New Anthropic research: Teaching Claude why. Last year we reported that, under certain experimental conditions, Claude 4 would blackmail users. Since then, we’ve completely eliminated this behavior. How?
LLMS
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Codex: A Transformative Tool for All Computer Work, Not Just Coding
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codex is for everyone — a transformative tool for all work done with a computer, not just coding
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Codex Goal Feature for Long-Running Tasks
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/goal in Codex is wild!
— Vaibhav (VB) Srivastav (@reach_vb) 8 mai 2026
Give Codex the mission.
Tell it what “done” looks like.
Let it keep going until it hits the end state.
Perfect for big refactors, migrations, retry loops, long-running experiments, and all the “don’t stop until this works” tasks.
Enable it in… pic.twitter.com/6VZKGHt7Ch/goal in Codex is wild! Give Codex the mission.
Tell it what “done” looks like.
Let it keep going until it hits the end state. Perfect for big refactors, migrations, retry loops, long-running experiments, and all the “don’t stop until this works” tasks. Enable it in -
Routing tasks to cheapest model that meets quality bar
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What changed in May 2026: > Before: you picked one model and committed.
> After: you route tasks to the cheapest model that meets the quality bar. DeepSeek V4-Pro scores within 7-8 points of Claude Opus 4.7 on SWE-bench. At 1/7th the cost during promo. The prompting skill -
DeepSeek as cheap second opinion alongside Claude
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The key insight most people miss: DeepSeek replacing Claude entirely is the wrong move. DeepSeek as a $0.14 second opinion running alongside Claude is the right move. Same thinking system. Dramatically different API bill.
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DeepSeek V4 integrated with Claude Code, reduced cost, parallel usage
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DeepSeek V4 now speaks Claude Code natively. $0.14 per million tokens vs $5.00 for Claude Opus 4.7. Here's the exact setup that runs DeepSeek as a second opinion alongside your main Claude session, without replacing it: ———————————-
DEEPSEEK PARALLEL
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Representational Geometry Influences Uncertainty in Large Language Models
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"Representational Curvature Modulates Behavioral Uncertainty in LLMs" Most interpretability work studies what features live inside LLMs. But this paper studies something deeper, which is the geometry of the whole evolving representation. The key result is that representational
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LangChain adds API support for AI due diligence research agents
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@p0
's API suite is now supported in LangChain, and they wrote a cookbook for a due diligence agent. We're part of the recipe:
• Deep Agents for orchestrating dedicated research track subagents
• @p0
’s Task API for research, findings, confidence scores, and follow-ups Full -
Team’s Success with Devin 3.0 and Devin Desktop Highlighted
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i'm just an advisor, cant take credit. team is doing great with devin 3.0 and devin desktop
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Hardware limitations on sparse neural activation in LLMs
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The human brain is incredibly efficient because it only activates the specific neurons needed for a thought. Modern LLMs naturally try to do this too (> 95% of neurons in feedforward layers stay silent for any given word), but our hardware punishes them for it. One of the most