Hugging Face:
https://huggingface.co/Qwen/Qwen3.5-0.8B [Translated from EN to English]
→ View original post on X — @arrakis_ai, 2026-03-02 13:04 UTC
OPEN SOURCE
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Qwen3.5-0.8B Now Available on Hugging Face
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Qwen3.5 Small Model Released on Hugging Face
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🚨 Breaking News: The Qwen3.5 small model has been released on Hugging Face.
→ View original post on X — @arrakis_ai, 2026-03-02 13:04 UTC
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joyfl v0.7: Stack Manipulation Puzzle Solver Framework
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joyfl — v0.7: Quotation Solver Newest release last week uses the existing search framework to find matching program fragments. It's like using a sudoku solver to discover solutions for stack manipulation puzzles, based on user-provided tests.
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Free Anthropic courses on Claude with certificates
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stop satisfying yourself with random youtube tutorials. Anthropic just released free courses on building with Claude. with certificates. prompt engineering, tool use, RAG, evaluations. built by the people who made the model. not some influencer who read the docs once. $0.00.
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Lean community contributions to proof tooling and Mathlib development
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RT @Robertljg: Honestly, a huge part of this is thanks to the incredible Lean community @leanprover ! Mathlib, proof tooling, and years of…
→ View original post on X — @animaanandkumar, 2026-03-02 00:12 UTC
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Lean Community Infrastructure Enables LLM Advancement in Proof
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Honestly, a huge part of this is thanks to the incredible Lean community @leanprover ! Mathlib, proof tooling, and years of infrastructure work made this possible —Lean is far more usable today because of that collective effort. And yes, LLMs have gotten much better at Lean lately 🙂 definitely helps accelerate learning and prototyping. Evan Chipman (@evanchipman) This is the 3rd time this week I thought “someone ought to make x” then open this app and see a team announce x. The speed of this era is disorienting. — https://nitter.net/evanchipman/status/2028157017597374837#m
→ View original post on X — @animaanandkumar, 2026-03-01 22:57 UTC
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TorchLean: First Fully Verified Neural Network Framework in Lean
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Super excited to release TorchLean!! I’m happy to answer questions and would love to discuss verified NNs + theorem proving especially what it’ll take for the field to become widely usable in real ML systems. Blog post + codebase release soon! Prof. Anima Anandkumar (@AnimaAnandkumar) We’re excited to release TorchLean which is the first fully verified neural network framework in Lean. The Lean community has largely focused on pure mathematics. TorchLean expands this frontier toward verified neural network software and scientific computing. With the recent release of CSlib, we see this as another step toward a fully verified ML stack. We support features: 1. Executable IEEE-754 floating-point semantics (and extensible alternative FP models) verified tensor abstractions with precise shape/indexing semantics 2. Formally verified autograd system for differentiation of NN programs Proof-checked certification / verification algorithms like CROWN (robustness, bounds, etc.) 3. PyTorch-inspired modeling API with eager-style development + export/lowering to a shared IR for execution and verification Project page: leandojo.org/torchlean.html Paper: [2602.22631] TorchLean: Formalizing Neural Networks in Lean Work done @Robertljg, Jennifer Cruden, Xiangru Zhong, @huan_zhang12 and @AnimaAnandkumar. #MachineLearning #ScientificComputing #Lean — https://nitter.net/AnimaAnandkumar/status/2027907453908857298#m
→ View original post on X — @animaanandkumar, 2026-03-01 22:38 UTC
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WiFi signals turned into radar to see through walls and estimate poses
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We just turned WiFi signals into a radar that can see through walls and estimate exact poses of people. Surveillance just got order of magnitude more easy todo. No need for cameras. Git hub repo close to 12k https://
github.com/ruvnet/wifi-de
nsepose
… https://
x.com/BoWang87/statu
s/2027941789848514643/video/1
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TorchLean: First Fully Verified Neural Network Framework in Lean
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We’re excited to release TorchLean which is the first fully verified neural network framework in Lean. The Lean community has largely focused on pure mathematics. TorchLean expands this frontier toward verified neural network software and scientific computing. With the recent release of CSlib, we see this as another step toward a fully verified ML stack. We support features: 1. Executable IEEE-754 floating-point semantics (and extensible alternative FP models) verified tensor abstractions with precise shape/indexing semantics 2. Formally verified autograd system for differentiation of NN programs Proof-checked certification / verification algorithms like CROWN (robustness, bounds, etc.) 3. PyTorch-inspired modeling API with eager-style development + export/lowering to a shared IR for execution and verification Project page: leandojo.org/torchlean.html Paper: [2602.22631] TorchLean: Formalizing Neural Networks in Lean Work done @Robertljg, Jennifer Cruden, Xiangru Zhong, @huan_zhang12 and @AnimaAnandkumar. #MachineLearning #ScientificComputing #Lean
→ View original post on X — @animaanandkumar, 2026-03-01 00:43 UTC