It's called Computer for Counsel. Not a chatbot with a law-themed prompt — an agentic system that runs the grunt work: NDA review, case research, regulatory monitoring. The catch that matters: every output links back to the real statute, filing, or ruling.
SYSTEMS
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Insider’s Guide to ML System Design Interview with Diagrams and Framework
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Insider's guide to the Machine Learning System Design Interview: https://
amzn.to/47QOwEQ v/ @bytebytego 10 real questions & detailed solutions 211 diagrams to visually explain how various systems work 7-step framework to solve any #ML system design question -
Book on NLP from foundations to agents with RAG pipelines
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“Mastering NLP From Foundations to Agents” by Lior Gazit and Meysam Ghaffari, from @PacktDataML http://
amzn.to/4nJrLw4 𝗟𝗲𝗮𝗿𝗻 𝘁𝗵𝗶𝘀:
Engineer NLP systems from ML foundations to LLM architectures
Implement RAG pipelines, routing layers, and agent workflows -
Path-scoped rules keep Claude’s context clean for big codebases
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6/ For a big codebase, do not pile everything into one file. Use path-scoped rules in a .claude/rules/ folder. They load only when Claude touches the matching part of your code, so the context stays clean instead of carrying everything at once.
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Voyager 1.6 divides AI deployment into compile and run phases
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Deploying AI models to embedded targets usually means a full SDK on a device that doesn't necessarily have room for it. So Voyager 1.6 splits that workflow in two. You compile your model on a development machine using axelera-devkit, which produces an .axm file. Then you run it
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Agent memory becomes data management system for LLMs
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// Agent memory is a data system now // Great paper on long-term memory for LLM agents. (bookmark it) Agent memory has grown from simple retrieval into a full data-management layer with storage, retrieval, update, consolidation, and lifecycle governance. Yet most evaluations
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AI in Stores: Instacart’s Caper Carts with NVIDIA Jetson
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Real grocery stores are hard on AI: changing shelves, spotty Wi-Fi and shoppers on the move.
— NVIDIA (@nvidia) 24 juin 2026
On the NVIDIA AI Podcast, @instacart 's David McIntosh shares how Caper Carts use NVIDIA Jetson and edge AI to recognize items in store. pic.twitter.com/oPXNhjJ5uUReal grocery stores put AI to the test: constantly changing shelves, unstable Wi-Fi, and moving customers. In the NVIDIA AI Podcast, Instacart’s David McIntosh shares how Caper Carts use NVIDIA Jetson and AI.
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Closing the Agent Loop with LangSmith and Context Hub
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"Most agents don't learn, they just leave traces."
— LangChain (@LangChain) 24 juin 2026
In 12 minutes, @jakebroekhuizen breaks down how to actually close the loop.
Surface issues with LangSmith Engine
Write memory updates back to Context Hub
Let the agent actually improve between runs
If you're thinking about how… https://t.co/aIfMwT33hM pic.twitter.com/QcjKez5SQ4Most agents don't learn, they just leave traces. In 12 minutes,
@jakebroekhuizen
explains how to actually close the loop. Surface issues with the LangSmith engine
Write memory updates to the Context Hub
Let the agent -
Vector Search Perf module for slow AI retrieval diagnosis
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2/ Next problem → sluggish AI retrieval. Scaling it up is notoriously difficult. This Vector Search Perf module walks you through diagnosing slow queries with Atlas Metrics. PLUS actually managing memory sizing and quantization in full production > https://
fandf.co/4b6Q3uW -
Scoped agent access to stack felt surprisingly clean
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gave an agent access to my actual stack today
— Nico (@nicos_ai) 24 juin 2026
no scary “it can see everything” moment. scoped permissions, every action logged
felt illegal how clean it was https://t.co/xeAVrovr1D pic.twitter.com/NyPzYIQWf3gave an agent access to my actual stack today no scary “it can see everything” moment. scoped permissions, every action logged felt illegal how clean it was