Geoffrey Angus

151 posts

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Geoffrey Angus

Geoffrey Angus

@GeoffreyAngus

Building stuff. Formerly @Google, @Stanford.

San Francisco, CA 가입일 Kasım 2015
354 팔로잉199 팔로워
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Zvi Mowshowitz
Zvi Mowshowitz@TheZvi·
They're burying a lot here. There's a 66% price cut from Opus 4.1 to $5/$25, it uses fewer tokens to solve problems, upgrades to Claude Code in the app, no more length limits on conversations, no more Opus-specific plan caps...
Claude@claudeai

Introducing Claude Opus 4.5: the best model in the world for coding, agents, and computer use. Opus 4.5 is a step forward in what AI systems can do, and a preview of larger changes to how work gets done.

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Claude
Claude@claudeai·
Introducing Claude Opus 4.5: the best model in the world for coding, agents, and computer use. Opus 4.5 is a step forward in what AI systems can do, and a preview of larger changes to how work gets done.
Claude tweet media
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Pierce Freeman
Pierce Freeman@piercefreeman·
two million tokens is such a crazy large amount of context (it's basically all of Shakespeare's known works) until you start trying to have it parse logs then you might as well be holding a magnifying glass up to an encyclopedia
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sam mcallister
sam mcallister@sammcallister·
GOOD MORNING NEW YORK CITY COME DO YOUR BEST THINKING AT OUR THINKING SPACE IN THE WEST VILLAGE SAY NO TO SLOP
sam mcallister tweet mediasam mcallister tweet mediasam mcallister tweet mediasam mcallister tweet media
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anton 🇺🇸
anton 🇺🇸@atroyn·
it’s difficult not to make the aesthetic comparison between the claude “keep thinking” ads and the sora 2 launch
Claude@claudeai

Keep thinking.

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Sabri Eyuboglu
Sabri Eyuboglu@EyubogluSabri·
@StanfordHAI just ran this story on self-study and cartridges -- it's a really nice overview for those curious about our work
Sabri Eyuboglu tweet media
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Ethan Mollick
Ethan Mollick@emollick·
I had some early access to Sonnet 4.5. It is a really good model. I saw especially big jumps in doing finance and statistics, which tend to get overlooked in the focus on coding.
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Sholto Douglas
Sholto Douglas@_sholtodouglas·
Incredible work - this should immediately become one of the most important metrics for policy makers to track. We’re probably only a few months from crossing the parity line. Huge props to OAI for both doing the hard work of pulling this together and including our scores. Nice to see Opus on top :)
Sholto Douglas tweet media
Tejal Patwardhan@tejalpatwardhan

Understanding the capabilities of AI models is important to me. To forecast how AI models might affect labor, we need methods to measure their real-world work abilities. That’s why we created GDPval.

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Lisan al Gaib
Lisan al Gaib@scaling01·
Tri Dao says Claude Code makes him 1.5x more productive and that it's quite helpful at writing Triton kernels
Lisan al Gaib tweet media
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Claude
Claude@claudeai·
Keep thinking.
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Thariq
Thariq@trq212·
The Claude Code SDK now supports custom tools and hooks directly in code. Additionally, we’ve refreshed all our docs with complete references and 10 new guides on how to utilize the SDK.
Thariq tweet media
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shreya rajpal
shreya rajpal@ShreyaR·
Introducing ❄️ @snowglobe_so, the simulation engine for AI chatbots. Magically simulate the behavior of your users to test and improve your chatbots. Find failures before your users do.
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will brown
will brown@willccbb·
cant stop thinking about this one insanely elegant, seems insanely powerful
will brown tweet media
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Together AI
Together AI@togethercompute·
Announcing DeepSWE 🤖: our fully open-sourced, SOTA software engineering agent trained purely with RL on top of Qwen3-32B. DeepSWE achieves 59% on SWEBench-Verified with test-time scaling (and 42.2% Pass@1), topping the SWEBench leaderboard for open-weight models. Built in collaboration with the @Agentica_ team. 💪 DeepSWE is trained with rLLM, Agentica’s modular RL post-training framework for agents. rLLM makes it easy to build, train, and deploy RL-tuned agents on real-world workloads — from software engineering to web navigation and beyond. 🤗 As always, we’re open-sourcing everything: not just the model, but the training code (rLLM), dataset (R2EGym), and training recipe for full reproducibility. 🔥 Train DeepSWE yourself. Extend it. Build your own local agents. No secrets, no barriers. DeepSWE and rLLM mark our major shift: from training language reasoners to building language agents that can truly learn from experience. We believe the future of AI lies in experience-driven learning — and we’re here to democratize it. Welcome to the era of experience. 🌍
Together AI tweet media
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Pierce Freeman
Pierce Freeman@piercefreeman·
Text diffusion models might be the most unintuitive architecture around Like: let's start randomly filling in words in a paragraph and iterate enough times to get something sensible But now that google's gemini diffusion is near sota, I think we need to take them seriously
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Dylan Patel
Dylan Patel@dylan522p·
The Nvidia Tensor Core is the most important evolution of computer architecture in the last decade We explain why / how it's evolved Shout out to collaborators @bfspector @tri_dao @colfaxintl @charles_irl @ia_buck Neil Movva Jonah Alben esp @simonguozirui for the cutest cover pic
SemiAnalysis@SemiAnalysis_

NVIDIA Tensor Core Evolution From Volta To Blackwell Amdahl’s Law, Strong Scaling Asynchronous Execution Blackwell, Hopper, Ampere, Turing, Volta semianalysis.com/2025/06/23/nvi…

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