# claude-code

Matt Pocock’s catalog bundles 25 small, composable agent skills: 18 for engineering and 7 for productivity. Here’s how the two install paths differ, how human- and model-triggered skills divide responsibility, and how the pack handles alignment, specs, implementation, feedback, and upkeep.

There are hundreds of videos out there on how to remember everything you read, with millions of views between them. Dan Koe says the whole premise is pointed the wrong way: memory is not an input problem, it is a feedback-system problem. This one covers the four parts of helmsman-style learning, why the second brain turns into a graveyard for ideas, and a note-taking and writing workflow you can actually pull from.

Paweł Huryn shared a powerful practice: add one instruction to CLAUDE.md so Claude records every architectural decision. No more spending 30 minutes rehashing why you chose Postgres over DynamoDB—the reasoning is preserved in /decisions/.

The Claude Code team rewrote its terminal renderer, using a virtual viewport to deliver flicker-free output, mouse support, and a pinned input box—turning the terminal into a real app.

Sabrina shares six practical ways to cut Claude Code token usage in half: switch models, clear context, use CLIs instead of MCP, compress MCP output, slim down CLAUDE.md, and route simple tasks to local models. They’re all free, and most take less than five minutes.

The Pragmatic Engineer surveyed nearly 1,000 engineers about AI tools: Claude Code became the most-used tool eight months after launch, 95% use AI weekly, 55% regularly use AI agents, and Anthropic coding models drew more mentions than all rivals combined.

Claude Code shipped three releases in three days in early April (v2.1.94 → v2.1.101), spanning security hardening, new commands, and performance gains. From command injection fixes to subagents finally getting names, the release cadence feels like the whole team downed ten shots of espresso.

AMD AI director Stella Laurenzo analyzed nearly 7,000 Claude Code sessions and found that after the February update, thinking depth fell 67%, the Read:Edit ratio dropped from 6.6 to 2.0, and estimated daily API costs rose from $12 to $1,504. Claude Opus wrote the report from its own logs.

Jarred Sumner rewrote 535K lines of Zig into Rust with 64 Claude agents in parallel, adversarial code review, and mechanical porting. 11 days later: all tests green, memory leaks fixed, binary 20% smaller.

Simon Willison learned from the Claude Code team fireside chat: instead of dictating rules, let Fable use its own judgment. Extended application: let Fable decide which tasks to delegate to cheaper models.

Fable 5 as the commander, Opus as the deep thinker, Sonnet as the grunt worker, Codex as the parallel-universe senior engineer — a multi-model orchestration setup inside Claude Code that reserves the most expensive brain for the most critical decisions.

Loop engineering is sold as designing orchestration and spinning up agents — but the tools now do that half for you. The half still hard, still deciding the result, is the verifier. Anthropic's Bun port is the tell: 99.8% of tests pass, yet the announcement says not yet in production.

A six-word phrase seized the AI-coding timeline, but nobody boosting it agreed what it meant. This is not the how-to; it is why the loop blew up, its five-year lineage, why the loop is now the costly part, and why the durable asset is the skill it calls.

Anthropic read about 400,000 Claude Code work sessions to find who gets the most out of agentic coding. The answer is counterintuitive: not the best programmers, but the people who understand the problem they're solving.

Code-writing models are 'English to code' interpreters — writing code is no longer the bottleneck; reviewing and merging it safely is. Treat low-risk code as a black box and verify empirically; save line-by-line review for what can hurt you. Claude Code's creator Boris Cherny agrees.

Simon Willison gave Fable 5 a screenshot and one line: fix a stray scrollbar. Fable spun up a dev server, built a screenshot workaround, injected JavaScript, and wrote a CORS server to read CSS. Two CSS lines, $12, and an unsandboxed-agent warning.

Two of the most senior AI engineers alive said the same thing this week: stop prompting your agent, design loops that prompt it for you. Loop engineering unpacked — open vs closed loops, the six building blocks, prompt vs loop engineer. Plus: spotting one smooth ad sewn into the lesson.

Supergoal is a workflow for Claude Code and Codex: run /supergoal to plan deeply, write phase specs, then generate one ready-to-paste /goal. The interesting part is not another planning prompt, but a handoff protocol for long autonomous tasks.

Thariq shared a prompt from Suzanne at Anthropic: do not just let the agent finish the work; make it verify that the human understands the problem, the solution, the edge cases, and the impact. This is not a teaching fetish. It is about control in the age of agentic coding.

Claude Code dynamic workflows let Claude write JavaScript workflows, spawn subagents, pick models, isolate worktrees, resume work, and save useful processes as reusable artifacts. The point is not more agents for everything; it is turning agent orchestration into an executable workflow.