions before depending on the output.

Turning a Childhood Comic Collection Into an Open Source App

Tue, 07 Oct 2025 00:00:00 GMT

My Dad passed away at the start of 2022, and my Mom decided to sell the house shortly after. I was on the East Coast for work, so I stopped by before heading home and spent about six hours going through my childhood bedroom closet, making quick decisions about what could fit into the three oddly shaped boxes I was shipping back to LA.

They were oddly shaped because I had already made the correct adult decision to prioritize two guitars.

When I got to the comic books I had not looked at in more than 20 years, I started shoving stacks into whatever empty space was left. In retrospect, this was not archival best practice. I had no real idea what issues I had, what condition they were in, or whether I was preserving anything coherent. I just knew I wanted them.

A week or two later, I had a stack of old comics sitting in my house and no real system for them.

I wanted to keep the books, but I still needed a practical way to catalog them.

Rediscovering the collection

I did what any reasonable person would do and opened a spreadsheet. Titles in one column. Issue numbers in another. Maybe a note about condition if I was feeling ambitious. About 30 minutes in, I was already tired of entering data.

Then I found CovrPrice, which was immediately better than my spreadsheet. I could see cover art, group issues by title, sort by release date, track estimated values, and generally treat the collection like an actual collection instead of a list of things I half-remembered owning.

CovrPrice did one important thing right: it made the collection visible again. That mattered. It also got me back into collecting. Three years later, I still visit MidTownComics.com religiously, buying new releases and hunting down issues I missed as a kid.

Keeping the inventory current was harder. A collection is only useful if the inventory stays current. The second logging new books feels like a chore, the inventory becomes incomplete.

The maintenance problem

A couple months ago, I got an email from CovrPrice saying my yearly subscription price was going up by 50%. I understand that running a web app is not cheap, and I am not offended by software costing money. Good tools should cost money. People should be paid.

But once I was using CovrPrice every week, the friction became impossible to ignore. It was slow. It was cumbersome. Routine updates required too many page loads.

The real problem was not storing comics. It was maintaining the state of the collection without making myself hate the process.

Adding new purchases took too long. Search, click, wait, add, wait, confirm, wait, repeat. Forms loaded slowly. Pages loaded slowly. The delay discouraged me from making small updates.

Instead of logging five new books when they arrived, I would put them aside. Then there would be 12. Then 20. Then I would have a growing stack of unlogged books next to my desk.

A subscription is less appealing when using the product discourages the task you bought it for. At that point, you either accept the mismatch, stop maintaining the archive, or build something that fits.

Building the tool around the habit

Around the same time, a few things converged. I had been getting slowly addicted to what I call "vibe-coding" with AI assistants like bolt.new. We went on vacation for two weeks in the South of France, where I naturally spent too much time thinking about side projects. And while we were there, bolt.new had some kind of free tokens weekend that made experimentation feel cheap enough to try.

I have a love/hate relationship with tools like that. They are very good at starting projects and getting you 80% of the way there. Then, eventually, they can consume tokens repeating failed fixes until you intervene and debug the code.

AI made it easier to test the shape of the app quickly, but I needed a real workflow against which to judge the result. The reason the AI-assisted build worked at all is that the problem was not abstract. I had the books, the pain point, the fields, the habits, and the failure mode sitting in front of me.

So I started building a personal, self-hosted comic collection tracker around the way I add and find books.

The app tracks titles, issue numbers, variants, cover images, condition, slabbed versus raw books, current values, and where things live in the collection. It gives me search, sorting, basic stats, and a faster add flow. It is built around the maintenance habit, not around the fantasy that someday I will sit down and lovingly enter metadata for an entire afternoon.

I built it with React, TypeScript, and Tailwind CSS. I also added a simple n8n workflow that updates the site's main JSON file whenever I add new purchases to a Google Sheet. The next step is probably triggering that automatically from email order receipts from places like MidTown Comics, because that is where the workflow actually starts.

The value-tracking part is intentionally modest. I list current values for each comic, but I do not have it tied into a pricing system. I experimented with pulling spot averages from eBay listings, but exact issue matching, variants, and condition made that messy fast. It would be interesting to solve eventually, but it is not the point. Organization matters more to me than automating estimated resale values.

Once I had the build stable enough for my own use, I made the repo public and released it as open source: github.com/banastas/comics.banast.as

Why personal archives need personal tools

An inventory stays useful only if it stays accurate. Reducing the effort needed to add a purchase makes me more likely to keep it current.

That is also the concern behind the photography workflow posts. The system needs to be easy enough to maintain for years, including when the collection grows or the software changes.

It is also why this now sits next to the same argument I make in Build on Your Own Land: the canonical version of something you care about should not depend entirely on someone else's interface.

Building your own tool is not always efficient. Sometimes it is a ridiculous use of time. Sometimes the mature commercial product is better, cheaper, safer, and absolutely the right answer. I still use plenty of rented tools because I enjoy having a life.

My collection has personal history as well as financial value. I want a tool that makes it easy to find a book, check whether I own an issue, and record a new purchase.

Open sourcing the app does not make it universally useful. It makes it inspectable. It makes it reusable if someone has a similar need. It means the thing I built for my own collection is not trapped inside my own private setup. That is enough.

The app succeeds for me if I keep using it when new comics arrive. That is the practical test: whether the next update is easy enough that I actually do it.

What Geoffrey Hinton Is Actually Warning About

Mon, 29 Sep 2025 00:00:00 GMT

I spent 90 minutes watching Geoffrey Hinton talk about two different AI risks: people using the systems to cause harm, and people losing control of more capable systems. He also discusses the incentives that encourage organizations to deploy them quickly.

I care because the same forces Hinton is describing are already showing up in production workflows: smaller teams, faster approvals, synthetic first drafts, compressed timelines, unclear rights, and executives mistaking plausible output for finished work. If you make media, software, design systems, production tools, or anything else with a lot of moving parts, the questions also apply to how you organize and review everyday work.

1. What Hinton Actually Said

Hinton's warning has two layers.

The first layer is human misuse. Bad actors, sloppy institutions, and companies under pressure to move fast can use AI to scale things humans were already doing badly: scams, phishing, cyberattacks, political manipulation, synthetic media, autonomous weapons, and maybe eventually biological design. McKinsey cites a 1,265% rise in phishing attacks after generative AI platforms started proliferating in 2022. That is not the same thing as "all cyberattacks are up 1,265%," but it is a reason to examine how cheaper language generation and impersonation affect fraud.

Those risks do not require machine consciousness. They arise from how people use the tools.

The second layer is the one Hinton says took him longer to appreciate: AI systems may become smarter than humans in a way we do not know how to control. In the interview, he says, "If you want to know what life's like when you're not the apex intelligence, ask a chicken."

His point is not that chickens are morally inferior. His point is that being less intelligent than the dominant species is a lousy governance position. Chickens do not get a committee seat. They do not get an appeals process. They get whatever the smarter species decides is convenient.

Hinton has put the chance of AI leading to human extinction in the next few decades at roughly 10 to 20 percent. That is his judgment under substantial uncertainty, not a measured probability that can be treated as settled.

The useful thing about Hinton is that he keeps saying he does not know. He rejects both confident predictions and easy reassurances. In the interview, he says anyone who claims to know exactly what will happen, or exactly how to handle it, is talking nonsense.

His uncertainty is a reason to investigate the risk, rather than assume either catastrophe or safety.

The reason his warning lands differently from generic AI punditry is that Hinton has real standing. He pushed neural networks for decades, co-founded DNNresearch with Alex Krizhevsky and Ilya Sutskever before Google acquired it, shared the 2018 Turing Award with Yoshua Bengio and Yann LeCun, and in 2024 shared the Nobel Prize in Physics for work connected to the foundations of machine learning.

That does not make him infallible. It does make him worth taking seriously.

2. What People Are Overreacting To

Online discussion often simplifies the warning.

One version is: AI is alive, angry, and coming for us. That is not Hinton's argument. He does talk about the possibility that machines could have forms of understanding, self-modeling, and maybe even emotion-like states. But the immediate risk does not depend on whether a model is conscious or has intentions.

A call-center agent does not need consciousness to replace a call-center worker. A recommendation engine does not need hatred to radicalize people. A fraud system does not need ambition to impersonate your boss convincingly enough to move money.

Another overreaction is the idea that every job is going to vanish at once. Hinton does say routine intellectual labor is exposed, and when asked about career prospects in a world of superintelligence, he joked that people should "train to be a plumber." But that is not the same as saying all work disappears at once.

The first wave is not "AI replaces humanity." The first wave is more mundane and more irritating: fewer entry-level roles, smaller support teams, more pressure on everyone to produce at machine-assisted speed, and a lot of middle managers discovering that "efficiency" is a polite word for "somebody else gets laid off."

The third overreaction is treating Hinton as a prophet. He is not. He is a brilliant scientist with unusually deep intuition about neural networks, digital learning, and the history of AI. That expertise gives weight to his argument without making every prediction correct.

3. What People Are Underreacting To

The discussion gives too little attention to why organizations adopt these systems.

Debate about whether AI is conscious can distract from questions about its deployment. The harder question is: what happens when powerful prediction, persuasion, simulation, and automation systems are owned by institutions that are rewarded for growth, speed, extraction, and dominance?

Companies are not evil because they use AI. But companies are built to pursue advantage. If an AI system lets a company produce the same output with fewer people, there will be pressure to do that. If it lets a platform hold attention by making people angry, there will be pressure to do that. If it lets a military reduce its own casualties by moving lethal decisions into machines, there will be pressure to do that.

Those pressures follow from existing financial and strategic incentives.

Hinton's point about regulation is basically that capitalism works better with constraints. He argues for highly regulated capitalism: let companies make money, but force them to make money in ways that are socially useful rather than socially corrosive.

The problem is that frontier AI is international, expensive, strategically important, and politically intoxicating. No company wants to slow down. No country wants to lose. The EU has an AI Act, but Article 2 excludes systems used exclusively for military, defense, or national security purposes. That exclusion matters when considering which uses regulation addresses.

The other thing people are underreacting to is the difference between biological and digital intelligence.

Hinton's technical argument is that digital systems have advantages we do not. They can be copied. They can run many instances. They can share what they learn at enormous bandwidth. In an abstract for his lecture "Will Digital Intelligence Replace Biological Intelligence?", Hinton argues that digital computation allows many copies of the same model to run on different hardware, learn from different datasets, and share learning efficiently by averaging weight changes.

That ability to copy and share learning distinguishes digital systems from an individual human worker. Deploying many copies across tools and services can greatly expand their effects.

Again: the system does not have to be evil to be destabilizing. It only has to be competent and attached to current incentives.

4. What This Means For Creative And Production People

For people working in media, production, design, or software, changes in the cost of producing work affect staffing, schedules, and review.

When rough images become cheap, taste matters more. When first drafts become cheap, judgment matters more. When research becomes cheap, knowing what to ignore matters more. When synthetic footage, temp voice, generated boards, auto-cut selects, searchable archives, and agentic production tools become normal, the bottleneck moves from "can we make something?" to "should we make this, is it true, who benefits, and what breaks if we ship it?"

That requires evaluation as well as production skills.

Production people already know this better than most technologists. Every production requires coordination among people dealing with trust, taste, money, schedule, labor, safety, ego, weather, permissions, unions, vendors, logistics, and last-minute compromise.

AI can help with parts of that. It can summarize scripts, generate boards, search footage, prep call sheets, draft schedules, compare looks, build temp comps, transcribe interviews, organize research, and make the blank page less blank. Some of that is genuinely useful. Some of it is mediocre but fast. Some of it is faster to produce than it is to check or correct.

The mistake is treating the tool as the system.

If you work in creative technology, your job is increasingly to understand the whole pipeline: where AI saves time, where it creates hidden review costs, where it violates rights, where it leaks confidential material, where it repeats biases from its source material, where it makes people overconfident, where it turns one person's craft into another person's "optimization."

Reviewing an AI-assisted workflow requires specific questions:

Is this output good, or merely plausible?
What source material did it depend on?
What human role did we just compress, and was that role doing invisible quality control?
Who signs off when the system is wrong?
Are we saving labor, or moving labor into review, repair, and liability?
Does this make the work better, or just faster to approve badly?
Are we using AI to serve the people doing the work, or to make budget cuts look innovative?

I have heard people in production describe AI as either a solution to every problem or a threat to every job. Evaluating a specific tool requires a more detailed account of its usefulness, limitations, and risks.

These concerns affect approval procedures, vendor contracts, data policies, editing, design, budgets, and decisions about which tasks require a person.

5. What I'm Watching Next

I am watching five things.

First, entry-level opportunities. If AI takes over junior work, people may lose the assignments through which they learn to become editors, producers, designers, or supervisors. Layoffs are only part of that concern; the availability of training and supervised experience matters too.

Second, provenance. If media becomes cheap to generate and easy to personalize, trust moves from "does this look real?" to "where did this come from, who touched it, and can I trace the chain?" That matters for journalism, documentaries, marketing, archives, legal review, and every production that depends on public credibility.

Third, control of the tools. Who owns the models, training data, distribution services, computing capacity, and interfaces? Creative workers become dependent on a few providers if those companies control every tool, archive, and distribution channel they need.

Fourth, the safety theater. I am watching how many companies talk about responsible AI while shipping products that externalize risk onto users, workers, moderators, customers, and the public. A written policy needs enforcement, clear responsibility, and authority to delay a release when necessary.

Fifth, the people who can still see the whole system. This is the one I care about most.

Hinton's warning is not that AI makes humans irrelevant tomorrow. It is that we are building systems whose capabilities may outrun our institutions, our incentives, and maybe eventually our control. That warning should make us more serious, not more theatrical.

People responsible for these workflows need to understand how a tool affects the business and the people doing the work.

They need evidence to distinguish useful applications from unreliable or harmful ones, and authority to require review where errors would matter.

That is a practical response to Hinton’s warning even while the larger questions remain unresolved.

Watch the full 90-minute interview here.

What Phil Tippett Brought to CG Animation

Sun, 31 Aug 2025 00:00:00 GMT

Rob Bredow's TED Talk about artist-driven innovation at ILM is useful because it does not start with general claims about technological change. It starts with production history. Motion-control cameras. CG dinosaurs. De-aging. LED volumes. Generative AI tests. Actual jobs where a tool ran into a story problem, a schedule, a crew, and a director who needed the shot to work.

Tippett is remembered in this conversation because of the line from Jurassic Park, usually paraphrased as "I think I'm extinct." In Bredow's telling, the real moment came after an early CG dinosaur test changed the direction of the film. Tippett, who was known for stop-motion and go-motion creature work, saw where things were headed. Spielberg liked the line enough to give a version of it to Dr. Grant.

The familiar retelling makes this sound like the end of practical creature-animation expertise. Yet Tippett’s knowledge of creature performance remained useful when the production adopted different tools.

Applying stop-motion experience to CG

Jurassic Park was originally built around a blend of approaches. Stan Winston's team created full-scale animatronic dinosaurs for the close physical work. For shots where a dinosaur had to run, leap, or move with more freedom, the production explored stop-motion and go-motion techniques with Tippett's team.

Then ILM's CG tests changed the plan.

That part is real. It was a shock. Computer graphics could suddenly do something that had not seemed practical at that scale: put living, breathing synthetic animals into live-action plates with a freedom of movement the older approach could not easily match.

But Tippett's value was never only "the guy who knows how to move a puppet one frame at a time."

His value was creature performance: movement, weight, timing, anatomy, rhythm, animal behavior, and the hard-won ability to know when something imaginary feels alive. A tool can interpolate motion. It cannot automatically know why a predator pauses before striking, why a heavy animal carries momentum through a turn, why a head twitch feels alert instead of random, or why one extra beat before the attack makes the audience lean forward.

When the film moved toward CG dinosaurs, that knowledge did not become useless. It became necessary in a new form.

As documented in vfxblog’s oral history of the Dinosaur Input Device, ILM and Tippett Studio developed a way to use stop-motion techniques to create digital animation. The Dinosaur Input Device was essentially a stop-motion armature fitted with sensors so traditional animators could perform dinosaur motion physically and translate that motion into digital data. ILM could then apply the animation to CG models, light it, render it, and integrate it into the final shots.

The final image was digital, but the performance did not come from the software by itself. The dinosaurs worked because digital tools were guided by people who understood physicality.

Tool skill and craft judgment

A lot of people confuse tool skill with craft judgment because, in a stable era, they can look like the same thing.

Tool skill is knowing the software package. It is knowing the camera menu. It is knowing the edit system, the plugin, the render settings, the model, the API, the prompt pattern, the shortcut, the button. Tool skill matters. Nobody wants to sit through a session with someone who cannot operate the machine.

But tool skill is not the whole job.

Craft judgment is knowing what good looks like. It is knowing why something feels wrong before you can explain it cleanly. It is movement, rhythm, story, performance, taste, proportion, timing, audience, cost, feasibility, and context. It is knowing what to ignore. It is knowing when to push back. It is knowing what the tool cannot see.

That distinction becomes important when tools change.

If your value is only that you know how to use an older interface, you are vulnerable. That vulnerability comes from changes in demand, rather than any failure of character. Interfaces change. Workflows collapse. Vendors bundle features. Models absorb steps. A thing that used to require a specialist can become a dropdown, a template, or a prompt.

But if your value is judgment, taste, context, relationships, production reality, and knowing what good is, you have experience to apply. Applying it still takes effort. You still have to learn enough of the new tool to understand what it changes. But you are not starting from zero, because your experience includes more than operating the tool.

AI changes the toolchain

Discussions of AI often overlook that distinction.

Claims that nothing will change and claims that all human work will disappear are both too broad to help someone assess their own position.

AI will replace some tasks. AI will compress some workflows. AI will make some tool-specific skills less valuable. Some people will get displaced. Some skills will lose market value. Some workflows will collapse. That is not a thought experiment. It is already part of the pressure around creative work, production work, software work, research work, and the thousand little coordination jobs that hold projects together.

Developing the ability to evaluate work is a useful response alongside learning to operate new tools.

AI can generate a shot idea. It does not know whether the shot is shootable, ownable, safe, on brand, on budget, or right for the story.

AI can generate a creature image. It does not know whether the creature's motion will feel alive, whether the anatomy can support the action, or whether the design will still work after rigging, lighting, and editorial get involved.

AI can make a deck. It does not know whether the plan fits the schedule, budget, rights, crew, client requirements, or construction process.

AI can generate code. It does not know the product intent unless someone gives it the context, and it does not prove the behavior is correct without tests, review, and judgment.

AI can summarize notes. It does not know which sentence matters politically or operationally unless the person using it understands the room.

These are questions the people using the tool still need to answer.

Poor choices can be repeated faster and presented more convincingly when generation is cheap. Review needs to account for that.

Using generated material for exploration

The Landis Fields AI test in Bredow's talk is a good example because Lucasfilm did not present it as a finished movie. It was closer to a moving mood board: one artist using AI tools over a short period to explore what a probe droid might see on a new Star Wars planet.

A moving mood board can help a team react earlier. It can make a tone visible. It can give producers, directors, designers, and supervisors something to reject, combine, redirect, or investigate before significant production spending begins. It can be a faster way to ask, "Is there anything here?"

But the fact that a tool can make something that looks like a scene does not mean production happened.

A real show still needs people who can answer the next questions. What is the story? What is approved? What is cleared? What needs design? What needs to be built? What can be shot? What needs VFX? Who owns the assets? What survives editorial? What does the schedule allow? What happens when the director changes the blocking? What does legal say? What does the crew need to know before call time?

That is why “text prompts alone are not a great way to make a movie” is a useful observation. Generated material still requires decisions about story, design, performance, and production.

Apply what you know to new tools

So what do you do when the tools used in your work are changing?

First, learn the new tool enough to understand what it changes. You do not need to become a zealot. You do need enough practical experience to know what is newly easy, what is newly cheap, what is newly risky, and what everyone is pretending it can do.

Second, preserve the craft underneath the old tool. If you came from photography, do not let camera menus be the only thing you know. Keep studying light, gesture, lens choice, composition, timing, subject, and story. If you came from editing, do not confuse the NLE with pacing, tension, clarity, and performance. If you came from production, do not confuse software with judgment about people, time, money, risk, and approvals.

Third, study why outputs work, not just how they were made. Compare versions. Keep references. Save source files. Look at failures. Ask what changed between the bad version and the good one. Comparison and critique help you explain why one version works better than another.

Fourth, learn enough production reality to know what breaks. A beautiful image can be unshootable. A gorgeous creature can be unriggable. A clever workflow can be impossible under a union agreement, a security policy, a client approval path, or a delivery spec. The person who knows the tool’s practical limits is valuable.

Fifth, use AI to remove bad friction, not to skip judgment. Let it draft, search, summarize, compare, rough out, clean up, and generate disposable options. Then add the human review layer needed to check quality, rights, safety, budget, and story.

Become the person who can evaluate the output, not just generate it.

Judge the method by the work

Familiarity with a tool can become a source of professional pride. So can being among the first to adopt its replacement. Neither establishes that the method suits a particular job.

Tippett’s experience is useful because it shows specific knowledge transferring to a new process: anatomy, weight, timing, and performance. That did not happen without learning and collaboration, and it does not guarantee that every worker will avoid displacement.

The practical question is which parts of your experience remain useful, and what you need to learn to apply them with a different tool.

The Bot Was the Least Interesting Part

Sat, 30 Aug 2025 00:00:00 GMT

Kirshbot was a character-bot experiment in measuring speech patterns and using them to evaluate generated dialogue. I wanted to find out whether explicit rules for rhythm, sentence length, and vocabulary could help identify lines that no longer sounded consistent with the character.

Generating a resemblance was only the starting point. The useful result was a profile that gave reviewers specific reasons to accept or reject a line.

What Makes a Voice Recognizable

A recognizable character voice is not just a list of catchphrases.

It is rhythm. Silence. Vocabulary. Sentence length. What the character notices. What they refuse to say. Whether they explain themselves. Whether they decorate a thought or state it directly.

That is why "write this like Character X" usually produces bad results. The model reproduces obvious traits without consistently following the character’s speech patterns.

Real production teams know this already. They use style guides, character bibles, continuity documents, script notes, brand voice systems, approvals, and a lot of human taste. Maintaining a consistent voice requires explicit guidance and review.

Kirshbot became a small, overbuilt way to test those rules and see which parts could be measured.

The experiment’s limits

This was an unofficial, non-commercial experiment using a fictional character as source material. It was not endorsed by the owners, writers, performers, studio, network, or franchise. An analytical experiment does not establish permission to publish or commercially use an imitation.

In professional use, a team would need authorization, clear labeling, and human review of the writing. A voice profile cannot settle questions of rights, likeness, contracts, or licensing.

The Core Experiment

The practical question was narrow:

Can you measure a character’s speech patterns, encode them as constraints, and use those constraints to evaluate whether generated text follows the profile?

The workflow looked like this:

Dialogue Input -> Transcription -> Speech Analysis -> Voice Profile -> Generation -> Validation

Generation is the least trustworthy step in that chain.

The system first analyzed dialogue using transcription and speech-analysis tooling. Then it built a measurable profile: pace, pause frequency, sentence length, reading level, filler rate, repeated structures, and tonal habits. Generated text only mattered if it could pass the validation checks.

What the Analysis Measured

The analysis looked for the unglamorous stuff: average speaking pace, pause patterns, sentence length, reading complexity, filler frequency, repeated structures, tonal tendencies, and common thematic territory.

Those measurements are not "the character." They are evidence.

A voice profile describes measurable tendencies and known problems, much as a production style guide does. It can specify typical sentence length, pacing, vocabulary, and phrasing to avoid.

Instead of saying, "This doesn’t sound right," you can say the sentence is too long, the diction is too ornamental, the joke breaks the tone, the thought explains itself too much, or the line sounds like a summary of the character instead of something the character would say.

Define Testable Constraints

The system worked best when it stopped asking for personality and started enforcing limits.

A broad style request might say: "Make it dry, understated, philosophical, and weirdly funny." Sometimes that works. Usually it works once, then drifts.

Specific checks made the results easier to evaluate.

For this project, generated lines were checked against a profile before they could be considered successful. If the output wandered outside the voice profile, it failed. That means a short, boring line could be more successful than a clever one.

This is also how real creative systems work. A brand voice guide does not exist to maximize cleverness. A character bible does not exist so every writer can show off. Continuity notes do not exist because everyone loves paperwork.

They help writers maintain consistency across drafts and episodes.

What the measurements cannot establish

A line can match the measured profile and still be poor writing. It may contradict continuity, miss the purpose of a scene, or imitate familiar traits without giving the character anything appropriate to say.

A reviewer still needs to assess those qualities. The same method could help with brand voice, game dialogue, or support messages, but each would need its own source material, constraints, and approval process.

A Reusable Review Process

The useful pattern is simple:

Source Material -> Voice Guide -> Measurable Constraints -> Draft Output -> Review -> Approval

AI can help extract patterns, summarize tonal rules, identify contradictions, flag off-brand drafts, compare alternate phrasings, and maintain continuity across a large pile of material.

A person still needs to assess the writing and confirm permission for its intended use.

What I’d Build Next

I would develop this into an internal tool for reviewing voice consistency.

Something a creative team could use internally to ask:

Does this line match the established voice?
Does this scene violate the character bible?
Did the tone drift between drafts?
Are these marketing blurbs still on-brand?
Which approval notes keep recurring?
Where does the system need a clearer rule?

What the Experiment Showed Me

The useful result was a set of explicit constraints. They gave the generator guidance and reviewers concrete reasons to accept or reject a line.

That does not make measured similarity the same as good writing. It gives a creative team another way to detect inconsistency before approving a draft.

How Liquid Death Makes Water Distinctive

Fri, 29 Aug 2025 00:00:00 GMT

I just finished watching Mike Cessario's MasterClass on building Liquid Death. What interested me was how its packaging, comic tone, and retail distribution work together.

The reported $1.4 billion valuation is useful mostly as context. It came from private financing coverage in March 2024, not audited public-company reality, and it is not the argument.

In a category where product differences are small, packaging and brand associations can give people a reason to choose.

The can makes the joke visible

Water is an absurdly good category for a brand person because the functional claims are almost exhausted. It hydrates you. Great. So does the thing next to it.

Cessario's insight was not that consumers secretly wanted "better water." It was that the entire category looked and behaved the same. Clear plastic bottle. Blue label. Mountains. Purity. Wellness. Whispery copy about nature. The emphasis is usually freshness and purity.

Liquid Death used a skull and a tallboy can associated with beer or energy drinks.

That contrast did a lot of work. A can that looked like beer or an energy drink created instant confusion: wait, what is that? Is that allowed? Why is someone drinking that at work? Then the reveal: it is water.

The can lets the buyer participate without explaining anything. You can carry it at a concert, in a meeting, at Target, on a set, wherever, and the packaging makes the joke recognizable without an explanation.

Packaging as Media

Liquid Death’s packaging stands out among conventional water brands.

The name, the skull, the tallboy format, the "Murder Your Thirst" posture: all of it turns the package into a miniature campaign. It is designed to be noticed on a shelf, held in a hand, photographed, misunderstood, explained, and shared.

In the MasterClass episode, Cessario says the first proof-of-demand test was a cheap commercial and small paid-media pushes before the product was broadly available. The exact production numbers are less important than the operating move: test the brand system before the business can fully exist.

Does the name stop people? Do people share the joke? Do people understand the anti-marketing joke? Do they ask where to buy it? Can this thing generate demand before there is distribution?

For operators, that is the useful part. When the object itself is funny, strange, or socially legible, every asset gets easier. The commercial, the shelf, the social post, the merch, the event placement, the celebrity stunt: they are all expressions of the same object.

The Joke Has Rules

The brand keeps the same comic tone across its packaging and campaigns.

Cessario talks about the brand like a character, closer to professional wrestling than conventional beverage marketing. Everybody knows it is theatrical. That is why it works. The audience is not being deceived; they are being invited.

This is also why the joke can reach people who do not care about metal, punk, skateboarding, or skulls. A mom at Target does not have to be a Slayer fan to understand why a can of water pretending to be dangerous is funny. The joke is not "we are hardcore." The joke is "water has been given the emotional styling of something you are not supposed to drink at 10:30 in the morning."

The brand is rebellious, but it is not incoherent. Its tone has limits. Cessario mentions using something like an SNL standard as a taste filter: sharp enough to be funny, not so reckless that Walmart pulls it off the shelf. Retail relationships affect which jokes the company can use.

You can be rude. You can be dark. You can be absurd. But if you are building a real brand with real retail partners, the team needs agreed limits for what it will publish.

Merchandise Extends the Brand

The merch is easy to dismiss as brand hype, but I think it is central.

Liquid Death shirts, skateboards, odd collaborations, novelty products, and limited drops make the brand recognizable beyond beverages. They give people more ways to declare affiliation than simply buying a can of water.

The Tony Hawk blood skateboard thing is the cleanest example. On paper, it is ridiculous: get Tony Hawk as an ambassador, put his actual blood in the ink for limited-edition skateboards, film the process, sell the boards, let the internet argue about it.

Public reports put the drop at 100 boards priced at $500 each, with The National reporting they sold out in under 20 minutes. In the MasterClass episode, Cessario also claims the campaign generated huge media attention from a relatively small production spend. That is exactly the kind of PR math you should treat carefully.

But strategically, the lesson holds even if the claimed reach is overstated.

They did not just make content about a product. They made another product that created content.

A funny video can be ignored. An unusual physical product can attract coverage of its own. People share it because the existence of the thing is the story.

Distribution Makes Purchase Possible

The can has to show up in places where the performance makes sense: concerts, convenience stores, grocery aisles, coolers, offices, gyms, sets, parties. If it only exists online, the joke is smaller. If it is only a DTC novelty, it becomes a gag gift. Distribution lets people buy the water after seeing the campaign.

The campaigns make people aware of Liquid Death; stores make it available. Retail presence gives you a chance to act on that memory when you are thirsty, bored, curious, or standing in front of a cooler trying to choose between seventeen brands of flavored bubbles.

According to Cessario, Liquid Death had to launch online because retailers resisted putting the name on shelves. Public materials from the company later said the brand had reached 113,000 retail doors across the U.S. and U.K. and $263 million in 2023 retail scanned sales. Again, these are company-reported figures. But the direction is the point. The brand did not stop at attention. It built a path from attention to purchase.

What Other Teams Can Apply

Apply the underlying decisions to the category you work in.

Category Contrast

Do not start by asking how to be a slightly better version of everyone else. Ask what the category has made illegal, embarrassing, absent, or unthinkable. Liquid Death used aggressive comic imagery in a category dominated by purity and nature.

Packaging as Media

The package is not just a container. It can attract attention in a store, a photograph, or a customer’s hand. If the package needs a campaign to explain why it is interesting, the campaign is doing too much work.

Consistent tone

Agree on the limits of the comic tone before approving scripts, edits, and partnerships. A joke that makes the team laugh may still be unsuitable for a retail partner.

Merchandise

The question is not "what swag can we sell?" It is "what other products would customers enjoy from this brand?" A shirt, skateboard, novelty product, or absurd collaboration can prove the brand has a culture around it, not just a SKU list.

Choosing Where to Sell

Put the object where the joke gets better. Liquid Death makes obvious sense at a concert. It is also funny in a boardroom because it looks wrong there. Where people encounter the product affects how they interpret it.

The Useful Distinction

Liquid Death questioned familiar conventions in water marketing: natural imagery, restrained packaging, and an earnest emphasis on health. It replaced them with a recognizable comic tone and maintained that tone across packaging, campaigns, and merchandise.

Retail distribution then gave interested people a way to buy it. Other teams can learn from how those decisions support one another without copying the skulls or the stunts.

Photography Workflow 2025

Mon, 10 Feb 2025 00:00:00 GMT

A couple of weeks ago, I stumbled across Photomator and realized that Pixelmator had recently been acquired by Apple.

That was enough to get my attention. The first photo editor I ever used seriously was Aperture, and I have been waiting for Apple to remember that it once made a genuinely good pro photo app. Photomator looked like the thing I had wanted for years: a modern, Apple-native Lightroom replacement that did not require a separate library just to edit RAW files.

The app switch was the easy part. The real work was confronting the library I wanted it to use.

Leaving Adobe Behind

Earlier in 2024, I abandoned Photoshop for a one-time purchase of Affinity Designer. That left Adobe reduced to Lightroom with 1TB of cloud storage: a joyless subscription for one app and one storage service.

I had been passively looking for a Lightroom replacement for a while. There were plenty of worthy competitors, but most either did not have cloud storage or did not have a reasonable cloud storage option for an active catalog. I did not want to solve the editing problem by creating another storage problem. I already had enough of those, thanks.

Photomator seemed different because it relied on Apple Photos and my existing iCloud storage plan. It was not asking me to move the archive into yet another proprietary cloud. It was asking whether Apple Photos could provide access to the active library, with Photomator handling the serious edits.

Why Photomator Felt Different

I signed up for the free trial, which was 7 days before converting to $29.99/yr. I expected to bounce off it quickly. Instead, I found a powerful photo editor with a much more modern feel than Lightroom, and with some UI lifted directly from Apple Photos in a way that felt practical rather than lazy.

There was also the obvious Apple question. Would they turn Photomator into the new Aperture, or would they pull a Dark Sky and slowly dissolve the good parts into a simpler built-in app? I came across this video by Joseph Slinker, and his argument put me somewhat at ease: Photomator could become the "Pro" version of Apple Photos, the way Logic sits above GarageBand and Final Cut Pro sits above iMovie.

Who knows. Apple is Apple. But deep down, yes, I still want Photomator to be Apple's new Aperture.

The iCloud Problem

Then I looked at my iCloud storage.

My family plan is 2TB, shared across the family, though I am clearly the problem user in this arrangement. It was nearly full, which meant I needed to free space before adopting this workflow.

The next morning, I identified the larger problem: Apple Photos held over 90,000 photos and thousands of videos.

The question was not whether Photomator could replace Lightroom. The question was whether I needed immediate access to all of this.

That question changed the project. I had spent years treating the archive as something that should always be nearby. But "nearby" had gradually turned into "bloated, expensive, and annoying to maintain." There is a difference between owning an archive and carrying all of it in your active library forever.

What if I took the same approach I had taken to my Lightroom catalog years before and culled 90,000+ images and videos down to just the best? Daunting, yes. Also useful. Choosing what to keep also meant reconsidering which photographs I liked.

Backing Up Before Deleting

The selection process was not the hardest part. The backup was.

Before I deleted anything, I created a series of smart filters in Apple Photos that let me work through large sets by camera type, which often aligned with chronological periods in my life: iPhone eras, Sony eras, older camera eras. It was not perfect, but it gave the archive enough structure to become workable.

For larger sets, usually 6k–10k assets at a time, Apple Photos would take hours to export everything. Often it would finish with a short list of "errors" for files it could not export. Those had to be handled one by one: find the filename, manually export the asset, confirm it landed.

That manual recovery took time, but I needed to confirm the exports before deleting anything.

The destination was the same place I use for RAW storage: my Synology NAS, which syncs nightly to Google Cloud Platform's Archive-class Cloud Storage. By the end, I had backed up around 1TB of images and videos, adding less than $2/month to my storage bill.

That gave me a backup of the full library without requiring me to keep it all in active cloud storage.

Culling the Active Library

Once the backups were done, I went back through the smart filters and started choosing keepers.

The process was simple: add a ❤️ to anything that belonged in the active library, let the smart filter remove that keeper from the current view, then delete what remained. Look closely, decide, move on. No elaborate taxonomy. No pretending every burst frame was sacred because storage had been cheap once.

By the end, I had removed almost 80,000 images and videos from Apple Photos and freed up almost 1TB of iCloud space. That included the roughly 6,100 RAW files I had brought over from Lightroom.

This mattered more than the app switch. It gave the system headroom, made Apple Photos feel usable again, and gave iCloud more "extra room" than I had on the 1TB Adobe plan. The smaller active library was easier to browse and use.

Everything still exists on the NAS and in Archive-class cloud storage. But I no longer need immediate access to all of it on my phone.

The Edit-History Problem

In the middle of all this, I hit the problem that almost stopped the move entirely: Photomator could not read Lightroom sidecar files. Every edit I had made on more than 6,000 photos would effectively be lost.

I wrote to Photomator support about it, and they replied quickly. Their explanation was straightforward: editing sidecar data is app-specific. Different photo editors may all be manipulating exposure, contrast, color, masks, and geometry, but they do it with different engines and assumptions. The data is not interchangeable just because the words in the interface are similar.

I do not know why I had not fully internalized this before. Of course Lightroom edits would not magically become Photomator edits. I could move the RAW file, but Photomator could not reproduce the Lightroom edit instructions.

Culling 90,000 photos felt daunting but possible. Re-editing 6,000 photos felt absurd. After sleeping on it, I saw a reason to reconsider the edits.

It was also an opportunity.

Some of those edits were years old. My taste has changed. My eye has changed. The tools have changed. I wanted to see how I would edit those images now rather than retain every earlier choice.

Where the Workflow Landed

So I canceled Adobe. I paid a cancellation fee, which I was not happy about, and which only further convinced me that Adobe had lost its way for the kind of use I actually had left for it.

At this point in 2025, I am using Photomator 100%. It costs roughly one-quarter of the Adobe plan, uses the iCloud storage already underneath my family setup, and lets me maintain an active library of "just the best" instead of pretending that every file deserves immediate access forever.

The workflow is now straightforward. Sony RAWs and iPhone RAWs follow the same path: back up to the NAS and GCP, cull in Apple Photos, keep the active library small, edit the selected images in Photomator.

It reminds me a little of what film did for me. Film slowed me down because every frame had a cost. This is not the same thing, obviously. Digital still lets me make too many pictures. But the workflow creates a useful checkpoint after the fact. It makes me look again and decide what is worth keeping close.

The next step is to develop a series of LUTs for Photomator while I continue to covet the Leica Q3 43, even though the workflow changes have been more immediately useful than another camera.

Moving to Photomator reduced my subscription costs and prompted me to clean up the library. As earlier posts show, my preferences and requirements will probably change again.

(Previously: Photo Workflow 2022)

Why MiniDisc Still Feels Like the Future

Sun, 01 Jan 2023 00:00:00 GMT

My Sony MZ-E75 was part of my everyday carry back in 2000/2001. Then sometime in 2002/2003, I packed light and headed for LA, and my MiniDisc decks landed in a box in the closet of my parents' house in Boston.

Fast-forward to a few months ago. My mom sold the house and told me to come get any of my stuff that I did not want to end up in a dumpster.

I pulled out this deck, hit the eject button a few times, and immediately felt the old little thrill of it. Not nostalgia exactly, or at least not only nostalgia. More like recognition. The mechanism still felt good. The size still made sense. I still liked its compact design and the feel of its controls.

I was not ready to part with it. Into the box it went, and a few weeks later I had all my MiniDisc gear back at my home in Los Angeles.

At first I thought I was bringing home old gear for the memories. Maybe I would listen to some tracks I recorded 20 years ago, smile at whatever past version of me thought belonged on a disc, and put everything back on a shelf.

What surprised me was how modern the format still felt. MiniDisc sits in a strange middle place: physical enough to slow you down, digital enough to edit, small enough to carry, and different enough from streaming to make me reconsider how I like to listen.

The point is not that MiniDisc sounds better than everything else. I am not trying to win an argument on a message board. The point is that the whole interaction makes music feel chosen again.

MiniDisc has the best kind of format contradiction. It is physical media that behaves like digital media. You hold it, label it, eject it, stack it, and lose it in a drawer. But you can also edit it, rewrite it, sequence it, and move files onto it from a computer. It gives you a finite object while keeping the ability to change its contents.

That is why it still feels useful after streaming won. Streaming made music infinite and frictionless, which is extraordinary and something I use constantly. But with a large streaming library, I often keep looking for something else. Preparing a MiniDisc gives me a smaller selection to listen to. One album. One mix. One little square of plastic that has to be prepared before it can be carried.

This is the same useful friction I wrote about later in Friction Is About to Become a Luxury: not friction that wastes time, but friction that makes the choice feel deliberate.

Once the gear was back in my apartment, all it took was some time off from work between Christmas and New Year's for me to spend hours exploring the format again. I found the current r/minidisc community, the MiniDisc wiki, and a whole group of people who had kept the format usable.

That mattered because my old gear was pre-NetMD. It could play and record, but it could not easily move music from a modern computer over USB. So I found a Sony MZ-N510, one of the NetMD-era players that could receive music from my computer.

I always wanted to enjoy collecting and listening to vinyl. I liked the idea of putting on an album and letting the format enforce attention. But vinyl never quite became my thing. MiniDisc did, because I can prepare an album and take it with me.

On a rainy afternoon right before Christmas, the NetMD deck came in the mail. I stayed up too late for the next few nights converting blank media into a stack of full-length albums. Community-developed tools let me transfer music from a modern computer without setting up an old Windows XP machine.

I started with, and still prefer, Platinum MD, although it can be a bit buggy. Web MiniDisc Pro seems more solid in actual use, though part of me still wishes it were packaged as a native app. Either way, the important thing is that I can still transfer music to the old Sony hardware. A discontinued format now has web tools, desktop apps, documentation, repair notes, transfer guides, and people still making it work because the object is worth the trouble.

After the transfers, I had a stack of MiniDiscs. The next step was labeling them, which sounds cosmetic until you do it. A printed label makes each disc easy to identify.

The subreddit and wiki are full of resources for every part of this, including some very nicely designed labels. Using u/mikojank's template as the base, I made a slightly modified version for printing on 8.5" x 11" sheets.

For best results, print at 100% on sticky 8.5" x 11" high glossy photo paper and cut the labels with an X-Acto knife and a ruler. Scissors will be tough at this size, trust me.

Designing, printing, and applying a label gives each disc a recognizable cover. You can recognize it from across the table. You can hand it to someone. You can put it in a case. You can build a tiny library that does not require an account, a subscription, a recommendation engine, or a search field.

After an initial run of 27 discs, my original supply was exhausted. With just a bit of scouring and some late-night internal debate, I created an account on amazon.co.jp and ordered the last two 10-packs of the Sony color multipacks I could find. I also grabbed one very fun 30th anniversary disc from NeedleJuice Records.

Those might be the last MiniDiscs I ever buy. Maybe that is part of the appeal. Knowing the supply of machines and blank media is limited makes me consider what I want to record on each disc.

Streaming won because it is easier, cheaper, and functionally infinite. I am not trying to live in 2001.

I still enjoy choosing, sequencing, labeling, and carrying a disc. Preparing the music is part of the pleasure, and I’m glad the equipment and community let me keep doing it.

Photography Workflow 2022

Wed, 12 Jan 2022 00:00:00 GMT

"Simplify, simplify." -Henry David Thoreau

I keep writing these photo workflow posts because the workflow keeps changing. Not in a dramatic, new-year-new-me way, but in the useful way where small annoyances pile up until the obvious answer is to simplify again.

In 2018, the problem was getting my arms around Google, Adobe, SmugMug, RAW files, exports, and too many copies in too many places.

In 2020, the big realization was that culling had to happen before the archive got bigger. Keeping everything felt safe, but it also made the library slower, more expensive, and less useful.

By early 2022, the shape of the problem had changed again. I was less interested in finding the perfect editor and more interested in reducing how many systems I had to trust at once. The goal was fewer services, better backups, and a photo library I could actually live with.

Current Process

The process is still familiar: import, back up, cull, edit, export. The main changes are the services I use to store and back up the files.

Transfer images from the camera
- JPGs go to a /JPG/ folder on the Desktop.
- RAWs go to a dated folder, like /2022-01-12/.
Back up and import
- JPGs go to Apple Photos, organized with one album per camera type.
- RAWs go to Lightroom CC and the NAS.
- The NAS automatically backs up to Backblaze B2 Cloud Storage.
Cull
- I review everything and delete what I know I am not going to edit.
Edit
- Lightroom is still the main editor.
- Presets still help, mostly by getting me to a familiar starting point faster.
Export
- Instagram matters less to me than it used to.
- Exports are more likely to end up in my own archive, printed, or, because this is a 2022 snapshot, occasionally pointed at NFT-related experiments.

The important change is that each part has a clearer job. Apple Photos is where the everyday JPG library lives. Lightroom is where active RAW editing happens. The NAS is where the durable copy lives. Backblaze keeps the offsite backup.

Culling Became the Real Workflow

The best change from 2020 was not a piece of software. It was keeping the cull going.

Flashback to 2020:

Not only does this bit of digital housekeeping feel great now that it's done, it will also save $180/yr with the downgraded storage plan - which, up until today, I had been paying to store raw files for photos I was ultimately never going to edit.
— Bill Anastas (@banastas)

I am really happy I have been able to keep this up for the last two years. Culling is now kinda fun to me, almost like a game where the challenge is to pare down a day's worth of images to the essential shots.

Bi-yearly update! Keeping the culling going, I added just around 1 pic/day on average over the last 2 years.
— Bill Anastas (@banastas)

That sounds more virtuous than it feels. Mostly it is practical. Fewer mediocre frames make the good ones easier to find. A smaller active catalog costs less. The next round of editing starts from a better pile.

It also trains taste in a way that buying a new app does not. The question is still brutally simple: am I ever going to do anything with this shot? If the answer is no, keeping it around does not make me more serious about photography. It just increases my storage bill.

Adobe, naturally, found a way to re-enter the conversation. They raised my plan price to $19.99/month. I am still using Lightroom, but the price increase made me pay closer attention to Luminar, ON1 Photo RAW, and Capture One Pro. None had exactly what I wanted yet, but platform dependency has a way of becoming visible right after the bill changes.

Reviewing Photos on the iPad

The biggest hardware change is the iPad 12.9 M1.

I love, love, love that screen. It is not the most sober purchasing justification in the world, but for batch photo review it matters. A nice, portable, high-quality display lets me review in smaller sessions, with less friction, which means I am more likely to actually do it.

I have also used the iPad quite a few times to transfer images directly from the camera into Lightroom. It works, and it is genuinely useful in the right context, but file management on iPadOS is still awkward enough that I do not want to pretend it is magic. Better than it used to be, yes. Still weird, absolutely.

I splurged on 2TB of iPad storage and have not come close to filling it. That probably says as much about the culling as it does about the iPad.

The Platform Shift

The biggest software change was moving away from GSuite.

This started with a massive Google Photos to Apple Photos migration, which sounds tidy until 127,147 photos and 2,775 videos are making their way into iCloud and you wonder if you have made a terrible life choice.

Once that was done, I canceled GSuite along with many, many TBs of cloud storage. That also kicked loose a bunch of adjacent digital housekeeping: moving old files to iCloud Drive, cleaning up lingering storage piles, and shifting my email to iCloud now that custom email domains exist.

This was not really about preferring one giant tech company to another giant tech company. I am not naive enough to turn Apple Photos into a personality. The point was reducing sprawl. Google Photos, Google Drive, GSuite, Lightroom, the NAS, random export destinations, and old archives all had slightly different versions of the truth.

Apple Photos and iCloud are not perfect, but in 2022 they gave me one place to browse and manage the everyday library. The RAW archive still lives outside that system. That split feels healthier to me: convenient access where convenience helps, owned storage and backups where durability matters.

Backups and Cost

The NAS now syncs to Backblaze B2 Cloud Storage.

At the moment, I have 3,217.6GB stored there. That is costing around $0.53/day, or $16.12/month.

There is probably a cheaper arrangement somewhere if I wanted to turn this into a spreadsheet hobby. I do not. For that price, the RAW archive has an offsite copy that is not tied to my Lightroom subscription, not sitting only in the house, and not dependent on me rotating drives like it is 2009.

That is worth the money.

The recurring theme here is that the archive should be easier to maintain, not just larger. More storage does not make an unorganized library easier to search. The real cost is the time spent trying to find something and the low-level distrust that comes from not knowing which copy matters.

Simplify Again

So this is where the workflow stood in early 2022: simpler than 2020, less Google-shaped, still using Adobe, more centered around Apple Photos for everyday access, and backed by a NAS plus Backblaze for the files I actually care about preserving.

It is not final truth. None of these posts are. They are versioned snapshots of what worked at a given moment, with all the compromises still visible.

The pattern is the useful part: simplify, use the system for a while, notice what still hurts, and simplify again.

(Previously: Photo Workflow 2020)

Making the iPhone Boring on Purpose

Thu, 23 Sep 2021 00:00:00 GMT

After I updated to iOS 15 on Monday, I decided to make my iPhone boring on purpose.

I kept unlocking the phone without having anything I needed to do.

I noticed that I spent a lot of screen time in any given day staring at my apps and folders the same way you stand in front of the fridge, just looking for something to open. Not hungry. Not looking for anything specific.

There are experts who have described versions of this more thoroughly than I can, but the practical thing I realized was simple: there are a limited number of apps I actually use regularly, and a much larger number I would open without a purpose.

That is a bad default for an object I carry everywhere.

What changed

The experiment was very small. I removed everything from the iPhone home screen except four apps in the bottom bar:

Messages
Safari
Where/When
Camera

Everything else moved off the home screen. If I wanted another app, iOS could surface it through Siri Suggestions, Recently Added, the App Library categories, or search. I could still get to anything. I wanted to stop opening apps just because I saw their icons.

Four is not magic. It was just the number that matched the reasons I most often picked up the phone on purpose.

Why these four

Messages stayed because it is direct communication. Usually, if I open Messages, there is a person on the other end and some actual reason to be there. That does not make it noble. It just makes it less ambiguous than a folder full of little rectangles waiting to be checked.

Safari stayed because the web is still the general-purpose tool. It can absolutely become a distraction, but opening Safari feels different from tapping a row of apps that each already knows exactly which loop it wants to put me in. Safari asks me to start with an intention, even if the intention is only "look this thing up before I forget."

Where/When stayed because it was one of the few apps on my phone that represented a specific personal job. I built it so I could keep a small, private location memory in my own calendar instead of depending on a platform that had drifted away from the thing I needed. Opening it means I am checking in somewhere because I may want to remember that place later. It is not a feed. It is closer to a tiny piece of personal infrastructure.

Camera stayed because it points away from the phone. That sounds slightly precious, but it is true. The Camera app is for capturing something in the world, not for consuming whatever the phone has prepared for me. It is one of the few default apps that usually makes the phone disappear after doing its job.

That was the whole bar: communication, web, memory, camera. Specific reasons to pick up the device.

Why it worked

Having to search for another app gave me a moment to decide whether I needed it.

I did not delete my distracting apps. I did not pretend Siri Suggestions would always guess perfectly. I did not become a better or more focused person because I moved icons around, which would be a deeply embarrassing thing to claim in public.

What changed was the first move after unlocking the phone. The home screen stopped advertising quite so much. If I had a task, I could do it. If I did not have a task, there was less there to convert vague restlessness into 20 minutes switching between apps.

This is the part that improved my relationship with the phone. General lingering was reduced because the phone had fewer visible invitations. Once the task was finished, it became easier to put the phone down until the next direct reason presented itself.

With fewer apps visible, I spent less time looking for something else to open.

The iPad version

Although I have 14 apps in the iPad dock instead of 4, this effect is even nicer when animated:

The iPad is a different object for me, so the same rule did not need to apply with the same severity. That is probably the better lesson anyway. What mattered was which apps I saw first, rather than the exact number.

Looking back from now, I do not think the exact four-app setup is the important artifact. Maybe it changes. Maybe a different season of life deserves a different bottom bar. The part that survives is the idea that the first screen should answer a question before it creates one: why did I pick this up?

Sometimes the best interface is the one with less to offer.

Owning My Location History With Where/When

Mon, 24 May 2021 00:00:00 GMT

For more than ten years, I used Foursquare to record where I had been. When it stopped sending check-ins to my calendar, I wanted a searchable record that did not depend on the service.

Why I kept checking in

I signed up for Foursquare on June 2, 2009. I was (super)user number 14,488, which is exactly the sort of early-user trivia that only matters if you were there and if you have a personality defect about preserving old account metadata.

For ten years I checked in almost everywhere I went. Different jobs, long days, weird little lunches, trips, the big life markers, the places I would have forgotten entirely if I did not have a record of where I had been. My wedding in Palm Springs is in there. My son's birth at Sint Lucas Andreas in Amsterdam is in there. A lot of ordinary Tuesdays are in there too, which may be the more useful part.

That history became valuable in a way the gamification never did. I mostly did not care about badges, points, or mayorships. They were fun enough in the early Foursquare era, but they were not the reason the habit stuck. The habit stuck because a place and a timestamp are often enough to bring back the rest of a day.

This was never about tracking other people. I kept the log to help me remember. I do not have a perfect autobiographical memory, and I do not particularly want to pretend that I do. But if I can see that I was in a specific neighborhood, restaurant, office, airport, hospital, or hotel on a specific date, my brain usually does the rest.

That is what made the calendar connection so important. Foursquare was not just a social app to me. It was feeding a personal log that I could search later.

When the platform stopped serving the habit

Over the last couple of years, the official Foursquare app turned off the feeds and my calendar connection went down. They had warned that this day would come, but the feeds kept working long enough that I came to rely on them.

I put a quick fix into place. I downloaded my Foursquare user data, parsed it into a structure I could import into my calendar, and got the historical record back where I wanted it. For new check-ins, an IFTTT applet could still send Foursquare activity to the same calendar, but it was unreliable enough that I never trusted it as the permanent answer.

All of that made the whole system feel temporary. I had more than a decade of location history, but the thing maintaining the habit had become fragile. Worse, Foursquare itself was becoming a different kind of company. The consumer check-in app that had created the habit was fading, while the value of the accumulated location data moved elsewhere.

I still needed the record after Foursquare stopped supporting the way I used it.

Building the smallest tool that solved it

I have been slowly learning iOS development for years. Really slowly. I started with Objective-C, then Swift, and most recently SwiftUI. Every year or so I pick up where I left off, learn something new, push it a little further, get frustrated, and put it down again.

This last weekend, I finally knew enough to build the small thing I had wanted all along.

Where/When is not a sophisticated app, and I am not pretending to be an iOS expert. But that was also part of the point. I had lived with the problem long enough to know exactly what the tool needed to do, and almost everything else could be left out.

The app did not need a feed. It did not need followers. It did not need recommendations or a discovery layer or a public profile. It needed to let me say, "I am here, and I may want to remember that later," then put that record somewhere I already trust and already search.

That place, for me, is my calendar.

What Where/When does

Where/When is deliberately small. When I am somewhere I want to remember, I can manually check in and save a title, a note, the raw GPS coordinates, and an Apple Maps link directly to my calendar. That is basically it.

No feed. No leaderboard. No social graph. No company trying to guess where I go next.

Because the first version does not send anything to my own server or maintain its own giant place database, it labels each check-in with the best location information available: a place name if it can find one, otherwise an address, otherwise raw coordinates. That makes it less polished than a dedicated location platform, but it also keeps the app simple and private by design.

No data is shared with anyone or anything. The app writes the record to the calendar I choose, and the data stays mine.

The result is a location diary I can search in my calendar. If I want to know where I was on a day, I look at my calendar. If I want to remember why that day mattered, the place is often enough to get me there.

Why owning this record matters

This is the same reason I keep coming back to small owned tools. The point is not to rebuild every platform by hand. The point is to know which parts of your life are important enough that you do not want them trapped inside someone else's product roadmap.

Location history is sensitive. It should be treated that way. But it is also useful, especially when it is a record I keep about myself for my own memory. A decade of check-ins taught me that the data mattered. Foursquare taught me the habit. Platform decay taught me that I needed another place for the record to live.

Where/When was my attempt to keep that habit alive privately, locally, and on my own terms.

I put Where/When on the App Store, and I put it on Product Hunt at the time, mostly because shipping anything at all felt like a milestone. The app was small, but the problem was not. I wanted a way to remember where I had been without handing the whole system back to a platform that had stopped serving the job.

Tracking Planes From My Window With PiAware

Mon, 19 Apr 2021 00:00:00 GMT

With a Raspberry Pi, a USB ADS-B receiver, and a small antenna in my window, I could see aircraft broadcasting over Los Angeles. I had not known I could build a receiver myself. The appeal was immediate: inexpensive hardware, public radio signals, and a local map showing what it received.

What PiAware is

PiAware is FlightAware's software for running an ADS-B receiver on a Raspberry Pi and feeding the data back into FlightAware's network.

ADS-B is the system many aircraft use to broadcast their position, altitude, speed, and identity. Sites like FlightAware aggregate those signals from receivers all over the world. PiAware lets you run one of those receivers yourself, contribute to the larger network, and still see the local data directly on your own device.

FlightAware also gives feeder accounts extra access as a thank-you for contributing data, which is a nice bonus. I was mainly interested in receiving the signals myself.

My setup

I purposely installed PiAware on a Raspberry Pi Zero W so that I could eventually mount it on the roof without introducing a lightning path back into my home network. So far, though, keeping the antenna in a window has worked better than I expected.

Eventually I may install the antenna on the roof. For now, the window setup is good enough that I have not felt much urgency to change it.

What I could see from Los Angeles

The surprising part was range. I am about 21.9 miles from LAX, and even with the antenna sitting in a window, I can often see transponder signals from aircraft on the ground at the terminals.

From my desk, I could follow departures, arrivals, and ground traffic, with aircraft IDs, altitude, and range.

On most days, this little setup averages a 100+/s ADS-B message rate, which seems pretty good for a receiver that is still just sitting inside by the glass.

The parts

This was my parts list at the time. Check FlightAware's current PiAware setup notes and hardware recommendations before buying anything, because Raspberry Pi availability, receiver options, filters, and antenna setups change.

I have not done an A/B test with or without the filter. I originally bought it because I was in a congested RF area, and it seemed worth having in the chain.

FlightAware has the official PiAware install instructions, so I would start there rather than trust an old parts list from 2021 as a buying guide.

Why this kind of project sticks

Opening the map lets me see nearby aircraft and their reported positions.

I can identify aircraft passing overhead and follow their routes from my window.

Influences: Julia Child

Tue, 03 Nov 2020 00:00:00 GMT

Photo: Schlesinger Library, Radcliffe Institute, Harvard University

When I was little, the TV was always set to PBS for Sesame Street. I cannot recall a single specific Sesame Street episode, but I do remember what happened after it ended. The French Chef would come on, and suddenly the bright, soft world of kids' television gave way to this very tall, very funny, very confident adult moving around a kitchen like she owned every inch of it.

I was mesmerized.

Every knife cut, every shift of the pan, every reach for a bowl or bottle had this wonderful combination of skill and looseness. She clearly knew exactly what she was doing, but it never felt stiff or precious. It felt alive. Even when something looked improvised, it never felt like panic. It felt like the dish had simply taken a turn and she knew how to follow it.

That may be the thing I responded to first, even before I had any idea what she was actually making. She made cooking look like authority without anxiety. No matter what happened, because of the confidence and control she brought to the room, it seemed as if it was meant to be that way.

Not until I was older did I realize that one of my favorite parts of watching her cook was the wine. When a dish called for red wine, Julia would add some to the pot and then fill a waiting glass for herself. That detail explains a lot of what I love about her. The technique mattered. The food mattered. But the pleasure mattered too. She could be serious about cooking without turning it into a joyless little test of discipline.

She showed me the joy of cooking, especially cooking for other people. Not the performance of being impressive, not the fantasy of getting everything exactly right, but the warmer and more useful thing: making something, feeding someone, adjusting as you go, and refusing to be ruined by the parts that do not behave.

"The only real stumbling block is fear of failure. In cooking you've got to have a what-the-hell attitude."

I was determined to learn how to cook, but the real goal was to learn how to do it fearlessly, the way Julia seemed to. I wanted that looseness. I wanted the ability to recover. I wanted to be the kind of person who could put food on the table without making everyone else nervous about whether I was succeeding.

Julia Child's Kitchen on display at the National Museum of American History

"You don't have to cook fancy or complicated masterpieces - just good food from fresh ingredients."

At some point, four-year-old me was probably watching more The French Chef than Sesame Street. When adults asked what I wanted to be when I grew up, I had an answer ready: a chef.

Looking up her quotes later, I found one about her first meal in France that made me love her even more:

"The whole experience was an opening up of the soul and spirit for me...I was hooked, and for life, as it turned out."

I still enjoy that combination: caring about the food, enjoying the company, and recovering when something goes wrong.

The VFX Industry: Part 2

Mon, 26 Oct 2020 00:00:00 GMT

This is part of a three-part series on the VFX industry: how the work changed, why the business became harder, and how those pressures affected artists.

Series: Part 1 · Part 2 · Part 3

This second part is about getting to Imageworks, helping crew shows, watching reels, and seeing what an Oscar does to a building full of people already thinking about the next deadline. For anyone outside VFX, the useful part is how much of the business depends on finding people who can survive real production, not just people who can make a good-looking sample.

When one door closes

For a number of reasons, I decided to go back to Boston at the end of my internship at Centropolis FX to save up some money for a move back to LA the following summer.

During this period of time, Centropolis' parent company, Das Werk, went bankrupt. Shortly thereafter, Warner Bros. pulled all shots from the Matrix sequels in an effort to protect the release date, and Centropolis FX closed its doors.

Warner Bros. re-awarded the Matrix shots to Imageworks, and my then future boss immediately hired the head of recruiting from Centropolis to bring over as much shot-ready talent as they could for the new work.

I again crossed over the California border in June 2003 and settled in to look for work. Everyone I knew from the internship program had all said the same thing, this town has a way of testing your resolve -- many of them had only found jobs once they were nearly out of savings and getting ready to pack it all in. My experience was no different; when things weren't looking so good, I landed a job as a runner at Weller/Grossman productions in North Hollywood. I worked there for a few months before getting a call from the same person that had called me to come in to interview at Centropolis, except now she was a lead recruiter at Imageworks. Their recruiting coordinator was being promoted and they would need a new one; which I soon became!

As an aside, this was technically not my first interview with Imageworks. A couple weeks after coming back to LA, I applied for a job as a PST (Production Services Technician) and went in to talk with that team. PSTs are assigned to each show and cover variable shifts 24 hours a day - their main focus is keeping the render queue moving and fixing any issues preventing that when they arise. This position required extensive scripting knowledge of which I had little to none of at the time. As one might assume, I bombed this interview and felt horrible for wasting everyone's time. Years later as an employee, I stumbled upon the notes of my interviewers during this cringefest (thanks for believing in me, Nathan!).

So many reels

When I started at Imageworks, they were just about to go full-force into production on 2 simultaneous shows; The Polar Express and Spider-Man 2.

I immediately started setting up interviews for open positions on each show; this involved scheduling a few interviews with each of the show teams every day as well as running reel reviews.

I would get 20-30 packages delivered to my desk every day - these were all reels from people that wanted to work in VFX. The vast majority were recent grads with no experience (see PST interview above). After sorting through all of these for a few weeks, and also seeing the CG supervisor's reactions to bad reels, I started to get a sense of how well someone would do just by the way the package looked.

If I received a highly-polished package with VHS and VHS case both labeled and wrapped in an elaborate design, accompanied by a shiny folder with resume and supporting pages all on heavily-textured paper, and almost always with a business card where the person submitting names themselves as "CEO of 'My Name' Studios" - I knew for sure this was going to be a hard pass.

On the opposite side of the spectrum, I once got a package from Richard Edlund that contained an unlabeled VHS tape with a wrinkled resume wrapped around it with a rubber band. When I got that, I literally ran to a tape deck to see what he was showing on the reel.

We later spearheaded a program that would bring us as a facility closer to schools with the hope that we could influence the curriculum in a way that would get us production-ready candidates soon after graduation. We also used this opportunity to ask them politely to discontinue their "reel prep" courses - they were most assuredly not helping chances at all.

The Interview/Reel Review Room

Reel review was always a fun time; the busiest, most-talented people in the building would always show up on time (for the most part) so that they would get a chance to crew their show with needed talent. We would always separate these by department; CG supervisors would look at color, lighting, FX, roto, and matchmove, animation supervisors would only look at animators, and our Character TD leads and engineering leads would mostly focus on resumes alone.

The CG Supes would turn the sound off on the TV and give every reel about 3 seconds to impress. One out of 30 reels would get some kind of facial reaction from them and something like one out of a hundred would come in to interview.

It was always incredibly important to be specific about what you worked on in the shots that you were showing in your reel. This is especially true as, even more so at the time, this was a very small industry and fakers were sniffed out quickly (again see PST interview above).

I remember one interview where a candidate told the interviewer they had done FX for a shot on the reel they had just watched. This statement caused the interviewer to pause and ask again (their last chance to come out clean). The candidate doubled-down and before they could finish repeating what they had said previously, the interviewer was already on the phone asking someone to come down to the room.

It had turned out that, for this film in particular, we had hired the entire FX team after that show had wrapped. The person being interviewed had just lied about what they had been responsible for on the shots and the CG Supe had caught them red-handed.

The FX lead came down to the interview room and the CG Supe instantly shot up and said, "This is the FX lead from the film you just showed us - do you want to tell us anything different than what you just said?" The candidate finally admitted to what had been done and they were soon out the door.

The day I met Oscar

I never worked on a single shot, but with the small part that I did play inside of the massive effort and hard work I saw our team put into it, I was so proud of our shop for winning an Oscar.

"I love my job." -John Dykstra

When I went into work that day, two full pallets of champagne were being dropped off on the loading dock.

Then the party started and, front and center, were John Dykstra and Scott Stokdyk with their brand new Oscar statues taking pictures with the team in front of a large Spider-Man 2 poster.

They stayed in that spot until the entire company had taken pictures with them - something I will never forget, and not just because of this:

John Dykstra, Bill Anastas!?, Scott Stokdyk

A week or two later, I was dropping off a resume and interview schedule for Scott. When I walked into his office to leave it on his desk I saw his car keys, a half-eaten sandwich, and an Oscar.

I knew this meant we were already focused on the next project, but I had no idea how many of those would be coming thanks to that golden statue.

Producer takeaway

A reel is not a packaging contest. The useful question is always: what did this person actually do, and can they do it under production pressure with a team depending on them?

The VFX Industry series

Lessons Learned From Dunkin' Donuts

Tue, 13 Oct 2020 00:00:00 GMT

From 1996 to 2001, I worked at Dunkin' Donuts in Quincy, Massachusetts.

I started with the 6 a.m. to noon shift on weekends in high school, then picked up more hours over summers and breaks through my senior year of college. I worked every shift the store had, from opening at 4 a.m. to closing at 11 p.m.

It was not a cute first-job story. It was five years behind a counter in a town where everyone eventually came through the drive-through: commuters, cops, politicians, regulars, addicts, rich assholes, broke assholes, kind people, desperate people, and people who covered several categories before breakfast.

I threw more trash in this dumpster than any other since

I learned how to pour coffee and work a register, sure. Mostly, I learned how unofficial systems work: who gets charged, who doesn't, which rules matter, which rules are theater.

1. Avoid Filled Donuts at All Costs

When I first started, before anyone trusted me near a drive-through headset, I used to make the donuts. Technically, I finished the donuts. We had no oven on premises. One baking store supplied five or six satellite stores with unfinished donuts.

The guy who made those deliveries was super nice and always on time. I found out later that he had been caught skimming from the registers a couple years earlier. From what I understood, the owners let him work off the debt instead of involving the law, provided he did the job well. He did.

I liked finishing donuts because it gave me an hour away from the rush to focus on my frosting skills, which were not nothing.

There was one warning a coworker gave me that I came to believe completely: never, ever eat a filled donut. There is nothing metaphysically wrong with one. The problem, at least in our store at that time, was the preparation.

The fillings came in large, funnel-like plastic cartridges that fit into a machine. To make jelly donuts, you pulled the cartridge off the shelf, set it into the machine, stabbed the donut shells with the spigots, and clicked the fill lever.

The cartridges were pretty well sealed. The spigots were not. They were supposed to be washed after each use, but they also held as much filling as they were long, which meant following the guideline would waste a ton of filling. In practice, they sat on the shelf with spigots attached and exposed filling oozing out between uses.

We only finished donuts once a day. That meant sugary filling could sit in open air before being fired into the next day's donuts. I am not making a universal claim about Dunkin' policy or food science. I am saying what I saw.

In college, I had a professor miss class for a few days. When he came back, he said he had food poisoning. The culprit, he believed, was a jelly donut he had waited a day to eat. I said nothing, but felt like I knew.

Aside #1: The Customer Can Still Hear You

When we were short-staffed and a rush hit, the manager would come out of the office and work the counter. The space between the register and the donut case was only a couple feet wide, but he seemed to believe that as soon as he turned his back, the customer could no longer hear him. Either that, or he did not care.

This happened more than once:

Facing the customer:

"Hi, how are you today? What can I get for you?"
Facing the donut case:

"I'll get you a donut you fat fucking cunt - I hope you choke on this and die you dumb shit."
Facing the customer:

"Would you like anything else today? How about a fresh-brewed iced coffee?"

The amazing thing is that I never saw him get caught. Maybe he had calibrated the exact volume required to be monstrous in private while appearing normal in public. It did not look scientific.

It taught me that service friendliness can be paper-thin. Also, customers can still hear you.

2. The Unwritten Rules of Free Coffee

At the time, Dunkin' franchises had one of the most liberal employee food and drink policies I have seen. On shift, an employee could drink any coffee product, except a Coolatta, and eat anything from the donut case.

Management also instructed us never to charge a few local politicians who came in from time to time. I assume this was some weird attempt to bank political goodwill through medium regulars.

Once those ideas were in place, I started applying my own logic.

Any coworker who came in off shift was not being charged by me. If I came in off shift, I would not bother the person working. I would walk behind the counter in street clothes, make coffee to my exacting standard, and walk back out with nothing more than a nod.

Then there were the super-regulars. Plenty of people came in at roughly the same time every day, but these were the ones you were genuinely happy to see. They were kind, patient, tipped, and treated the person at the window like a person. I decided they were not paying either.

One guy drove a white Jaguar, and later a light-blue one, that we recognized immediately. All he had to do was say "hello" into the box and his coffee would be waiting by the time he pulled up. He offered to pay every single time. Every single time, I told him, "Not a chance." He would put a buck in the tip cup, thank us, and drive away.

Eventually I trained other people on this rule. The Jaguar guy did not pay for coffee. Even the manager's son honored it without knowing who the guy was or why he mattered.

I carried that habit into the way I tip. When I find a bar I like, which is not often, I over-tip. By over-tip, I mean excessively tip. People who work for tips should take care of people who currently work for tips.

Also, I have drunk more than I could ever have paid for this way. That is not the reason, but I would be lying if I said it was not an outcome.

3. Addiction Is a Powerful and Terrifying Thing

Quincy and the surrounding parts of Boston have always had a tough time with addiction. Over those years, I encountered more heroin addiction and casual crack use than I knew how to process. One Saturday made it impossible to treat as background noise.

I worked for a couple months with a woman someone told me was 26. I remember being shocked because she looked much older. That is not a punchline. She was carrying damage I did not yet have language for.

She came into work one Saturday morning seeming off. She was working the drive-through, and at random she would reach into the next customer's bag, take a quick bite of food, then hand it out the window. Obviously that was not okay, but nobody understood what was happening yet.

When her break came, she left and disappeared. People thought it was weird, but not impossible. Weird things happened there.

I was working a double that day, 6 a.m. to 6 p.m., to cover for a friend. Around 3 p.m., I took out the trash and saw her walking toward me in a completely new uniform.

She looked at me and said, "What the hell are you doing here?"

I told her I was working a double.

She said, "No seriously, stop fucking with me - what the hell are you doing here?"

After a few more words, I realized she thought it was the next morning and that she was arriving for a 6 a.m. shift.

I brought her inside. She was distraught and still not convinced of the time. I sat her at a table and gave her water. Eventually she said she felt better and left.

Soon after, a creepy guy in a rusty pickup truck started circling the store. He came inside and asked for her. We told him she had already left.

About an hour later, they were both back, with the police about a minute behind them. She was arrested in front of the counter for allegedly stealing the tip jar at a nearby store. The guy in the rusty pickup, who by then we assumed was her boyfriend, saw it and took off before the cops saw him too.

I do not know what happened to either of them after that. I hope they got clean.

Aside #2: Heroin Isn't the Only Thing That's Bad for You

I worked the drive-through all the time. The two people on it were the busiest people in the store for the entire shift, but time passed quickly and the tips were good.

There was an older couple in a beat-up minivan I recognized as regulars. They usually ordered two coffees and a dozen donuts. I had not realized until one day that the dozen donuts were for them, right then, in the van.

As I rang them up, I saw one of them in the passenger seat lift up their shirt and inject insulin into their stomach.

My counterpart passed them the food as I got their change. By the time I reached out with it, they had already eaten two donuts from the box.

I could not bring myself to eat another donut for years after that.

That was the first time I felt like a drug dealer selling coffee and donuts. I was not happy to be further poisoning the world, but what are you gonna do?

4. Don't Be an Asshole, Even If You Own the Place

The couple that owned our store, along with about five others nearby, came in from time to time to check on things.

The wife always seemed to arrive directly from riding her horse. I do not think I ever saw her wear anything other than riding pants, which prompted the manager to joke that she would beat him with a riding crop if he messed something up. I did not think he was entirely joking.

The husband came in less often, but when he did, it was a show.

We had a short row of two-person tables. Looking out from the counter, he would sit at the one farthest to the right, directly underneath the No Smoking sign. The manager would bring him coffee and the books, and the owner would light up a cigar.

He would sit there for at least an hour going through cash reports while customers complained about the smoke. But he owned the place, so what are you gonna do? I am not even sure he liked smoking that much. He seemed to like smoking there because people did not want him to.

I found out a few years ago that he was charged in a conspiracy to defraud the IRS, so maybe there was more going on than I knew at the time.

The lesson was not subtle. Ownership can make petty people feel consequence-proof, until it doesn't.

5. Messing with Cops You Kinda Know Is Still Probably Not Smart

The stereotype is not entirely wrong. Quincy cops came in for coffee all the time, and they were firmly in my "never charge, ever" category.

The local cops all knew us. My best friend, to this day, worked at Dunkin' at night and as a mechanic during the day, usually outfitting cop cars. Because of that, he was practically untouchable. One detective friend once told him, "If you ever get into trouble - as long as you're not selling drugs to kids or beating up your wife, give me a call and I'll take care of it."

One late afternoon, a group of us piled into two cars after a staff meeting. On the way back, for reasons known only to young idiots, both drivers decided to race down the main road.

Within a couple minutes, both cars were pulled over. The cop went to my friend's car first, recognized him, and started laughing. Then he walked back to the car I was in.

He started with, "You assholes are so lucky that I know your friend," and then recognized the rest of us and yelled, "Oh what the fuck - get the fuck out of here!"

Another time, my friend and I went to a liquor store. As we pulled into the parking lot, he recognized the number on the police cruiser parked out front.

He pulled in next to it so closely that I was shocked we did not scrape it. Then he went inside and left me trapped in the passenger seat.

Quincy police used to drive cash deposits from the liquor store to the bank to avoid what I can only imagine had been a number of unpleasant incidents. So the cop came out, saw he had less than an inch of space to get into his cruiser, and immediately started screaming at me.

By the time my friend came out, the cop, with one hand on his gun, had worked through one of the most impressive lists of expletives I have ever heard.

As soon as he realized my friend was responsible, he went from bright-red fury to hysterical laughter.

The pranks and brushes with death were funny then and seem incredibly stupid now. The important part was getting to know local cops as actual people instead of one flat category of authority. Up to that point, I thought cops were just to be feared and avoided. Some should be. Some absolutely should be. But knowing a few well enough to joke with them complicated my thinking in a way I needed.

That was the larger education of the place. Dunkin' did not make me wise or build character in any clean, inspirational way. It put me behind a counter for five years and sent the whole town through, one coffee at a time.

Photography Workflow 2020

Mon, 12 Oct 2020 00:00:00 GMT

Simple

Simple is better.

In 2018, a little more than two years and one completely different world ago, I wrote down my photo workflow for the sake of documenting it.

The secondary purpose was to have something I could revisit and simplify. Enough small changes have stacked up since then to justify another snapshot.

The 2018 version still had a lot of "save everything, everywhere, just in case" energy. That made sense. RAW files are the negatives. Losing them would be bad. But by 2020, fewer files deserved active attention.

Simple did not only mean fewer steps. It meant a smaller catalog, fewer unedited RAW files, and less for future-me to inherit.

The 2020 process

The broad shape is familiar: transfer, back up, import, cull, edit, export. The difference is that I now cull the shoot before syncing it to Lightroom.

My process at present is:

Transfer images
- JPGs go to /JPG/ on the Desktop.
- RAWs go to a dated folder, like /2020-10-12/.
Back up and import
- JPGs go to Google Photos, with one gallery/album for each camera type.
- RAWs go to the NAS, which backs up to Google Drive, with folders organized by format and camera type.
- I pause Lightroom CC syncing and import the RAWs.
Cull
- I review everything and remove anything I know I am not selecting for editing.
- Once the obvious rejects are gone, I resume Lightroom CC syncing.
Edit
- My Neutral + Lens Correct preset is automatically applied to all imported images.
- My "Go To" preset gets applied along with exposure, color balance, and whatever else the shot needs.
Export
- Final exports go to a "Selects" gallery on Google Photos.
- They may also go to Instagram, IG stories, or the archive/shoot posts I was maintaining at the time.

Compared with 2018, this is less sprawling. It is still very much a 2020 workflow: Google Photos, Google Drive, Lightroom CC, NAS storage, Instagram, the whole thing. But Lightroom is no longer where every frame has to live forever.

Culling became the important step

The one part I am still working through is the culling and review process.

Even with a 5K iMac display, I still feel like the nicest screen I have at present is on an iPhone XS Max. That sounds ridiculous until I am making picks. The screen is great, the phone is always with me, and reviewing in smaller chunks helps.

Depending on the situation, sometimes I will let the entire shoot sync to Lightroom just so I can review and cull on my phone.

There are rumors of a Mini-LED iPad Pro coming in early 2021. If that happens, this workflow may need to adapt, especially because direct import from the camera is possible. For now: get the files safe, cut the obvious misses early, then let the smaller set move through Lightroom.

The important part is the order. The cull is not cleanup anymore. It is part of deciding what the archive is.

Catalog Bloat

A few weeks ago I realized that, in addition to paying Adobe $30/month for 2TB of cloud storage with a Creative Cloud membership, my catalog was full of shots I would never touch again.

For the majority of the 15 years I have been shooting, I did not follow a strict photo cull. I kept things because storage existed, backups existed, and RAW files felt too important to question. That sounds responsible until the catalog becomes a slow, expensive junk drawer.

Applying the current workflow to my entire catalog took most of a weekend. It also removed over 2/3 of what I had been saving.

Not only does this bit of digital housekeeping feel great now that it's done, it will also save $180/yr with the downgraded storage plan - which, up until today, I had been paying to store raw files for photos I was ultimately never going to edit.
— Bill Anastas (@banastas)

That was the practical reward: $180/yr saved and a downgrade to the $15/month plan. Not glamorous, but satisfying.

The smaller catalog is also easier to use. Syncing is less annoying. Browsing old work is faster. The good shots are not buried under every alternate angle, missed focus, duplicate, and "maybe someday" RAW file.

Why Deleting Helped

There is something genuinely nice about scrolling through years of photo selects faster. Not because the library is smaller as an abstract virtue, but because the things left behind are more likely to be worth seeing.

I thought it would be tougher to delete that many images. It helped that copies still existed elsewhere. The NAS and Google Drive had the durable RAW backup, and Google Photos made old JPGs easy to view. I was deciding what deserved to stay in the active editing catalog.

The useful question became very simple: "honestly, will we ever edit this shot?"

Asking that question across 15 years of photos made the answer easier on newer shoots. If a frame is not useful now, and it is not likely to become useful later, keeping it in Lightroom adds storage costs and makes the catalog harder to browse.

The point is not to become precious about minimalism. I do not need the photo library to prove anything. I just do not want another weekend-long purge 15 years from now.

The expensive cable

The one new piece of hardware that has really helped is ridiculous.

An Apple 2 meter Thunderbolt 3 Pro Cable with a top transfer speed of 40Gb/s costs $129. That is insane, which is what I said when I first saw it.

It is also one of the most useful parts of the setup.

Transfers from my camera used to take 10, 20, even 30 minutes, depending on how many shots were on the card. With this cable, I do not think I have waited longer than a minute. Usually it is seconds.

The price still feels silly. The time saved does not.

The point of this snapshot

So this is the 2020 version: Google Photos, Google Drive, Lightroom CC, a NAS, and exports where they made sense then.

But culling became part of the workflow instead of an afterthought. Keeping everything had started to cost money, time, attention, and trust. A smaller Lightroom catalog was faster, cheaper, easier to live with, and less likely to become a burden.

(Previously: Photo Workflow 2018)

Letterboxd Is the Best Social Network

Mon, 05 Oct 2020 00:00:00 GMT

Given the current state of social media, a network built around one specific thing feels almost suspiciously sane. It is not trying to become the whole internet. It is not trying to train me into a permanent state of low-grade political panic. It is not, as far as I can tell, built primarily to influence elections. It is just movies.

I am sure there are other focused networks like this that I either do not use or have never heard of. The two that come to mind immediately are Untappd and Letterboxd. Untappd was fun for a while, but with my beer intake reduced to almost nothing as a general "feel better" rule during the pandemic, I have not exactly been chasing new badges for hazy IPAs.

So Letterboxd wins by default, and also not by default. Letterboxd is truly the only social network I love.

That is partly because it stays narrow. Letterboxd does not need to know everyone from high school. It does not need to host my family arguments, breaking news, recipe videos, brand apologies, and whatever rage machine happens to be idling that day. It has one domain, movies, and it makes the social layer useful instead of exhausting: logging, ratings, reviews, lists, stats, recommendations, and seeing what people with interesting taste are watching.

I do not know what part of my personality makes me so happy to track things, sort things, and turn habits into little personal data sets, but Letterboxd is extremely good at feeding that part of my brain without making it feel gross.

The site has replaced IMDb for me for quick lookups when some film detail escapes me: cast, crew, release year, runtime, where I know that actor from, whether I am remembering the title correctly, all of it. But it also has a vibrant community of people who actually love films, which is the part that makes it feel alive rather than just useful.

The logging is especially cool, not because I am in constant danger of forgetting every movie I have ever watched, but because the record becomes valuable later. Years of entries show what I watched, when I watched it, and what I thought at the time.

Stats

At any point, overall or for any year that I have data on, I can take a deep dive into my viewing habits and see what I liked best that year, how many films I watched and when, and breakdowns by genre, director, actors, crew, and country of origin.

That sounds dry, but it is not dry when it is your own taste staring back at you. The stats make patterns visible: which directors I keep returning to, which actors show up constantly, whether I am watching older films or new releases, whether a month was all comfort rewatches or a weird little international detour. It is analytics in the useful sense. It shows taste over time, not productivity.

At the start of each new year, Letterboxd also sends a beautifully designed report of the previous year's watching habits and totals. It makes it easy to revisit the year’s viewing.

Lists

OK, this is where things get a little crazy.

I started making lists of people and franchises I really liked, first to see how much of their work I had actually seen, and then to learn what I had missed.

Quentin Tarantino (90% watched)
Steven Seagal (75% watched)
Alien Universe (100% watched...and then some)
Keanu Reeves (41% watched)
The Work of Stan Winston (48% watched)

There is a very particular satisfaction in taking some vague feeling like "I have seen a lot of Keanu Reeves movies" and turning it into an actual number. Sometimes the number confirms the feeling. Sometimes it tells you that you have homework.

Then I went a bit further and made lists based on Oscar wins going as far back as the categories existed.

Best Picture Winners (1929-Present) (37% watched)
Best Animated Feature Winners (2001-Present) (63% watched)
Best Visual Effects Winners (1963-Present) (67% watched)

The list feature works beautifully for this because it is not just a static page of titles. Letterboxd knows what I have watched, so I can fade out the films I have already seen and turn a list into a to-do list without manually maintaining a second version of it.

Once that clicked, I started making more personal and much less defensible lists, like "Movies I can't stop watching no matter what point I start watching them." (100% watched) and "The Films That Raised Me" (100% watched).

Those are the lists that make Letterboxd feel less like a database and more like a notebook. It is not only what I have seen. It is what I return to, what shaped me, what I use as comfort food, and what I want to close the gaps on.

Media Library

Lists are functional for me beyond the social and obsessive parts. I also keep two separate lists tied to my media library and purchases I have made through iTunes or Movies Anywhere.

That matters because the actual question is not always "what should I watch?" Sometimes the better question is "where can I watch the thing I already own?" Between a personal media library, Apple purchases, Movies Anywhere, streaming apps, and whatever licensing chaos is happening that week, browsing can become strangely inefficient.

Letterboxd helps me track what I want to watch, what I own, and where I can watch it.

Letterboxd also has an Apple TV app that is equally well designed. This is my preferred method of figuring out what I should watch next and then knowing exactly where to look for it.

Add me!

(But, like, not in the creepy Facebook way.)

It is my favorite social network because it barely feels like one. It feels like a notebook for movies that other people happen to be writing in too.

Influences: Anthony Bourdain

Mon, 28 Sep 2020 00:00:00 GMT

Photo Credit: Christopher Sturman

"I understand there's a guy inside me who wants to lay in bed, smoke weed all day, and watch cartoons and old movies. My whole life is a series of stratagems to avoid, and outwit, that guy."

I wanted to start a series of posts about the people who have influenced me, and Anthony Bourdain feels like the right place to begin.

He encouraged me to visit unfamiliar places, try food I did not recognize, and listen to the people serving it.

A little over a decade ago, I used to exist almost entirely within a few square miles of Culver City, CA. I had my coffee place, my lunch spots, my bars, my home, and my office all inside a tiny radius. It was comfortable. It felt complete. I had convinced myself that all the best stuff I needed was already there, wrapped around me in a small, familiar loop.

Then I started watching No Reservations religiously, and that little loop started to feel small.

Bourdain opened up the world through my favorite common denominator: food. Not food as luxury, or as a list of reservations to brag about, or as some polished lifestyle accessory. Food as a way into a place. Food as work, history, appetite, hospitality, pride, survival, pleasure, and occasionally danger. He made the world feel reachable, not because travel was easy, but because the first move was so simple: show up hungry and be willing to listen.

Bourdain isn't the reason my passport went from a pristine booklet of untouched pages into a tattered mess of fading stamps, but he did get me excited about each one of those journeys.

That is the part that stuck. When I go to a new city or country, I try to find at least one place where I could picture Tony sitting down for a meal. Not the obvious place. Not the room built for people passing through with cameras and expense accounts. I mean the local room. The place that might be a little intimidating at first. The place where people are actually eating, drinking, talking, and getting on with their lives.

That became a method for me. Find the bar. Find the counter. Find the place where the menu assumes you already know what you are doing, then be humble enough to admit that you do not. Let the meal teach you something before you start trying to summarize the city.

If I’m in a city he documented, I try to visit at least one place he went. In 2018, I visited Le Royal in Paris after seeing it in his show.

Le Royal, Paris, 2005

Le Royal, Paris, 2018

Kitchen Confidential

Photo Credit: Martin Schoeller

"I've long believed that good food, good eating, is all about risk. Whether we're talking about unpasteurized Stilton, raw oysters or working for organized crime 'associates,' food, for me, has always been an adventure"

Superfan or not, I didn't realize until recently that the audiobook of Kitchen Confidential is narrated by Tony himself. That matters. You do not just get the words. You get the timing, the drag in his voice, the little stabs of humor, the weariness, the specificity. He knew exactly which lines were jokes, which ones were confessions, and which ones he wanted to emphasize.

In the weird and challenging times we find ourselves in now, hearing his voice takes me back to a time of both optimism and possibility, a place I hope we can all get back to soon.

"To me, life without veal stock, pork fat, sausage, organ meat, demi-glace, or even stinky cheese is a life not worth living."

That appetite was part of the lesson too. Bourdain could be sharp, impatient, profane, and unfair, but he never made pleasure feel small. He treated eating seriously without turning it into manners class. The point was not refinement. The point was to stay open to the world, even when the world came at you greasy, loud, strange, and stronger than expected.

The book also shows how much respect he had for the people who taught him, showed him the light, gave him a second chance, and kept showing up. He admired talent, but he seemed to revere reliability even more: the hard workers, the people who never called in sick, the people who did what they said they would do.

Kitchen Confidential helped him leave the kitchen as a job, or at least made that departure possible. It did not soften him, and it did not turn him into someone else. Since his death, I have often wondered which environment was more brutal: the kitchen that formed him, or the endless travel and loss of anonymity that came after.

RIP Tony

I miss this guy a lot. It is not often a day goes by that I do not think about him.

I find myself wondering what his takes on the present-day world would be, especially with regard to some of the most damaged industries: travel and food. In 2020, that question feels less like nostalgia and more like an absence. He spent so much of his work showing how much life was carried by cooks, servers, markets, bars, hotels, drivers, strangers, and crowded rooms. So many of those rooms are hurting now.

I do not know what happened to him toward the end. I do not want to pretend I do, and I do not want to turn his death into a theory. I just wish he could have been helped. I wish he had not had to carry whatever pain he was feeling alone. That is a simple thing to say and an impossible thing to fix after the fact.

I know for sure, though, that I will carry his inspiration with me always, and my son will know his name and the lessons he taught.

Most importantly, I hope my son learns to enjoy a meal with people he does not yet know, listen to them, and learn something about their lives.

The VFX Industry: Part 1

Mon, 21 Sep 2020 00:00:00 GMT

This is part of a three-part series on the VFX industry: how the work changed, why the business became harder, and how those pressures affected artists.

Series: Part 1 · Part 2 · Part 3

This first part is about how I got into VFX, what Centropolis FX was like in 2002, and how quickly the job stopped being just "cool computer stuff" and became production logistics: safety, reviews, delivery dates, budgets, and the invisible work behind every finished shot.

I woke up this morning with the sudden urge to start writing down some of my experiences in the visual effects industry. I'm worried I will start forgetting things as the years go on, and enough time has passed that the stories feel worth writing down, so here we go!

I applied for the LA Program at Emerson College in Boston on November 2, 2001 for the Spring 2002 semester. After watching the VFX features on the Jurassic Park DVDs a few too many times and also taking a CG Animation class at Emerson, I was determined to secure an internship at a visual effects company.

I didn't realize at the time how few visual effects companies there were in LA, and even fewer were taking interns, but I did hear about a VFX producer that was a very proud alum and actively looking for interns. I sent my resume in and a week or two later I remember running out into my parents' front yard (for best cell reception) when I realized that someone from LA was calling me on the phone. It was Diane, a recruiter at the time from Centropolis FX in Culver City - and she was calling to set up my interview.

Once in LA and settled into my temporary studio apartment at the Oakwoods on Barham Blvd, I got psyched up for the meeting. I was familiar with Centropolis' work, and thanks to Cinefex, I also knew what projects they were working on currently. Being able to cite their current work, I later was told, set me apart from all other candidates, and I was offered the spot -- as a side note, I have given out this advice more times than I can remember at this point; such a small amount of time and research literally changed my life.

Centropolis needed 2 interns for the spring semester (each intern splitting the week along with classes, etc); the other person they offered the spot to turned them down because the commute from the Oakwoods to Culver City was "too far" for them. When they told me this, they all laughed and said, "good luck in LA then!" I had two 4-hour classes a week, but with only one of those during the day, I got approval from both the college and my new boss to work the maximum number of hours I could each week.

Warrior. Legend. Intern.

The Scorpion King

When I started at Centropolis in January of 2002, work was well underway on The Scorpion King, an offshoot of The Mummy franchise starring Dwayne "The Rock" Johnson and directed by Chuck Russell.

We had a few involved sequences like the fully-CG fire ants, the sandstorm, and the flaming sword battle to complete along with a ton of wire/rig removals, CG arrow additions, and weapon reconstructions.

Watching hours of DVD featurettes on CG dinosaurs hadn't taught me anything about how many other unseen effects shots there were. Much of this had to do with keeping things safe at the actual shoot; from painting out wire rigs and harnesses to adding many CG arrows to a shot that would have been otherwise dangerous to shoot practically.

There was a pile of sword hilts lying around (in addition to the real swords), each with a green ball attached where the blade would normally be -- these were used when the actors were riding horses to keep both the actor and the horse safe in case of a tumble or fall -- we would add CG blades back in afterwards using the green ball as a marker.

CG Fire Ants

During a screening of the film for PETA as a final confirmation that no animals were harmed, they got very upset that so many ants had been killed on-screen - they also asked what species the ants were, to which Universal replied, "computer generated."

Loyalty

A week or two in, I walked into my boss's office and asked him if I could attend shot reviews - on that day in particular, the director and a representative from Universal were there to review the current shots and give notes. At this point, I was still gaining his trust, but he sat me down to explain how the last intern they let in the review started giving their opinions on the shots in front of the director of the film. A finaled shot can quickly become a work-in-progress based on the whim of the director and when this results in a profit or loss of tens of thousands of dollars (or even hundreds of thousands in some cases), the goal was always to make the best work possible within the budget. They hadn't let another intern attend shot reviews since that incident, even internally. That said, he said I could go to listen and observe, but I had to keep my mouth shut.

I remember that day not only because I was so excited to be in the review, but also because of what happened. In addition to the film's director, the head of VFX at Universal was also in attendance - he was there to represent the studio's approval of the shots in addition to the director's feedback.

At one point, we were all gathered in the lead compositor's office and my boss asked me to go and find the guy from Universal who was suddenly not there.

I saw him out in the lobby - he had stepped aside to take a phone call in between the screening room and the office - and I let him know where the group was and offered to walk him over.

As a joke, while we were walking back, he told me to go back into the room without him and say the following:

"XX told me to say that he doesn't give a fuck about this film and that he's put me in charge of approvals on the studio's behalf moving forward"

I walked back into the room, my boss looked up at me and asked if I had found him (at that point the guy was standing outside the door within earshot) - I looked back at my boss and had the following immediate thought in my head:

While it's very possible that what I was told to say will result in some laughter, it's also even more possible that the only one laughing will be the guy that told me to say it. I don't work for him, I work for my boss and I'm not about to jeopardize the remainder of my internship because this guy wants a laugh at the intern's expense.

The guy came back in the office, called me a chickenshit or something to the like, and then the review proceeded.

I could have handled that a few different ways in retrospect, but given it was probably my second week on the job, I think silence and therein loyalty to my boss would have won in any case.

Co-workers

Embedded in the show, I sat in the production pit across from my boss's office with some of the coolest people I have ever known. Not only was the work incredibly interesting, but they all taught me that the best way to deal with stress was with laughter and they'd crack me up all day.

The VFX PA loved to talk shit (and also spoke English as a second language), so the VFX Coordinator kept a file of her best quotes and we would all recite them with endearment - some of my favorites were:

"It's not broken, you just need to fix it"

"Plants have assholes! They do take a shit! I took a biology class."

"What about French, they don't believe in anything" (Trying to find a restaurant that is open on Good Friday)

They were both brutally honest with me about the job and the life that accompanied it - they would normally work long hours at least 6 days a week; if they were lucky enough to have a Sunday off, it was usually spent sleeping.

I learned a ton from them just watching how they interacted with everyone there; from the runners, artists, and other productions, to the film's director as well as between each other.

Baja Fresh addiction

The shows would order lunch for the team a couple times a week and the preferred place to order from was Baja Fresh. I became an instant addict after my first bite of a steak ultimo and never looked back.

I would eat a Baja Fresh burrito for lunch at least 4 times a week; often topping it off with many espresso shots.

This is not something I would even think of now knowing both a long day and long commute was ahead of me - it makes me long for the resiliency of my 20-year-old stomach.

Ron Jeremy

One day when I was coming back from Baja Fresh (like most), and I stopped at the gate to scan my card and enter the lot. As the gate arm went up, I started moving forward and didn't realize someone had started crossing the driveway while I was watching the arm raise.

That person was Ron Jeremy and I almost hit him with my car.

I went back to my desk and stated to my coworkers what I had almost done thinking, wow that was weird, I bet everyone else will think so also.

Forgetting where I was and what I had seen so far, I should have expected what I heard instead, "oh, you saw him too?"

Main title sequence

The show asked us, rather late in the game as I was told, to create a main title sequence for the film. We rushed concepts and lookdev across a couple teams; I even got to go to an exotic pet store to pick up 2 emperor scorpions to crawl across blue-screen stretched out across the screening room floor for one of the concepts.

Towards the deadline, we had to figure out the most cost-effective way of delivering on-time (albeit at the last possible minute).

Using 1 or 2 Laser Recorders @ Cinesite

We had 2 options to get the 2,140 frames to Universal on the due date with the more expensive option giving us a 3-hour lead on the other - we went with that option and the footage made it to Universal just in time.

A typical shot at Centropolis FX

I wrote this overview of a typical shot's workflow - it's now almost 20 years outdated, but an otherwise interesting snapshot of how the technology of the time drove shot production.

1. SHOOT
A VFX crew is present on the day of shooting to record reference data, physical data, and to place markers down on areas of the set to be used as reference later.

2. FILM
Developed film is sent to CFX. CFX sends the film out to be cleaned. All particles, dust, and hair are removed from the film in preparation for scanning.

3. SCAN
The negative is scanned and converted into digital cineon files. These are very high-resolution images that represent one frame of film for each image file.

4. BACK UP
As a "just in case" the shot is backed up on digital tape in the event that the cineon files are lost, corrupted, or destroyed.

5. SENT TO AVID
The shot sequence is sent to the Avid where it is checked for correct frame range and picture position.

6. ONLINE
By the time the shot is online and ready for treatment, the production staff has meticulously kept track of all aspects of the shot. There are separate database entry sheets for all of the above information as well as what must be done to the shot, who will be doing it, and a detailed breakdown of the cost of the total shot treatment.

7. WORK BEGINS
A shot will visit any number of combinations of departments within the facility. If 3D elements are being added, the shot must be tracked. If treatments are being done in the foreground and background of a shot, the elements in the middle must be rotoscoped out of the shot in order to be layered back into the shot by a compositor later. If reference points, rigs, or harnesses need to be taken out of a shot, they will be removed by the paint department. Depending upon how many different treatments a shot needs, the shot might eventually be sent to almost all departments within the facility. The final place a shot will always end up is in the compositing department. This is where all elements of the shot are combined into a finished version. If it works, anything that has been added into the shot will look like it was filmed at the same time the original footage was shot.

8. WORKING VERSIONS REVIEWED AT THE 1K
At any time in a shot's progress it can be sent to the 1K viewing machine to see how it looks or how it is coming along. This progress is checked by supervisors, VFX producers, and above all the client (director, producer, etc.) to make sure everything is going as it was imagined. From here it is decided whether or not the shot will be put to film for review.

9. COMPOSITE SENT TO DATA I/O
Here the shot is written to digital tape so that it can be sent from the facility to be put to film.

10. FILM VERSION AS PENDING FINAL
During the course of a show, the client will come in for frequent visits to see what is being accomplished. If a shot is nearing completion or is considered to be done it will be shown on film to the client and they will either final it or say what has to be changed.

11. REPEAT
The treatment process is repeated until the shot is finaled. Once the shot is finaled, it is sent on film to the production company doing the motion picture and it is archived at CFX.

Wrap party

Before going to my first wrap party, I learned another valuable lesson from my boss. Once everything was planned out and confirmed, he admitted to me that had he not padded the original budget to cover a wrap party, there wouldn't have been one. From this action I learned, no matter what, it's imperative to always find a way to thank people for their hard work. They had pushed very hard to get everything done on time and they all deserved, at minimum, an acknowledgement of that in the form of at least a few hours to burn off steam.

"Open Bar"

A friend from Boston had come out that weekend to visit me in LA and wanted to come with me to the party. I have a couple dozen pictures from that night that I won't share, but overall, the entire crew drank a heroic amount of alcohol.

At one point, I was out on the patio talking to my boss and he started telling me how much he enjoyed working with me; I thanked him for the opportunity and he thanked me for my hard work. It felt like we were having a moment and my very next question would have been career-related, until he said, "hey, is that your friend over there?"

I thought you guys were from Boston?

Not more than 6 feet away from us, my friend from Boston was puking his brains out into a trash can on the patio.

The Matrix sequels

There was an all-hands meeting one day and when I saw the concept boards on display in the screening room I immediately knew what we would be talking about -- we had landed shot work on the Matrix sequels.

I don't think I blinked once during the presentation - the head of recruiting noticed the look on my face and asked me to hang back for a minute to talk. She reminded me that everything I saw there was confidential, which I immediately acknowledged and agreed with, but inside I was freaking out!

I got to help a bit with the setup of these shows, but at the end of my internship I went back to Boston to graduate and decided that I would get a job to save up enough cash to move back out to LA as soon as possible.

Unfortunately, The Matrix sequels were the beginning of the end for Centropolis FX. The company was technically owned by the German post-production house Das Werk since it was sold in 2001 by Centropolis Entertainment. Das Werk went bankrupt and its stock crashed on the German exchange; Centropolis FX itself was solvent thanks to the Matrix work, but Warner Bros. didn't want to take any chances and pulled the shots once the Das Werk news came out.

Centropolis FX closed its doors on December 17, 2002 and the Matrix work was picked up by Imageworks. That said, my soon-to-be new employer, knowing they didn't have enough people on staff to cover the Matrix work, hired the head of recruiting from Centropolis and started bringing over as many of the crew as they could.

At the time, I was devastated that Centropolis had closed their doors; in retrospect though, it charted the path on which I would return and get to work with some of my friends again.

Overall, I don't think I've ever learned as much as I did in the same short time period - the majority of which I still utilize today.

In addition, the people that I met and worked with at CFX literally changed my life and directly influenced the serendipity that led me to the present day.

Producer takeaway

A fix that looks simple on screen may require tracking, roto, paint, CG, compositing, review, and film-out. The earlier you involve the people doing the work, the less you pay later in panic, overtime, and compromises.

The VFX Industry series

An Obsession with Formula 1

Mon, 14 Sep 2020 00:00:00 GMT

Lights Out @ Mugello

Lights Out

I started watching F1 religiously at the beginning of the 2020 season. I had always been F1-adjacent: curious, aware of the shape of it, willing to watch the occasional clip, but never fully committed.

Then 2020 happened. Quarantine, content hunger, and my brother's long-running obsession with it created the perfect storm. He has been all-in since we watched a screener of Rush together in 2013, and this year I finally stopped treating it like a thing I might someday get around to understanding.

Mostly, though, this is just an incredible thing to watch.

"F1 is like if NASA was a sport" -@brianastas

That is probably the cleanest way to explain why it works on me. F1 is not just a sport in the usual sense. It is machinery, people, systems, and risk all running at the same time. From driver personalities to team history, from the cars and their technology to testing, upgrades, rules and regulations, and the weird little personality of each track, there is always another layer to notice.

I never expected tyre strategy to become something I cared about. It turns out I care very much about tyre strategy.

How I got caught up

When I went in, I went in hard. I binged the first two seasons of Formula 1: Drive to Survive in a few days, which gave me the cast list, the recent grudges, and just enough context to understand why a seventh-place finish can sometimes feel like a triumph and a second-place finish can feel like a disaster.

Then I started filling in the history. I already knew Senna was a legend, mostly thanks to Senna (2010), but Ferrari: Race to Immortality (2017) and McLaren (2016) helped connect the modern sport to the older, much more dangerous one. Those movies made the current grid more legible, but they also made the safety stakes feel real. The danger is not a branding exercise. It is the thing the whole sport has been trying, imperfectly and expensively, to survive.

Through the 2020 season so far, my excitement from Friday practice to Sunday race day has not really faded. But I did start to notice the low-level fatigue that comes from seeing some combination of Hamilton, Bottas, and Verstappen at the front of the grid and then, very often, on the podium again. They are brilliant. That is not the problem. The problem is that dominance can make even brilliant things feel pre-written.

The last two weekends in Italy changed that. Monza and Mugello made the whole thing snap into place.

Italian Grand Prix @ Monza

"Absolutely anything can happen in F1." -@LewisHamilton

I always love an underdog story. I celebrated with my 82-year-old grandfather when the Red Sox won the World Series in 2004, a first in both of our lifetimes, so my tolerance for dramatic sports catharsis is not exactly zero.

Monza had that feeling.

Pierre Gasly had been through it. After watching the way his Red Bull opportunity fell apart, and after seeing how much of F1 is shaped by machinery, timing, contracts, team politics, and confidence, his win at Monza felt impossible until it happened. That is what made it so good. It was not just a fast driver winning a race. It was a guy who had been publicly sorted into the "not quite enough" pile forcing the entire sport to look at him again.

By the end, I was screaming like this Canal+ announcer.

Canal+ Announcer as Pierre Gasly Wins

Tuscan Grand Prix @ Mugello

This past weekend at Mugello was a completely different kind of race: 2 red flags and 8 DNFs, leaving just 12 drivers out of 20 racing to the finish.

Oh no!

There was a multi-car crash in the first turn of lap one that immediately brought out the safety car.

OHH NOO!!

Then, as soon as the safety car left the track, there was a massive crash on the restart. From my couch-level understanding, the drivers seemed to get mixed signals about when the race had actually gone live. Some were already accelerating hard while others were still bunched behind the leaders.

Once everyone was confirmed OK, the chaos changed the race in exactly the way that makes F1 so interesting. Safety cars and restarts add a whole new layer: strategies get rewritten, opportunistic teams can pit with less time lost, gaps disappear, and the whole field gets compressed into something more volatile. A race that looked like it might settle into a familiar shape suddenly becomes live again.

That is the thing I keep coming back to. F1 is a timed technical problem where the conditions will not sit still. The cars are changing, the track is changing, the tyres are changing, the fuel load is changing, the weather might change, the rules matter, the pit wall matters, and then a safety car can wipe out twenty laps of careful gap-building in one lap.

Following those changing conditions is what keeps me interested.

Alex Albon's first F1 podium

The very best part of Mugello was seeing Alex Albon on his first F1 podium. It felt well-deserved, and honestly like something that should have happened already if not for a few conflicts, bad timing, and some overall rotten luck. After everything else that race threw at the grid, seeing him finally get there was an extremely satisfying payoff.

I'm not a sports guy, but I love F1

I've made attempts in the past, but I'm really not a sports guy. Growing up in Boston would have been a lot easier if I was. I understand the civic function of sports. I understand the rituals. I understand, intellectually, that people enjoy yelling at televisions in groups. It just rarely clicked for me in a way that lasted.

F1 clicked because I enjoy learning how the cars, rules, and strategy affect a race. Following practice and qualifying gives me more to understand and anticipate on Sunday.

It gives me a sport where going down the technical rabbit hole is not extra homework. It is part of the experience.

F1 has a great YouTube channel and there are awesome fan pages like WTF1 and F1Fun4u. The teams even post deep-dive content breaking down the technology behind the cars (ex: How Do F1 Power Units ACTUALLY Work?).

If you end up falling into it like I did, a subscription to F1 TV Pro is only $9.99/month, and with 24 feeds available every race, f1viewer really helps keep everything organized.

f1viewer

I still do not think I became a sports guy exactly. But apparently I am very much a Formula 1 guy.

How I Infuse

Mon, 07 Sep 2020 00:00:00 GMT

I've been a bit obsessed with home media management since the day I found out about XBMC on the original Xbox.

That was the first time owned files felt like they could be something more than a pile of folders. They could have posters, descriptions, and a real interface.

When the first-gen Apple TV launched, I spent a ridiculous amount of time manually entering metadata into my files for a crude list view that was still difficult to navigate and basically impossible to search. It was overbuilt, but the impulse was right. I wanted my own media to feel as clean and accessible as streaming was starting to feel.

By 2020, Infuse made my own media library easier to browse than any other app I had tried.

Plex

When I first heard of Plex, I had never been so excited by a single piece of software.

Plex took a folder of media, scanned the files, and automatically pulled in the metadata that made the whole thing legible: posters, descriptions, cast lists, seasons, episodes, and watch status.

After that first scan, Plex acted like a personal Netflix. It kept track of what I was watching, remembered where I left off, and suggested what to watch next. It turned a folder tree into a library.

The feature that made it especially useful for me at the time was cloud-drive support. Plex could connect to my cloud storage and let me stream my personal library wherever I was. For a while, that worked so well that I disconnected my hard drives, put them in the closet, and relied on streaming my own files from the cloud.

Local + Cloud

At a certain point, a fear started to linger in the back of my head: what if I lost access to my cloud account and the data it contained?

I looked into different NAS solutions, but one brand was hard to ignore, so I pulled the trigger on a Synology DiskStation and two WD Red 8TB hard drives. Synology has been reliable, and the WD drives are workhorses.

The feature that made everything work for me was Cloud Sync. It keeps a local copy of everything in the cloud and preserves parity between the two environments.

That solved two problems at once. The cloud copy was still the convenient access layer. The NAS was the durable, local, under-my-control copy. Sync kept them from becoming two separate messes.

It also changed how I cleaned things up. My cloud drive had become a dumping ground for work files, project files, home files, and media. Previewing and rearranging that kind of mess through a web interface was miserable. Being able to make local changes, rename files, move folders, and have those edits sync back to the cloud saved me a stupid amount of time and frustration.

If the cloud account disappeared, I still had the library. If I was away from home, the cloud copy still made it reachable.

Infuse

For a while, I was happily paying Plex $39.99/yr for a premium membership that mostly showed my support for the product and my hope that it would exist for a long time.

Then Plex shut down cloud libraries, noting that server costs had become too high to maintain the feature. I still do not fully understand why Plex needed server-side infrastructure for what I had understood as a connection between my cloud drive and my viewing device. Whatever the reason, I could no longer connect Plex to my cloud drive and stream media that way.

Plex also started pushing parts of the app that users could not really disable, including live channels, and the interface began to feel more cluttered. Then came content deals with MGM, Lionsgate, and Warner Bros., which made it look to me like Plex was drifting toward mini-Netflix territory rather than staying focused on personal libraries.

Plex had solved a real problem for me, but the feature I cared about most had gone away while the product added features I did not want.

Around this point, my brother introduced me to Infuse, and I finally took a closer look. Infuse gave me cloud access to my personal library again, came from a team that seemed focused on playback and library experience, and cost roughly one-quarter of what I had been paying for Plex Pro.

Moving to Infuse exposed one last project: a refactor of my folder structure and naming conventions so the library would scan cleanly and still make sense to a human being looking at the files.

Metadata

Infuse has a great reference on metadata, but it still took trial and error to get high auto-detection without turning my folders into nonsense.

The important thing to know is that Infuse pulls metadata primarily from TMDb and TVDB, both of which are community-built.

If Infuse has trouble identifying something, a quick lookup on either site usually explains why: title formatting, year disambiguation, or a missing record.

The goal was not just to make Infuse happy. I wanted a structure that was readable if I ever had to browse the NAS directly. Automation is great right up until you need to fix one file by hand.

Movies

Movies were the easy part. My movie folder keeps everything at the root level using this naming convention:

/[Film Name] [Year].[ext]

Auto-detect Success: 99.69%

That format works because the folder is flat and movies rarely need more hierarchy. The year disambiguates remakes and duplicate titles, and the whole thing stays easy to scroll outside Infuse.

TV

TV took more fine tuning. Each show lives in its own folder, and each episode gets the show name, season, and episode number:

/[Show Name]/[Show-Name]_[Season][Episode].[ext]

Auto-detect Success: 99.85%

Based on the Infuse spec, repeating the show name in the folder and the filename is redundant, but Infuse does not seem to mind. It keeps the files useful outside Infuse.

With Plex, I had been using a ShowName/S01E01.mp4 format. That worked, but it meant every show folder contained files with the same generic names. If I needed to adjust something manually, the filename itself did not carry enough information.

The Infuse setup is more verbose, but that is the point. The parent folder gives the library structure. The full filename gives each episode enough context to stand on its own.

Library

Infuse wins on so many little workflow details, but one of its best tricks is allowing me to swap out libraries on the fly.

Away from home, I can switch to the cloud library and have access to everything on the go. Back home, I can switch to local storage and stream without using bandwidth.

That sounds minor until you have spent years trying to make one system cover every context. Infuse lets the local library be local and the cloud library be cloud.

It also creates collections automatically. I can browse pre-sorted groups by release decade, genre, or even resolution.

Sorting by Collections, Genre, and Release Date

Custom playlists are also an option, but I have not explored them much because they can only be created through the iOS or Apple TV app. That makes the task more cumbersome than it should be. A Plex-like web interface would be better, but it is not a deal-breaker.

The core experience is what matters: same library, clean metadata, Apple TV and iOS support, cloud when I am away, local when I am home.

Watching the library

Infuse has brought my own media into parity with the way I buy and watch media now. Only having to switch between two apps, Infuse and the Apple TV app, both with cloud access, has simplified what could have become a mess of a library.

Movies Anywhere is another thing I still cannot quite believe exists, mostly because I cannot believe the studios agreed to it. But it has been the final piece for consolidating scattered purchases inside Apple's TV app.

The setup still takes maintenance, but I can now find and play my own files alongside my digital purchases.

My hope now that this is all worked out is that the organization obsession will subside a bit, at least until the next shiny thing shows up, so I can finally spend more time watching.

Photography Workflow 2018

Fri, 24 Aug 2018 00:00:00 GMT

Hotel du Collectionneur | 51-57 Rue de Courcelles | 75008 Paris, France

Incremental Improvement

This year brought a ton of tweaking to my photography workflow. Most of it was focused on backups, but the whole thing was sparked by new gear and realizing that my old system was held together by habit, anxiety, and duplicate files.

It still does not feel quite there yet, but it is a vast improvement over how I started 2018. The goals are simple: protect the RAWs, make the JPGs easy to browse and share, and reduce enough chaos that I keep shooting and editing.

In-Camera

I made the jump from a Sony Alpha 7R to a Sony Alpha 7R III and could not be happier. It took me a while to figure out how I wanted to use dual-card slots. Right now, I am writing RAWs to the fast slot and Extra Fine JPGs to the slower slot.

I have read a ton of posts about this kind of configuration, both positive and negative. Let me be clear: I edit in RAW only. If I lost the RAWs, I would be devastated. The JPGs are bonus.

If I were out shooting and somehow ran out of space on the primary card, I would not think twice about formatting the JPG card and using it for a second batch of RAWs. That has not happened yet, but it would be a no-brainer. The RAWs are the work. The JPGs are useful, but expendable if they need to be.

Back Home (or, "wherever the laptop is")

Once I am done shooting for the day and back wherever the laptop is, I plug in the camera via USB-C.

I love this about the a7R III. The transfers are so fast, and I never have to take the cards out. That sounds minor until you have burned yourself by forgetting to put a card back in the camera, or by losing images because of some weird card reader issue. No more.

Once the files are transferred from the camera, I back up the RAWs to a SanDisk 500GB portable SSD if I am on the road. If I am at home, they go directly to a Synology DS216J NAS.

That is the first pass: get the RAWs somewhere safer than the camera. Everything after that is redundancy and access.

Cloud Services (or, "this is where things get complicated")

This is where the JPGs come into play. The batch gets uploaded to Google Photos.

There are a few reasons this works for me, but the main point is that I can see everything I shot on multiple devices. I can preview images anywhere and share unedited selects with family, especially with a 2-year-old with grandparents on different continents.

I should also mention that I have G Suite set up on my own domain, which includes unlimited Google Drive storage. I still do not believe this is real and not some kind of mistake Google made during setup, but it is real enough that I am using it.

Given that, my 16TB NAS is kept in sync with Google Drive using Synology's Cloud Sync app. If I move files over at home, they are backed up to the cloud automatically. If I am on the road with a fast connection, I can upload the day's shoot to Drive and know it will be on a drive in my closet shortly thereafter.

Going back to Google Photos, I have since corrected an issue that earlier-me implemented without thinking long term. Just because Google Photos allows you to upload RAWs does not mean that you should.

Uploading RAWs to Google Photos made my backups twice as big as they needed to be: Google Drive plus Google Photos, both holding the same giant originals. It also made recovery painful. Downloads are limited to 500 pictures at a time, transfer speeds are slow compared with Drive, and any download approaching or larger than 3GB would frequently error out.

There is just no need to upload RAWs to Google Photos. I was being lazy. Everything I shoot now has an associated JPG already. Everything from the past that does not have one is an overnight Photoshop batch process away from being JPG-ready.

I really love that I can drop a shoot folder on the NAS, or upload it to Google Drive on the road, and not think too hard about the redundant backup that starts from there. That is the good version of the complexity.

Work Time

Once everything is backed up or on its way to multiple places at the same time, the nervous feeling in my gut subsides and I can start editing.

I am an almost full convert to Lightroom CC. There are still some things I need to do in Lightroom Classic, but Lightroom CC is my preferred photo editing application now.

The main reason is catalog management. I still have random Lightroom catalogs strewn about on various drives with no recollection of what they contain or which photo repository they are associated with. Those days are over now as far as I am concerned.

I happily pay Adobe $29.99/month for the photography plan plus 2TB of storage. I am at around 75% capacity now and slightly dreading the 2x price jump to 5TB, but at the end of the day it has been worth it.

I can edit on my desktop, laptop, and phone using the same catalog, with instant updating between them all. Post work can happen anywhere because I am not constrained by a main catalog on my desktop at home.

I am slightly worried about needing more than 2TB of storage from Adobe. Since I started taking pictures, I have operated under the rule of "delete nothing, keep everything." I have calmed down on that only over the recent months.

If everything is backed up to both local and cloud storage, I am realizing that I do not need to keep every single shot in Lightroom. This was tough to adopt at first, but it has helped me take a more critical eye to what I captured and cull more efficiently.

Shooting on film slowed me down for many years. As soon as I pick up a digital camera, though, I start firing away like a madman. Being conscious of a post-processing workflow forces me to take a step back in the heat of shooting and think more about not just what I am capturing, but how many times I am capturing it.

This is the first crack in the old rule. Keeping everything felt responsible. Now I am starting to think keeping everything everywhere creates its own mess.

Posting Online

I have read a lot of inspiring posts by Samuel Zeller, a Swiss photographer and really the inspiration for this blog and the most recent incarnation of my portfolio site. His archive convinced me that having a public place for photographs matters.

I usually post my favorite shots to Instagram and, at the time, Ello. But I loved the idea of having full-res JPGs with all of the relevant metadata available somewhere online. The social feed is one thing. An archive is another.

So I took that advice and created an archive using SmugMug. It has allowed me to revisit work from the past: shots I used to love that I now think are horrible, and shots I ignored that feel better with distance.

It also helps limit the number of images I show in my portfolio, which is currently 12 and never more than 15. Instead of fighting the voice in my head that says, "oh, you gotta put this image in there as well," I can appease it by knowing there is a place for everything. Images that do not make it into the portfolio are not bad. They are just not the best.

In addition to those posting channels, I am also a big fan of The Print Swap. I am lucky to have had 2 of my photos featured so far, and in return I received 2 different prints from photographers around the world. There are so many people doing amazing work, and I love that The Print Swap slows this exposure down to the physical world.

That's It

I think that is everything. I know by this time next year things will most likely have changed again, but as of right now, this is my workflow.