Ben Affleck being an AI-pilled founder was not on my bingo card for 2026. Did you know Netflix bought his AI startup for $586M in March? đ€Ż
Ben Affleck reveals he writes Python, understands convolutional neural networks, worked extensively with GPUs, and used his celebrity status to get private looks at Google and OpenAIâs video models
âIâve always been kind of into computers since I was young. Then, when film started to move from analog film to digital, I became more interested in that aspect of it. The visual-effects workflow for many years has included machine learning, so I can write pretty shitty Python scripts and stuff like that.
âWith convolutional neural networks, which were the precursors to what the transformer can do, which is much more computation simultaneously, you would do things like look at whatâs called a tensor. Thatâs the numerical translation of a visual image in numbers, like the batch number, the frame number and the red, green and blue values of each pixel in each frame. Itâs just that simple. That numeric is called a tensor.
âYouâd use a convolutional neural network to identify patterns that reveal whatâs called edge detection or feature extraction, which is identifying patterns well enough to know, this is where the window ledge is, so we can more easily take the green-screen image out and replace it with something.
âThat was familiar to me early on because, prior to Artists Equity, I had a small visual-effects company. Iâve worked with GPUs a lot too. The visual-effects guys said, âHey, you should see. There are a couple: Google and this other company, OpenAI, are doing really interesting stuff with transformers in video.â
âIâve learned that I can actually just call up and go, âHey, itâs Ben Affleck. Can I come see what youâre doing?â Sometimes people say yes, to my astonishment.â
Moti Tabulo retweeted
Itâs easy to dismiss Jev it as âjust a classifierâ.
But people (me included) who have been training encoder-style models for classification for many years know they were usually special-purpose and limited in some way.
The breakthrough of Jev is that it generalizes well.
And Iâd say the secret sauce is probably more in the data than in the training algorithm. (Plus a nice API design on top of it.)
poor guy claim to have built Jev a year ago but no one cared, and now Jev stole all the thunder
many people are saying âyou gotta tell your storyâ or âmarketing is importantâ, and they just completely missed what actually made the difference here
i just looked into this laya model laya.convaiinnovations.com/ and:
- it only supports 512-1k context⊠a lot of use cases wonât fit at all
- evaluating the model directly shows its accuracy is as good as a coin flip. in order to get good results, you need to first fine tune it
iâm sorry, but thatâs not Jev
thereâs a massive gap between an interesting research and a useful product
you can âtell your storyâ all you like, but you canât blame Jev for stealing your thunder when Jev did all the work to make a well-packaged solution anyone can just grab and go
Jev is not completely new from an academic sense, just like how ChatGPT was not the first LLM
donât underestimate the effort and value in putting together something thatâs actually good enough for adoption - it makes all the difference
Iâm 45.
Solo builder from Cambridge, UK.
Looking to connect with other builders and AI enthusiasts!
I tagged all 421 replies to this across 358 accounts.
108 want more automated review.
93 ration human attention by risk.
63 replaced inspection with evidence.
33 review the plan before the diff.
I went through all 400+ replies to Gergely Orosz's question about reviewing code that agents wrote. 350+ accounts.
The argument I was expecting is humans versus AI, but that's not actually what's happening.
In fact, the unit of review is changing.
Those leaning on AI reviewers have their own problem: AI reviewers are too willing to find things, and noise becomes its own bottleneck.
The most-liked reply in all responses is @jacobtechtavern, on a crusade against AI review comments. Their fix is another agent filtering them.
The real question per change is how much assurance it needs. @abhirb already routes on it: low-risk auto-merges, security-sensitive gets a human.
Code isn't the scarce resource anymore, it's confidence.
So the future of code review? Spend human attention only where it matters.
Moti Tabulo retweeted
This is genuinely wild. đ€Ż
A Japanese AI lab just built a model that matches Claude Fable 5 performance.
It's called Sakana Fugu, and it was built in Tokyo by a co-author of the original Transformer paper.
The guy who helped invent the architecture every AI runs on just built something that sits on top of all of them.
But here's the twist. Fugu isn't one giant model. It's a coordinator. You send one API call, and it decides which frontier model handles which part of the problem, runs them together, and hands back a single answer better than any of them could produce alone.
SWE-Bench Pro: Fugu Ultra scores 73.7. Claude Opus 4.8 scores 69.2. GPT-5.5 scores 58.6.
And because the model pool is swappable, if any provider gets export-controlled overnight, Fugu just routes around it.
Introducing Sakana Fugu: A full multi-agent orchestration system accessible via a single model API.
Our âFugu Ultraâ model matches the performance of Fable and Mythos, delivering frontier capability without the risk of export controls.
Try it: sakana.ai/fugu đĄ
Moti Tabulo retweeted
The US government, citing national security authorities, has issued an export control directive to suspend all access to Fable 5 and Mythos 5 by any foreign national, whether inside or outside the United States, including foreign national Anthropic employees.
The net effect of this order is that we must abruptly disable Fable 5 and Mythos 5 for all our customers to ensure compliance.
Access to all other Claude models is not affected.
We apologize for this disruption to our customers. We believe this is a misunderstanding and are working to restore access as soon as possible.
Read our full statement: anthropic.com/news/fable-mytâŠ














