Guy Gur-Ari (@guygr) profile picture

Physicist, Machine Learner

Mountain View, CA
Joined July 2008
Photo by Guy Gur-Ari (@guygr)Photo by Guy Gur-Ari (@guygr)Photo by Guy Gur-Ari (@guygr)Photo by Guy Gur-Ari (@guygr)
Claude Sonnet 4.5 is now the default model for Augment Code. We’re rolling it out to all customers over the next 24 hours, where it will be available alongside Sonnet 4 (for a limited time) and GPT-5 in the model picker. Here’s how Sonnet 4.5 compares to Sonnet 4:
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Introducing Remote Agent - cloud-based dev agents with full-codebase context, deep IDE integration, and full toolchain access. Available in VS Code.
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Augment Agent is now live. Designed for large-scale codebases — with 200K context tokens, persistent memory, and deep tool integrations, Augment Agent is available in VS Code and JetBrains.
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Just bumped into @jyangballin on Stanford campus and discussed future of coding agents and swebench/sweagent. Very happy such smart and kind folks are working on this important problem with an open-source POV. The future is bright ☀️ Strongly believe open source is critical puzzle piece in AI safety story over the coming decade. To play our part, my research colleagues at Augment and I will begin by posting a blog post soon detailing all our learnings and hard-learned discoveries building coding agents for swebench
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Working on open source is hard. We just made it a little easier. Augment is free for open source contributors and maintainers. 🎉 Augment’s deep codebase understanding tears down some of the biggest pains for working on open-source projects– the context you need to contribute and the help you need to make quality PRs. Sign up at augmentcode.com/opensource
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AI coding tools are everywhere. Yet they all fall short when scaling to 100s of developers and complex codebases. We’re solving this problem @augmentcode. Introducing Augment Code, the first developer AI purpose-built for teams.🧵
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Guy Gur-Ari retweeted
Happy to release our work on Language Model Cascades. Read on to learn how we can unify existing methods for interacting models (scratchpad/chain of thought, verifiers, tool-use, …) in the language of probabilistic programming. paper: arxiv.org/abs/2207.10342
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Guy Gur-Ari retweeted
1/ Super excited to introduce #Minerva 🦉(goo.gle/3yGpTN7). Minerva was trained on math and science found on the web and can solve many multi-step quantitative reasoning problems.
Very excited to present Minerva🦉: a language model capable of solving mathematical questions using step-by-step natural language reasoning. Combining scale, data and others dramatically improves performance on the STEM benchmarks MATH and MMLU-STEM. goo.gle/3yGpTN7
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Very excited to announce a significant milestone in expanding reasoning capabilities of language models! 🎉🎉 We introduce #Minerva🦉: a language model that can solve mathematical questions using step-by-step natural language reasoning: bit.ly/3OBj2d5 🧵 1/
Very excited to present Minerva🦉: a language model capable of solving mathematical questions using step-by-step natural language reasoning. Combining scale, data and others dramatically improves performance on the STEM benchmarks MATH and MMLU-STEM. goo.gle/3yGpTN7
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Thrilled to announce🦉Minerva: a large language model capable of solving mathematical problems using step-by-step reasoning in natural language. See blog here: goo.gle/3yGpTN7 and samples here: minerva-demo.github.io (1/n)
Very excited to present Minerva🦉: a language model capable of solving mathematical questions using step-by-step natural language reasoning. Combining scale, data and others dramatically improves performance on the STEM benchmarks MATH and MMLU-STEM. goo.gle/3yGpTN7
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Super excited to share Minerva!! – a language model that is capable of solving MATH with 50% success rate, which was predicted to happen in 2025 by Steinhardt et. al. (bounded-regret.ghost.io/ai-f…)! #Minerva 1/
Very excited to present Minerva🦉: a language model capable of solving mathematical questions using step-by-step natural language reasoning. Combining scale, data and others dramatically improves performance on the STEM benchmarks MATH and MMLU-STEM. goo.gle/3yGpTN7
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Guy Gur-Ari retweeted
Very excited to present Minerva🦉: a language model capable of solving mathematical questions using step-by-step natural language reasoning. Combining scale, data and others dramatically improves performance on the STEM benchmarks MATH and MMLU-STEM. goo.gle/3yGpTN7
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Guy Gur-Ari retweeted
Language models have shown strong performance on a variety of #NLU tasks but are weaker at solving tasks that involve quantitative reasoning. Learn how #Minerva uses step-by-step reasoning to achieve a new state of the art on quantitative reasoning tasks→ goo.gle/3yGpTN7
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imo a major AI safety contribution, both in short-term (applications) and long-term (AGI) scope
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Guy Gur-Ari retweeted
This is incredible work on LLMs! Reading through this paper, I'm not only amazed by the huge amount of work behind BIG-bench, but also fascinated by the observations of current LLMs' (BIG-G, GPT, PaLM) behaviors on those tasks. A few things that I felt deeply resonated:
After 2 years of work by 442 contributors across 132 institutions, I am thrilled to announce that the github.com/google/BIG-bench paper is now live: arxiv.org/abs/2206.04615. BIG-bench consists of 204 diverse tasks to measure and extrapolate the capabilities of large language models.
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Thanks @LiamFedus @AJAndreassen @jaschasd @ethansdyer @guygr and team for the incredible work on BigBench ! You can find it on the Hugging Face Hub here 👉 hf.co/datasets/bigbench
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Today we release our paper in response to @ericweinstein's "Theory of Everything" #geometricunity: timothynguyen.files.wordpres… Our objections:
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In the Les Houches School of Physics, established in the wake of WWII, a *in-person* workshop on Physics & Machine Learning starting today: leshouches2020.krzakala.org Feels eerie, but so desired. Never had I imagined to have to restrict participation to European residents ...
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This was put in place to prevent backdoor access to the OpenAI API. Additionally we have finetuned on a specific dataset and use parameters optimized for our use case making AI Dungeon not necessarily representative of GPT-3 in general.
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I've noticed a number of people using AI Dungeon to test GPT-3's abilities. While it's a great way to see how GPT-3 can power an interesting application. It's a poor test of GPT-3's abilities in general. The first generation of any custom prompt is actually GPT-2.
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