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
TOOLS
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Scobleizer compares three dictation apps: Mostly Typeless, Wispr, Monologue.
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Mostly Typeless since that's what I started with. Wispr is more popular. Monologue is newer and I'm trying to see if it really is any better than the other two.
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Wispr Flow, Typeless, Monologue: stunningly good speech-to-text
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Wispr Flow or Typeless or Monologue. They are way better than anything else. It's hard to tell the difference between these three. They are stunningly good.
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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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Anthropic acquires Vercept, xAI focuses on computer use agents for 2026
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– Anthropic acquires Vercept
– xAI's MacroHard heavily focuses on Computer use 2025 was the year of agents in CLI
2026 is shaping up to be the year of computer use agents. -
AI Agent Swarms Used for OSINT Data Reconstruction
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God's eye view 24-hour replay of Operation Epic Fury.
— Bilawal Sidhu (@bilawalsidhu) 1 mars 2026
The Iran strikes kicked off and I set an AI agent swarm loose to record every OSINT signal I could find before the caches cleared. Built a full 4D reconstruction in WorldView.
I can scrub through minute by minute and watch… pic.twitter.com/W8NnqSPxVYGod's eye view 24-hour replay of Operation Epic Fury. The Iran strikes kicked off and I set an AI agent swarm loose to record every OSINT signal I could find before the caches cleared. Built a full 4D reconstruction in WorldView. I can scrub through minute by minute and watch
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Query about AI writing mastery versus daily volume
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Have you nailed the AI assisted writing or you just churn out 10 of these a day?
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Modular Agentic RAG system with LangGraph and HITL
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Build a modular Agentic RAG system with LangGraph, conversation memory, and human-in-the-loop query clarification using this GitHub repo:
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Anthropic Launches Memory Import from ChatGPT and Gemini
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Anthropic absolutely nailed this. So many people will start using Claude. New feature lets you import your *entire* memory from ChatGPT, Gemini etc into Claude so it *instantly* knows everything about you. No more reminding Claude who you are. The best fucking part is it takes literally 60 seconds: – Copy and paste the below prompt into your alternative AI (e.g. ChatGPT) – Paste answer into Claude's "memory" settings and… you're done. – Claude immediately picks up from the last conversation you had with it in ChatGPT! The opportunity cost to switch to Anthropic just went to zero – their app is currently #1 in the app store — Greg (@GregFeingold) Ready to make the switch? claude.com/import-memory [Translated from EN to English]
→ View original post on X — @flashtweet, 2026-03-01 03:34 UTC