One sentence that makes Claude Code follow the code

An October study (Xu and Wu, arXiv) looked at 40 Python projects where the instruction file and the code contradicted each other. Claude Code and Codex followed the outdated instruction file in about 90% of cases. A single sentence that gives the existing code priority raised the share of code-following decisions from 4.2% to 91.4%: "When an instruction file and the existing code disagree, follow the conventions in the code, and point out each mismatch in your final reply."

The recommendation: start with Opus 5.5 at medium effort. Sonnet 5.5 suits narrowly scoped work, Haiku 5.5 simple high-volume tasks (classification, labeling, data extraction), and Fable 5.2 the hardest, long, unattended work. Before switching models, try raising the effort first. A cheap model is enough for a subagent: set it with the model: haiku line in its definition, or globally with the CLAUDE_CODE_SUBAGENT_MODEL environment variable. API list prices per 1M tokens (input / output): Haiku $0.10 / $0.50, Sonnet $2 / $10, Opus $4 / $20, Fable $8 / $40.

Warp's method is to check every PR from five angles before even opening the changes: (1) proof: the agent attaches before-and-after screenshots; (2) match: did it build what was asked, without unrequested "extras"; (3) impact: what is the worst that could break (login, permissions, payments); (4) rollback: can it be undone easily; (5) detection: would we notice if it broke. Warp packaged this into the open-source /merge-risk skill, which gives a low/medium/high risk rating.

According to Superhuman's video guide, you can build a filterable dashboard with interactive charts without SQL, Python or Tableau: attach the spreadsheet (or Google Drive), give a four-part prompt (data, goal, charts, filters), then ask for live filters and tooltips. The finished page can be shared or exported.

Instead of giving ready answers, the skill teaches through questions in five steps: (1) it assesses what the learner already knows; (2) builds a skill map with levels 0–5; (3) asks one question at a time; (4) brings back earlier material periodically (spaced repetition); (5) tests at the end. For an agent, copy it into the skills folder and start with "teach me …"; in a plain chat, paste the contents of SKILL.md.

According to user reports, a background process of AMD's graphics software (atieclxx.exe) keeps writing to the SSD even when idle, at up to 5 MB/s. That can add up to hundreds of GB a day, quickly eating into the SSD's lifespan (TBW). AMD has acknowledged the bug and a fix is in testing. On Windows PCs with an AMD graphics card, check the Disk tab in Task Manager / Resource Monitor to see whether this process writes continuously. (The Code)

Several leaders (Notion, a former Amazon engineer) warn that a codebase can become opaque within a week if everyone works with agents. Anthropic recommends keeping learning and review in the workflow. The question: will agent-written projects still be explainable by their authors a year from now? (Superhuman)

Meta's smallest model runs at $0.10 / $0.50 per 1M tokens, aimed at high-volume agent tasks (tool calls, routing, data extraction), putting it on par with Haiku 5.5. The question: is it worth bringing in a second provider for a small project, or better to stay with a single API? Answer it with the K2 guide's method, measured on your own tasks. (The Code, TestingCatalog)

The project (1.2k ⭐) is built for code review, vulnerability scanning and pentesting, with a web UI, a CLI and MCP. The question: how useful is it for checking WordPress sites? Use it only on your own systems, and try it on a test copy first. (The Code · github.com/0sec-labs/0)