we found opus 5.5 to be 2X as efficient as gpt-6-sol in our internal cfo.ai evals so i was not expecting 6.1-sol to improve on that much but to see it 3X the efficiency of opus 5.5 and 2.5X against even sonnet 5.5 is actually 🤯 this is an insane point release

I open-sourced Dioramas, a framework for cinematic, interactive 3D websites. It's 100% free. No paid tier, no signup. It comes with 20 full example sites, each built around one interaction you can touch: a museum you explore by lantern, a mech you power up, a geode you crack open. It's powered by fal: every 3D model was generated with Meshy 7.1 from Nano Banana 2 images. Every prompt, model and line of code is in the repo. Repo: github.com/blendi-remade/dio…

ALERT 🚨🚨OpenAI's latest GPT6.1 Sol Ultrafast is NOT running on Cerebras but is instead running at a low batch size on NVIDIA GPUs. What does this say about Cerebras? Will Cerebras be serving GPT6.1 Sol Ultrafast in the future?

New blog post on speeding up the separating axis test (SAT) using inscribed spheres: box2d.org/posts/2026/09/insc…

GG !

Aucune idée. Ça a l'air bien !

you're not crazy between anthropic and openai, a new model used to come out every ~10 weeks now it's every ~11 days

Looks Very suspicious. Or it's just a jev like model...

Artificial Analysis numbers are in! GPT-6.1 Sol is performing at around Opus 5.5 Medium levels for under a third the price. Not bad at all, but still more hyped for 6.1 Astra to catch them up 👀

One interesting @OpenAI detail: researchers let models continuously optimize the computer-use harness itself, then feed the successful improvements back into training. OpenAI says this has already cut latency by more than 2×. AI helping improve the systems used to train AI is getting very concrete.

OpenAI just announced Ultrafast a model that can run up to 300tok/s ! 8x faster . I’ve been testing the new Ultrafast for some time ! I had two agents playing Tekken on PS1 just by looking at images. They made one decision every 0.95 seconds. That enables for the first time near-real-time, high-intelligence interactions. This unlocks a bunch of new use cases!! 🚀

That’s a great price reduction but what I would also love see is how I can delegate the choice of the right-for-task model to an automated routing.

🚨 BREAKING: Super excited to announce we're deploying the world's fastest inference with @Cerebras. Talk to any developer and they're excited to build with 20x faster AI... the problem is there's almost no compute available. We're here to solve that. Using GPUs for prefill - it's now more affordable than ever too. Thank you to the whole Cerebras team and excited to grow this partnership. First tokens live Q127 🚀

insane thing: we are currently losing money on openai pro subscriptions! people use it much more than we expected.

Ouch... Just as today is openAi dev day.

Or Google search ?

The "Russia is not my enemy" was a great campaign in terms of research. By scraping all these posts, analyzing their followers, and turning it into a visualization, we now have a very good and clear image of the pro-Kremlin network operating on this platform.

Oui tiens le lien github : github.com/bmad-code-org/BMA… Quand tu l’installes tu peux sélectionner « bmad game dev studio », c’est un lot d’agent, de skills, de workflow pour faire au propre ton projet avec un suivi via des fichiers .md etc.. tu as juste à te laisser guider ensuite

Netflix replaced their 15 years old recommendation algorithm with an LLM. It’s called "GenRec" and it completely changes how recommendation algorithms are built. For over a decade, the Netflix recommendation engine was a masterclass in feature engineering. Data scientists built thousands of complex, handcrafted features to figure out what you wanted to watch next. It required bespoke architectures. Massive infrastructure. Constant manual tuning. Netflix threw all of it away. They built GenRec, an LLM-backed ranker. Instead of translating your behavior into complex math, they just turn your watch history and metadata into a natural language sentence. They feed that raw text into a foundation LLM. The AI simply reads your behavior like a story, understands your evolving tastes, and scores the entire catalog in a single forward pass. No manual feature engineering. No complex bespoke architectures. Here is the part that should terrify traditional data scientists. This text-based LLM didn't just match the highly tuned production system Netflix spent years perfecting. It beat it. And it achieved those statistically significant gains using roughly 40x fewer labeled training examples. We are watching a massive paradigm shift in real time. The most complex predictive algorithms in the world are being replaced by models that just know how to read. If Netflix can replace their core product engine with an LLM, what complex system in your business is about to become obsolete?

"People won't play games made with a.i." - every anti-ai hater as they cope The data: