| | | 🔗 Stories, Tutorials & Articles | | | | | | | How I Use Every Claude Code Feature | | | | | Claude Code isn't just generating responses anymore - it's gearing up to run projects.
The new direction turns it into a programmable, auditable agent runtime. Think custom hooks, restart logic, planning workflows, GitHub Actions, and subagent delegation tricks like the “Master-Clone” pattern.
At the core sits CLAUDE.md. It’s not documentation; it’s a contract. This file defines how tools work together, keeps token use in check, and enforces conventions so the whole thing doesn’t spiral into chaos.
Bigger picture: With the rise of Claude CLI and its growing ecosystem, prompt-chaining is taking a backseat. What’s emerging? Structured, scriptable agents baked into real engineering workflows. |
| | | | | | | | | | AI's Dial-Up Era | | | | | AI's reshaping jobs - but not evenly. Some industries will feel the squeeze faster than others. It all comes down to a race: productivity vs. demand.
History's playbook? Think textiles, steel, autos. Automation boosted output. Jobs stuck around - as long as demand kept growing. Once markets topped out, headcount sank, even as factories hummed faster. |
| | | | | | | | | | AI Broke Interviews | | | | | | AI has revolutionized technical interviews, blurring the line between genuine skill and cheating with perfect solutions and polished answers. In response, companies are shifting back to in-person interviews for real-time cognitive transparency, authenticity constraints, realistic collaboration signals, reduced noise in the pipeline, and rebalancing the playing field. In this new era of AI-resistant interviewing, the focus is on measuring human reasoning through activities like explaining code, real-time architectural debates, physical whiteboards, live collaboration, adaptive questioning, and behavioral questions without scripts. |
| | | | | | | | | | You Should Write An Agent | | | | | | Building LLM agents - essentially looping stateless models through tools - looks simple. Until it isn't. Peel back the layers, and you hit real architectural puzzles: context engineering, agent loops, sub-agent choreography, execution constraints. |
| | | | | | | | | | 1,500+ PRs Later: Spotify’s Journey with Our Background Coding Agent | | | | | Spotify just gave its internal Fleet Management tooling a serious brain upgrade. They've wired in AI coding agents that now handle source-to-source transformations across repos - automatically.
So far? Over 1,500 AI-generated PRs pushed. Not just lint fixes - these include heavy-duty migrations. They're reporting up to 90% time saved vs. grinding it out by hand. |
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