"Building Business-Ready Generative #AI Systems — Build Human-Centered Generative AI Systems with Context-Aware Agents, Memory, and LLMs for the Enterprise" at http://
amzn.to/3Jdcio5 v/ @PacktDataML Learn:
Implement an AI controller with a conversation AI agent and
MACHINE LEARNING
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Building Human-Centered Generative AI Systems for Enterprise
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Step-by-step Guide to Build, Optimize, and Scale LLM Agents
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The Most Complete #AI Agentic Engineering System — Step-by-step guide to build, optimize, and scale LLM agents: http://
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Practical Guide to Building AI Agents with LLMs and Knowledge Graphs
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"Building AI Agents with LLMs, RAG, and Knowledge Graphs — A practical guide to autonomous and modern AI agents" See it at http://
amzn.to/4622k2h via @PacktDataML -
From flying cars to 2M token context windows: a commentary on AI progress
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We were promised flying cars but all we got was 2m token context windows. :/
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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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Top AI models stay close in a fast-moving packed peloton
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Funny how the top models from different labs stay within a tiny distance from each other. The peloton is moving extremely fast, but stays quite packed, no breakaway so far. And that is despite large differences, e.g. in available compute. What's the forcing function that makes
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Technical inquiry on spatial and rotational accuracy in navigation systems
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that is damn impressive! is the sub 10cm accuracy just horizontal x, y or does it hold up for altitude estimation too? any rotational accuracy metrics or is this position only?
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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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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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Formal Verification for Neural Networks in Lean
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Verification is a cornerstone for reasoning. Recent progress in mathematical reasoning and theorem proving has relied on LLMs + formal verification in Lean. But so far, Lean has focused on pure math, and lacks support for verification involving neural nets themselves. Applications include: 1. Certified robustness in neural networks, crucial in safety critical applications such as neural control. 2. Physics informed neural networks, which have been used to prove singularity problems related to the Millennium prize problem in fluid dynamics. 3. Theory related to neural networks such as approximation bounds, effect of quantization etc.
→ View original post on X — @animaanandkumar, 2026-03-01 01:04 UTC