Jack Cheng

Children’s author, pattern linguist, slow technologist. Senior editor + frontier team @Every. And new(ish) dad. Building lively worlds, real and imagined.

[Jack Cheng @jackcheng]

My new one for @every going into the weekend, about two favorite topics: woodworking and UI design (feat. @joshpuckett and @Wattenberger) https://t.co/UruRn3l9Wk

[Jack Cheng @jackcheng]

@kieranklaassen pronounced like “gif”

[Dan Shipper @danshipper]

I look at what I do with Fable 5.2 in the cracks of my week and think: this would've taken five people and $200,000.

Then I look at what my team does with the same AI.

Last week, between meetings, I analyzed our customer survey, made a game for our team, spotted an opportunity to sell @Every to teams, built a CRM, and started reaching out.

Look at that and you might ask: Why would I need a team at all?

The chart seems to agree. On OpenAI's GDPval benchmark, AI went from beating human experts 12.4% of the time in 2024 to around 83% in 2026. That's the aggregate view of the future of work: gather a lot of data and extrapolate a trend line.

But the aggregate misses something. To see it, you have to get intimate: zoom in on one real situation, with all its messiness, and extrapolate from there.

We gave everyone at Every access to the same customer survey and the same AI models.

• I found an opportunity to sell to teams.
• @poojary_yash saw that first-time subscribers were churning because we send too much email.
• @dabrundage noticed that many of our readers feel lonely: they're excited about AI, but people around them think they're crazy.
• @jackcheng turned the results into a Claymation game show.

Could you ask Claude to produce what each of them found? Maybe. But generating possibilities is cheap. Knowing which ones matter is hard.

@hammer_mt ran an experiment to predict what I'd say in Slack. When Claude got 400 attempts, it produced an answer like mine 93% of the time. But when it had to rank its own answers, mine landed in its top 10% only around 60% of the time.

Each person on a team is responsible for a different part of the world. That responsibility narrows where they look, and their perspective helps them decide what matters enough to act on. A team explores more possibilities than a single default prompt, and it can take responsibility for what it finds.

The obvious objection: models will get better at ranking. I think they will. But people aren't frozen while that happens. Comparing 2026 Yash with 2027 Claude misses that there will also be a 2027 Yash, with another year of experience, using 2027 Claude.

As models improve and spread, they raise the floor for everyone. The default answer becomes the baseline. The value is in using your perspective and responsibility to push past it.

That's why I think we need to change how we measure AI. Instead of only asking how well a model performs on problems everyone agrees are worth solving, ask: How good is it at doing my work, from my perspective?

The aggregate can show us the capability curve. The intimate helps us see what we might build, and who we might become, as we ride it.

I presented this at Thesis: 2027. The full talk is on the Every podcast:

[Jack Cheng @jackcheng]

Would you all watch a livestream of a few of us at @Every just hanging out for a few hours and playing some of these games?

[Jack Cheng @jackcheng]

We tested Fable 5.2 in early access October (and then very briefly again over the weekend) and I have never had more fun building stuff. It’s the first one that feels like a true creative collaborator. Here are some of the games I made

[Dan Shipper @danshipper]

BREAKING:

@AnthropicAI just dropped Claude Fable 5.2. It’s a paradigm-shifting model if you have a project ambitious enough to give it.

We’ve been testing it at @every, and I’ve seen this crazy explosion of creativity on the team. People are making their own creative software, games for their kids, and documentaries instead of slide decks.

Here’s your day zero vibe check:

• Coding: @kieranklaassen built Everycut, a video editor with the features we use from Premiere, Descript, and other tools—all in one app, with an agent built in. The first version was live in 3 days, and we immediately used it for our internal show-and-tell. This feels like a new era of professionals building their own professional tools, tailored to exactly how they work.

• Games and design: @jackcheng gave it a photo of his toddler’s messy playroom. It turned the actual toys into a playable racing game, with fun details like a stray shoe as an obstacle and a sippy cup as a power-up.

• Knowledge work: @hammer_mt turned his research into a documentary. The model came up with interview questions, he answered them on camera, and it cut the footage together. He called this the most productive 3 weeks of his career.

• A different way of working: Give it a big job and leave it running overnight or over a weekend. But tell it what “done” means: Mike found it could finish the build, then spend another day or two testing. Set the goal, the budget, and how much checking you need before walking away.

• The catch: It’s slow and burns through tokens. Our early-access usage was free, and these projects would have been expensive at API rates. It’s 20% cheaper than Fable 5.1, but for everyday writing, emails, or quick back-and-forth coding, I’d usually reach for something else.

The clearest signal: When our early access ended, @TylerNishida stopped working on some projects until he could get this model back. He didn’t want to build them with anything else.

The best measure of a new model is whether it makes you try things you wouldn’t have tried before.

Fable 5.2 absolutely does that.

Here’s our full day-zero Vibe Check:

[Jack Cheng @jackcheng]

A narrative game that teaches you about the history and business mechanical watches

[Jack Cheng @jackcheng]

Argon by comparison (though Carbon does a little better) https://t.co/tdzrNKonno

[Jack Cheng @jackcheng]

Another of my tests, starting with a photo of my toddler’s messy playroom: “Build a fun 3D racing game set in a child's playroom. It should have at least one creative game mechanic that sets it apart from other racing games.”

[Jack Cheng @jackcheng]

Here's the original reference photo

[Dan Shipper @danshipper]

BREAKING:

@Google just dropped Gemini 4 Carbon. It’s a much better coder than Argon—but I’m probably keeping Argon for writing.

We’ve been testing it at @every across coding, writing, design, and knowledge work. It can work for hours on an ambitious project, but it doesn’t always know when to stop.

Here’s your day-zero vibe check:

• Coding: On one run of our Senior Engineer benchmark, Carbon scored 44%, versus Argon’s 21%. A big improvement, though still middle of the pack. In a separate test, @hammer_mt gave it a backlog to work through. It identified 6 tasks as ready, then did 15—including work someone else was already doing. He stopped it after almost 4.5 hours.

• Big builds: @RandyCounsman gave it 3D scans of our podcast studio and an equipment list. In one attempt, it built a browser-based model accurate enough to check whether new gear would fit without getting into the shot. He estimates Carbon also wrote about 95% of a big update to Everycut, our internal video editor.

• Code quality: @kieranklaassen values its tests and documentation on large projects, including its record of approaches it considered and rejected. Fable remains his first choice, but Carbon has earned a place alongside it. The catch: It can produce huge files where Fable keeps things more modular.

• Audio and video: Randy found it caught audio editing issues other models missed. Kieran handed it a recorded meeting and asked for shorts. It came back with 6 titled clips.

• Design: @jackcheng got a hyperrealistic ginkgo-leaf app icon with details other models struggle with. Objects and 3D scenes look better than Argon’s. Interfaces split the team: Jack and Randy kept getting boxes within boxes, while @TylerNishida liked its ideas for new interfaces. Kieran says giving it design guidance makes a big difference.

• Writing: Our editorial benchmark put Carbon and Argon about even. If you like Argon, you’ll probably like Carbon. But Carbon costs more, and I haven’t seen enough improvement to justify switching.

My Reach Test: 🟨 I’ll use it sometimes.

Give it one big job with a clear finish line, and review what comes back. It’s also worth trying as a second opinion, especially on audio and video.

Full vibe check coming soon: https://t.co/Ch2pNl9Po5

[mike deal @dealville]

I designed + built an article for @Every, written by @danshipper

Here’s how “How Codex Happened” happened (visually).

[Dan Shipper @danshipper]

BREAKING:

@AnthropicAI just dropped Claude Fable 5.2. It’s a paradigm-shifting model if you have a project ambitious enough to give it.

We’ve been testing it at @every, and I’ve seen this crazy explosion of creativity on the team. People are making their own creative software, games for their kids, and documentaries instead of slide decks.

Here’s your day zero vibe check:

• Coding: @kieranklaassen built Everycut, a video editor with the features we use from Premiere, Descript, and other tools—all in one app, with an agent built in. The first version was live in 3 days, and we immediately used it for our internal show-and-tell. This feels like a new era of professionals building their own professional tools, tailored to exactly how they work.

• Games and design: @jackcheng gave it a photo of his toddler’s messy playroom. It turned the actual toys into a playable racing game, with fun details like a stray shoe as an obstacle and a sippy cup as a power-up.

• Knowledge work: @hammer_mt turned his research into a documentary. The model came up with interview questions, he answered them on camera, and it cut the footage together. He called this the most productive 3 weeks of his career.

• A different way of working: Give it a big job and leave it running overnight or over a weekend. But tell it what “done” means: Mike found it could finish the build, then spend another day or two testing. Set the goal, the budget, and how much checking you need before walking away.

• The catch: It’s slow and burns through tokens. Our early-access usage was free, and these projects would have been expensive at API rates. It’s 20% cheaper than Fable 5.1, but for everyday writing, emails, or quick back-and-forth coding, I’d usually reach for something else.

The clearest signal: When our early access ended, @TylerNishida stopped working on some projects until he could get this model back. He didn’t want to build them with anything else.

The best measure of a new model is whether it makes you try things you wouldn’t have tried before.

Fable 5.2 absolutely does that.

Here’s our full day-zero Vibe Check:

[Lee Knowlton @leeknowlton]

now that @every Thesis conference is over I need to actually watch the talks

thankfully have them all on Every TV https://t.co/Ny7Njwi3RW

[Katie Parrott @kplikethebird]

Only on @every can you find in-depth model reviews, actionable workflows, AND contemplations of the implications of Marxist theories of labor for the AI era.

[Loren @stew_loren]

I published my first article at @every!
https://t.co/KbeQ3jchLh

[Jack Cheng @jackcheng]

Fun fact: At Thesis next week each Every team member will be walking around with pockets full of these rocks to hand out as business cards*

*not really

[Dan Shipper @danshipper]

The bad news: Thesis: 2027 is sold out.
The good news: you can watch it all live, for free.

Next Thursday, join us for conversations with @joshm, @ivanhzhao, @ajambrosino, and a lot more. Plus, the Every team will be leading practical sessions on writing, engineering, and working with agents.

To celebrate, we’re giving away $20,000 in credits and benefits from partners like @AnthropicAI, @NotionHQ, and @OpenAI to lucky viewers who join the livestream.

Already an Every subscriber? You’ll still need to register for the livestream to win.

Register and watch free here: https://t.co/8ZtOU81D1i

[Jack Cheng @jackcheng]

100%

[Dan Shipper @danshipper]

video creation and game making are going to become widely distributed foundations of knowledge work in the same way doc and deck creation are today

[Jack Cheng @jackcheng]

@RandyCounsman @kieranklaassen When are we making a custom Android EveryOS?

[Mike Taylor @hammer_mt]

There's actually truth to this

[Jack Cheng @jackcheng]

Tapping the sign

[Jack Cheng @jackcheng]

One cool thing about working at @every is I get paid to write about my pet Star Wars theories https://t.co/x5tdIb7QJ0