标签：#tools

Last week I did something dumb: I sat down and wrote 50 Claude Code prompts in one sitting. Halfway through I was sure most of them would never get used. But I finished, pushed them to GitHub, and made myself use them for an entire week -- no ad-hoc prompting allowed. The result surprised me. Some skills were life-changing. Others were useless. Here is the honest breakdown. The Methodology I categorized the 50 skills into 5 types: Analysis (12), Generation (14), Debugging (8), Planning (10), Maintenance (6). Rule: every time I hit a task, I must use a skill file or admit I did not have one. The 7 That Actually Saved Me Hours 1. Code Review Assistant (saved ~3h) This was the biggest surprise. I usually review PRs by gut feel -- scan the diff, look for obvious bugs, approve. The Code Review skill forced me to be systematic: On a 400-line PR it caught 2 security issues I would have missed. That alone justified the experiment. 2. Bug Investigator (saved ~2h) Instead of pasting errors and asking "why?", this skill forces you to provide: error message, file context, hypothesis. 3. Dependency Audit (saved ~1h) Scanned a 3-year old Node project. Found 2 CVEs, 8 unused devDependencies (21 MB). 4. Auto Commit Messages (saved ~30m) Saves 2 minutes per commit. Over 15 commits in a week that is 30 minutes. 5. Test Generator (saved ~2h) Generates 5-8 test cases per function in seconds. 6. Refactoring Planner (saved ~1h) Reads the function, identifies extraction candidates, outputs a dependency-ordered plan. 7. Performance Audit (saved ~30m) Found an unoptimized 3 MB hero image and a render-blocking script. The Ones I Never Touched About 8 out of 50 were "not this week" -- Database Migrations, API Documentation, CI/CD Pipeline. What I Learned The ROI is in the analysis skills. Code review, bug investigation, dependency audit -- these are high-judgment tasks where Claude thoroughness beats speed. Skills are habit, not technology. The hardest part was not writing the prompts -- it w

Interactive diagrams with AI, and your code always in sync Discussion | Link

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We run 10+ web products. For years our SEO tooling was a $100+/month subscription we mostly used for one job: technical audits. The keyword and backlink dashboards sat untouched while the bill renewed every month. At some point the math stopped making sense, so we rebuilt our stack around what we actually use. Here's the honest breakdown — what we kept free, what we replaced, and why. What a technical audit actually needs to check Before picking tools, it helps to know what you're auditing. The technical layer is finite and well-defined: Crawlability — robots.txt, broken internal links, redirect chains Indexability — noindex tags, canonical correctness, soft 404s, orphan pages Sitemaps — only canonical, indexable, 200-status URLs On-page — titles, meta descriptions, one H1, valid structured data International — hreflang reciprocity (if you're multilingual) Performance — Core Web Vitals (LCP, CLS, INP) That's it. None of it requires a keyword database or a backlink index — the two things you're really paying $100+/mo for in the big suites. The free tools that cover most of it Google Search Console is non-negotiable and free. The Pages report is the source of truth for what's indexed and why the rest isn't. PageSpeed Insights (free) gives you real Core Web Vitals. Screaming Frog's free tier crawls up to 500 URLs, which is plenty for small sites. For a long time that was our whole stack: GSC + PageSpeed + free Screaming Frog. If your site is under 500 pages, you may not need anything else. Where it broke down for us The free Screaming Frog cap (500 URLs) is the wall. Several of our sites are bigger, and re-auditing them meant either the paid Screaming Frog licence (annual) or going back to a monthly suite. Both felt wrong for something we run after every deploy. So we built our own crawler for the technical layer — and then, honestly, turned it into a product because other people we talked to had the same problem. It does the whole checklist above across the entire sit

A native Mac cockpit for Claude Code Discussion | Link

Coding agents — Codex CLI, Claude Code, Cursor, and Pi — are productivity multipliers. But they all assume you are happy sending your code to someone else's servers. For many of us that is a deal-breaker: proprietary codebases, client NDAs, compliance requirements, or just the principle of owning your own compute. This guide shows how to swap out every cloud API with a local Ollama server running qwen3-coder:30b . Same tools, same workflows, no data leaving your network. Why Run AI Locally? The case is simple: Zero data exfiltration. Your code never leaves your machine or LAN. No per-token cost. Run 10,000 completions or 10 — the electricity bill does not care. Works offline. Airplane mode, restricted network, flaky VPN — irrelevant. No rate limits. No 429s at 2 am when you are in flow. The honest tradeoff: frontier models (Claude Opus 4, GPT-5) still outperform local models on complex multi-step reasoning and very large context tasks. For the 80% of day-to-day coding work — autocomplete, refactors, test generation, documentation — a well-chosen local model is more than good enough. Hardware Requirements I run this on an Apple M4 Pro with 48 GB unified memory . Apple Silicon's unified memory architecture is exceptionally well-suited to LLM inference: the GPU and CPU share the same memory pool, so a 22 GB model fits comfortably alongside a full development environment. Minimum viable setup: RAM What fits 16 GB 7–8B parameter models (qwen3:8b, llama3.2:8b) 32 GB 14–20B models (qwen3:14b, gpt-oss:20b) 48 GB 30–35B models (qwen3-coder:30b, qwen3.6:35b) 64 GB+ 70B models (deepseek-r1:70b, llama3.3:70b) On Intel/AMD systems with discrete GPUs the math is different: VRAM is the bottleneck, and models that don't fit entirely in VRAM fall back to slow CPU offloading. Choosing a Model For 48 GB unified memory, these are the models worth knowing about: Model Size on disk Active params Strengths qwen3-coder:30b ~22 GB 3.3B (MoE) Coding, 256K context, HumanEval SOTA qwen3.6:35b

On-device voice recorder that transcribes + exports Markdown Discussion | Link

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The AI workspace that plans, designs and ships with you. Discussion | Link

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Voice-first macro tracking for fitness enthusiasts Discussion | Link

Most AI short-form video demos skip the boring part. They show a finished TikTok, Reel, or YouTube Short. Maybe they show the prompt. Maybe they show the generated script or the final render. But the hard part is not making one video. The hard part is making the fifteenth video without the whole system turning into a pile of one-off scripts, half-remembered FFmpeg commands, broken captions, inconsistent hooks, and manual upload steps. That is where I think the conversation around AI video automation gets more interesting. Not: Can an AI generate a Short? But: What workflow does an AI agent need to generate Shorts repeatedly? I was looking at a Terminal Skills use case for building an AI short video generator, and the useful part is not the fantasy of "push one button, print infinite content." The useful part is the stack. The real job is a pipeline A short-form video generator sounds like one tool. In practice, it is a pipeline: topic research -> script -> voiceover -> footage or visual generation -> subtitles -> assembly -> platform formatting -> upload -> analytics Each step has different failure modes. Topic research can produce generic ideas. Scripts can be too long. Voice can drift from the brand. Footage can mismatch the narration. Subtitles can land under platform UI. FFmpeg can export a technically valid file that a platform still hates. Uploads can succeed in the API but fail the actual publishing workflow. If you try to solve all of that with one giant prompt, the agent has to keep too much operational knowledge in its head. That is fragile. The better pattern is to split the workflow into skills. What a skill gives the agent A skill is not just a code snippet. For this kind of workflow, a useful skill tells the agent: when to use this capability what inputs are expected what output should exist afterward what validation is required when to stop instead of pretending success That last point matters. For media automation, "the command ran" is not enough. Th

Exec assistant who lives in iMessage and calls your phone Discussion | Link

Keep PRs, issues, CI, and docs moving with AI agents Discussion | Link

My keyboard fell apart. Now it's your problem. Discussion | Link

Run AI coding agents hands-free from a heads-up display Discussion | Link

AI text correction & translation via keyboard shortcuts. Discussion | Link

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Self improving vibe coding env with no API fees Discussion | Link