@Lyft accelerated agent development from 6 months to just a few weeks with LangGraph and LangSmith. Hallucinations decreased by 20% AI Resolution rate up by 16%
MACHINE LEARNING
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Spatial RAG for Geographic AI Model Accuracy
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Yup, the bridge is what I was actually testing to see if it could reproduce. In some rolls it gets close but given a bit of spatial RAG (say of local aerial and street view imagery) the result could be far more geographically accurate.
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Functional vs Distributional Geometry in Hierarchical Concept Spectral Analysis
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The key distinction here is subtle but important: Functional geometry asks what a representation can do. Distributional geometry asks where that representation came from. This paper shows that at least part of hierarchical concept geometry can be explained by the spectral
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Semantic structure vs. function: A caution for mechanistic interpretability
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The line that stayed with me: semantic structure may be useful for function without being driven by function. That is a powerful caution for mechanistic interpretability. Some beautiful structures inside models may be less like “designed concepts” and more like the linear
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Paper: Semantic Hierarchies Are Geometric in Language Models
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What looks like ontology may be eigenspectrum. A beautiful new paper by Andres Nava and Matthieu Wyart gives a mechanistic account of one of the most striking facts about language models: semantic hierarchies appear geometrically. An owl is a bird.
A bird is an animal.
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AI Latency for Cloud Robotics and Edge Embodiment
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Agreed: Latency is now low enough to support robot inference in the cloud, and edge is where embodiment transforms and safety checks should be performed: https://
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MiniMax M2 Technical Report: Attention Mechanism Analysis
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The MiniMax M2 series was one of the most widely used open-weight LLM series earlier this year. Now, we got a technical report with some interesting tidbits. I summarized some of them below: 1. Full attention as an anti-trend?: They tried hybrid sliding-window attention
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Highly rated new book on generative AI applications with LLMOps
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Highly rated new book from @PacktPublishing @PacktDataML … "Architecting Generative AI Applications: Build, deploy, and scale production-ready GenAI systems with LLMOps best practices" See it at https://
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AI advantage from faster leaner systems, not better prompts
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The next AI advantage won't come from a better prompt. It'll come from a faster, leaner system underneath the model. Her's Law and the Tau Scaling Law framework are worth understanding deeply if you're making AI infrastructure decisions. @huawei is leading this thinking. What
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Huawei’s Tau Scaling Law changes AI optimization layer focus
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Most enterprises are optimizing the wrong layer of AI.
— Ronald van Loon (@Ronald_vanLoon) 27 mai 2026
Cost per token doesn't start with the model.
It starts underneath it.
Huawei's Tau Scaling Law (Her's Law) changes the conversation entirely.
Here's the breakdown…#HuaweiPartner @huawei pic.twitter.com/P5fnE5CtGuMost enterprises are optimizing the wrong layer of AI. Cost per token doesn't start with the model. It starts underneath it. Huawei's Tau Scaling Law (Her's Law) changes the conversation entirely. Here's the breakdown… #HuaweiPartner @huawei