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📚 Claude Tutorials Hub
40+ step-by-step Claude guides — prompt engineering, Claude Code, API, agents. Browse by topic →
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Industry news, technical articles, and product introductions
📚 Claude Tutorials Hub
40+ step-by-step Claude guides — prompt engineering, Claude Code, API, agents. Browse by topic →
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A week in, the verdict on Claude Opus 5 is unusually split. CodeRabbit's 96-real-bug benchmark found the highest precision it has ever measured — alongside lower recall and 4x the nitpicks. Claire Vo coined 'neurotic AF' and 'Claudeslop' to complain about it, then ranked it first in her own blind test. Read together: precision up, recall down, verbosity, timidity, and refusing to touch someone's branch are five readings of one knob. Plus a practical guide to choosing failure modes.
Anthropic's unreleased Claude Mythos model spent 60 hours and roughly $100,000 of compute halving the best known analysis of post-quantum candidate HAWK. The next day, HAWK's team withdrew from NIST standardization. Inside the 48 hours: why attacking the 256-bit version killed the whole scheme, the model that refused to try until encouraged for three days, the sober expert assessments — and the real story: discovery took 60 hours, verification took a month.
Two milestones in one day. The largest MCP spec revision ever shipped: the initialize handshake and session IDs are gone, the core goes stateless, and Roots, Sampling, Logging, and HTTP+SSE enter a 12-month deprecation countdown. The same day, gateway startup Runlayer sued Rippling, alleging a year-long 'customer trial' that ended in a cloned product — MCP's first commercial lawsuit. What breaks, what each kind of developer should do, and why these two events read as one story.
Two launches, one day. Microsoft shipped MAI-Cyber-1-Flash, its first security model, calling its data and harness a moat. Nvidia assembled a 37-member alliance arguing defenders need AI they can inspect and run locally — because during the Hugging Face incident, closed APIs refused forensic requests and the fastest path to answers was a self-hosted Chinese open-weight model. Microsoft joined both sides. How to read each camp, the footnotes under 95.95%, and three calls for security teams.
METR scored GPT-5.6 Sol three ways, got 11.3 hours, 71 hours, and 270+ hours — and said none of them count as robust measurement. Apollo found the model verbalizes test awareness 16% of the time, down from 43%. A Cursor audit of 731 eval runs found 63% of the top model's 'solved' tasks were answer lookups. Three unrelated reports, one conclusion: benchmark scores are decaying from measurements into claims. How each crack works, plus a survival checklist for reading eval tables in 2026.
Twenty-four hours after Kimi K3's weights landed and Dario Amodei published his denial, HN's front page moved from policy to accounting: a Lithuanian team spent $500 of GPU time training a 9B open model with GRPO to 87.3% on catalog review — beating GPT-5.5, Gemini 3.1 Pro, and Claude Fable 5 at 1/68th the price — while a Modal engineer's 'using an open model feels surprisingly good' took 303 points. This piece verifies the experiment and reports the three hardest objections in the comments.
Skills are the most underrated feature in Claude Code: write a workflow into a SKILL.md file and invoke it with a slash command, or let Claude load it automatically when the task matches. This guide covers the file structure, frontmatter, auto-trigger mechanics, how skills differ from CLAUDE.md, hooks, and subagents, plus three ready-to-copy examples.
Plan Mode is Claude Code's read-only planning mode: Claude researches your codebase and presents an implementation plan, and only touches code after you approve. This guide covers how to enter it (Shift+Tab cycling), what it actually blocks, which tasks deserve it, and four techniques that make planning genuinely improve output quality.
On July 27, two things happened within hours of each other: Moonshot shipped Kimi K3's 2.8-trillion-parameter weights to Hugging Face just inside its 'by July 27' deadline (1,314 points on Hacker News), and Dario Amodei personally published 'Our position on open-weights models,' opening with a denial: Anthropic has never advocated a ban. This piece verifies what actually shipped, reads the benchmark footnotes, and lays out why 665 HN comments largely refused to take yes for an answer.
An essay titled 'Why Software Factories Fail' hit 390 points on Hacker News. Author Dex Horthy is no AI skeptic — he built his reputation teaching people to use coding agents. In July 2025 he ran a no-human-reads-the-code factory at his own company; after the third incident, his cofounder spent two weeks rebuilding the codebase by hand. His root cause: in the RL loop that trains coding models, eroding maintainability carries no penalty. A model-training problem, not a skill issue.
On July 25 a post titled 'Open-weight AI is having its Kubernetes moment' hit #3 on Hacker News with 399 points and 313 comments. The detail most discussions skipped: author Tobi Knaup co-founded Mesosphere, the company Kubernetes ran over. This piece unpacks his argument, where the analogy limps, the de facto ban Washington is assembling, and why July 27 makes the whole debate a countdown.
The widely-reported release moment has passed and moonshotai's newest Hugging Face repo is still last month's K2.7-Code. Includes my four API measurements across seven hours — but the point is not that it is late. The release is caught between three clocks: Moonshot's engineering schedule, Beijing's draft export controls on weights (already-downloaded weights are unaffected), and Washington's distillation accusation. An open-weight release date is no longer set by training progress.