in claude md just @agents
.md always i have a root agents md that tells agents when setting up new directories or repos to always do that.
AUTOMATION
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How a root agents.md ensures @agents .md is always created
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AI21 Labs surpasses Claude Code in efficiency and performance with Test Agent.
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4/5 Still came in ~$0.30 under Claude Code’s spend at a similar score. So we added a lightweight Test Agent that writes repo tests and filters failing patches, pushing our final result to 60.9% – surpassing Claude Code (60.9% vs 56.2%) at the same cost.
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AI21 Labs: ReAct Agent Performance with Enrichment and Scaling Strategies
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2/5 Started with a baseline: classic ReAct agent (GPT-5.2), single Docker-terminal tool. Baselines on the slice: vanilla 53.8%, enrich-only 55.6%, scale-only (n=5 + LLM judge) 55.4%, enrich-then-scale 57.7%.
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LangSmith Engine reviews traces, learns from usage, updates Context Hub
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You can use LangSmith Engine to review your agent traces to find bugs and areas for improvement across agent prompts + code. Between runs, the agent can review conversations, learn from real usage, and update Context Hub files.
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GPT-4 pricing robots collude in Harvard-Penn State experiment
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Researchers at Harvard and Penn State ran an experiment that should worry anyone who buys anything online. They built pricing robots out of GPT-4 and dropped them into a simulated market as competing sellers. No instruction to cooperate. No way to talk to each other. The only
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NVIDIA Nemotron 3 Ultra: Open Model for Agentic Tasks
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Introducing NVIDIA Nemotron 3 Ultra.
— NVIDIA (@nvidia) 4 juin 2026
A frontier smart open model built for long-running agents that need to plan, reason, use tools and keep working across complex coding, research and enterprise workflows.
Up to 5x faster inference and up to 30% lower cost for agentic tasks.… pic.twitter.com/AcHTauUzjmIntroducing NVIDIA Nemotron 3 Ultra. A frontier smart open model built for long-running agents that need to plan, reason, use tools and keep working across complex coding, research and enterprise workflows. Up to 5x faster inference and up to 30% lower cost for agentic tasks.
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PlantOS: Correlating motor current, pressures, and descaling performance
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PlantOS correlates motor current vs rolling force, hydraulic pressures vs strip dimensions, descaling performance vs surface drag patterns.
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AI Services Split: Navigating the Shift from Digital to Phygital
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The AI Services Split: From Digital to Phygital
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Mapping IT Applications for AI Integration: A Challenge and Use Case
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En ce moment, je travaille pour un client qui a besoin de revoir tout son SI, un peu à la traîne, afin d’y intégrer de l’IA. Et le plus difficile, c’est de réussir à cartographier toutes les applications.
— Defend Intelligence (Anis Ayari) (@DFintelligence) 4 juin 2026
Et avec l’IA, c’est un excellent cas d’usage.
Parce que oui, l’IA peut… pic.twitter.com/p3cjvNwgR8Currently, I am working for a client who needs to overhaul their entire IT system, which is a bit behind, in order to integrate AI. And the most difficult part is successfully mapping all the applications. And with AI, this is an excellent use case. Because, yes, AI can
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Stop writing skill files for agents manually; use CLAUDE.md and AGENTS.md
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You can now stop writing skill files for your agent by hand.
— AlphaSignal AI (@AlphaSignalAI) 4 juin 2026
Coding agents forget what you taught them last week.
Every session burns tokens re-explaining your procedures.
CLAUDE.md helps one agent, AGENTS.md helps another.
You maintain both yourself, learning on behalf… pic.twitter.com/eLitYPMaT3You can now stop writing skill files for your agent by hand. Coding agents forget what you taught them last week. Every session burns tokens re-explaining your procedures. CLAUDE.md helps one agent, AGENTS.md helps another. You maintain both yourself, learning on behalf