biagio

building AI systems at @Revolut

[biagio @AntonelliBiagio]

@antirez I guess they already started :D

[Mario Zechner @badlogicgames]

I wrote up how I built the shitty robot so you can too. This was a fun project that will keep on giving.

Thanks to all the open weights folks out there, without whom this would not have been possible.

https://t.co/egPxlCECQg

[hardmaru @hardmaru]

For over a decade, we’ve accepted that end-to-end backprop is the only way to train deep networks. But holding the entire network in memory all at once is why AI training is hitting a resource wall.

We found a new way to break the network into blocks and train them independently. The trick? Treating the network’s forward pass like a diffusion model denoising a signal.

This reinterpretation slashes the memory needed to train deep models. In our #ICLR2026 paper (https://t.co/PK5h0mqQSo), we matched end-to-end performance across ViTs, DiTs, and LLMs. We did this while training just one isolated block at a time.

[Zain Shah @zan2434]

Imagine every pixel on your screen, streamed live directly from a model. No HTML, no layout engine, no code. Just exactly what you want to see.

@eddiejiao_obj, @drewocarr and I built a prototype to see how this could actually work, and set out to make it real. We're calling it Flipbook. (1/5)

[antirez @antirez]

When you hit the gym in 2026 leaving your buddy continuing the work.

[biagio @AntonelliBiagio]

coolest agent out there!

[Revolut @Revolut]

Meet AIR.

We’re rolling out gradually to UK customers first, with more countries launching soon.

[antirez @antirez]

The difference between the two, for serious engineering work, is simply brutal. Claude Code with Opus is, when the task at hand is very complicated, borderline useless, while GPT 5.4 can do a reverse engineering mixing: hardware knowledges, major disassembly skills, and so on.

[Lenny Rachitsky @lennysan]

"Using coding agents well is taking every inch of my 25 years of experience as a software engineer, and it is mentally exhausting.

I can fire up four agents in parallel and have them work on four different problems, and by 11am I am wiped out for the day.

There is a limit on human cognition. Even if you're not reviewing everything they're doing, how much you can hold in your head at one time. There's a sort of personal skill that we have to learn, which is finding our new limits. What is a responsible way for us to not burn out, and for us to use the time that we have?" @simonw

[Kimi.ai @Kimi_Moonshot]

Congrats to the @cursor_ai team on the launch of Composer 2!

We are proud to see Kimi-k2.5 provide the foundation. Seeing our model integrated effectively through Cursor's continued pretraining & high-compute RL training is the open model ecosystem we love to support.

Note: Cursor accesses Kimi-k2.5 via @FireworksAI_HQ ' hosted RL and inference platform as part of an authorized commercial partnership.

[Sebastian Raschka @rasbt]

I (finally) put together a new LLM Architecture Gallery that collects the architecture figures all in one place!
https://t.co/NO7z6XSRHS

[Andrej Karpathy @karpathy]

I had the same thought so I've been playing with it in nanochat. E.g. here's 8 agents (4 claude, 4 codex), with 1 GPU each running nanochat experiments (trying to delete logit softcap without regression). The TLDR is that it doesn't work and it's a mess... but it's still very pretty to look at :)

I tried a few setups: 8 independent solo researchers, 1 chief scientist giving work to 8 junior researchers, etc. Each research program is a git branch, each scientist forks it into a feature branch, git worktrees for isolation, simple files for comms, skip Docker/VMs for simplicity atm (I find that instructions are enough to prevent interference). Research org runs in tmux window grids of interactive sessions (like Teams) so that it's pretty to look at, see their individual work, and "take over" if needed, i.e. no -p.

But ok the reason it doesn't work so far is that the agents' ideas are just pretty bad out of the box, even at highest intelligence. They don't think carefully though experiment design, they run a bit non-sensical variations, they don't create strong baselines and ablate things properly, they don't carefully control for runtime or flops. (just as an example, an agent yesterday "discovered" that increasing the hidden size of the network improves the validation loss, which is a totally spurious result given that a bigger network will have a lower validation loss in the infinite data regime, but then it also trains for a lot longer, it's not clear why I had to come in to point that out). They are very good at implementing any given well-scoped and described idea but they don't creatively generate them.

But the goal is that you are now programming an organization (e.g. a "research org") and its individual agents, so the "source code" is the collection of prompts, skills, tools, etc. and processes that make it up. E.g. a daily standup in the morning is now part of the "org code". And optimizing nanochat pretraining is just one of the many tasks (almost like an eval). Then - given an arbitrary task, how quickly does your research org generate progress on it?

[Boris Cherny @bcherny]

@EthanLipnik 👋 Early versions of Claude Code used RAG + a local vector db, but we found pretty quickly that agentic search generally works better. It is also simpler and doesn’t have the same issues around security, privacy, staleness, and reliability.

[Simon Willison @simonw]

"This is a 600X cost reduction over 7 years, i.e. the cost to train GPT-2 is falling approximately 2.5X every year."

[biagio @AntonelliBiagio]

@tunguz yes

[biagio @AntonelliBiagio]

@bryan_johnson what about using claude code from toilet?

[hardmaru @hardmaru]

Amazing that @SchmidhuberAI gave this talk back in 2012, months before AlexNet paper was published.

In 2012, many things he discussed, people just considered to be funny and a joke, but the same talk now would be considered at the center of AI debate and controversy.

Full talk:

[mitsuri @0xmitsurii]

Jensen Huang: I want my engineers to stop coding.

[rohit @krishnanrohit]

Guess who's vibe coding in 2026 ... Linus Torvalds, using Google's Antigravity. It's over.

[biagio @AntonelliBiagio]

@gonzalo_io just with claude code from ios claude app. Push directly PRs from there, review on github mobile app, and have a github workflow to deploy to my hetzner server :D