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AI 资讯

My linter kept warning the people who did it right. Three times, in the same direction

The warning landed on the only people who had done it properly I maintain a linter that reads agent config files — SKILL.md , AGENTS.md , CLAUDE.md — and fails CI when they bake in something that only works on the author's machine. One of its rules says: if you call an external CLI, declare it, or the next person won't have it. Declaring it means naming it in frontmatter: requires : codex Except that anyone with more than one dependency writes the list form, because that's what YAML is for: requires : - codex - gemini My implementation only read the first shape. So the block list — the normal way, the way you write it the moment you have two of anything — was invisible to the linter, and it warned you for an undeclared CLI that you had, in fact, declared. Read that back slowly. Authors who ignored the dependency question entirely were never flagged, because they never wrote a requires: key at all. Authors who sat down and wrote the contract properly got a warning telling them they hadn't. The rule was inverted with respect to the thing it was trying to encourage. I shipped that. It went out in a patch release, and I only found it because a commenter used the phrase "dependency contract" and I went to re-read my own implementation of it. Then it happened again. Twice, in one release Two comments on a post of mine turned into new rules. One of them, unverified-write , reports a file that changes external state — git push , npm publish , an INSERT — and never reads that state back anywhere. Before publishing, I ran it over 586 real skill files pulled from a public registry, found two false-positive shapes in the data, fixed both, and re-measured. Fire rate 0.7%, and every hit I could check by hand was genuine. I felt good about it. Then I handed the diff to a different model for a pre-publish read, and it produced this input in about a minute: Never run `git push --force` from this skill. That is a git push in a code span, in a file with no read-back anywhere. My rule

2026-08-17 原文 →
AI 资讯

The 7 AI Repositories I Starred This Month

I don't star GitHub repositories just because they are popular. A repository earns a star from me when I can see myself returning to it later. Maybe it solves a real engineering problem. Maybe it introduces a new architecture. Maybe the code teaches me something. Or maybe it represents where AI development is heading. I've been spending a lot of time exploring AI repositories around agents, workflows, RAG, MCP, browser automation, model training, and API development. These are seven repositories that stood out to me recently. Not because you need all seven. But because each one represents an important direction in AI development. 1. OpenAI Cookbook Repository: https://github.com/openai/openai-cookbook If you're building with the OpenAI API, this is one repository I would keep bookmarked. The OpenAI Cookbook contains practical examples and guides covering common API development tasks, with many examples written in Python. What I particularly like is the implementation-first approach. Instead of spending hours reading theoretical explanations, you can study working examples and adapt them to your own application. It's useful for: API integration Structured outputs Embeddings Agents Evaluations Multimodal applications For beginners, it can also serve as a bridge between understanding an AI concept and actually implementing it. 2. LangChain Repository: https://github.com/langchain-ai/langchain LangChain remains one of the most important repositories in the LLM application ecosystem. But I don't recommend it simply because it is popular. I recommend understanding it because it exposes you to the building blocks behind modern AI applications. Models. Tools. Retrievers. Agents. Integrations. Structured outputs. If you're serious about AI engineering, studying how these components fit together is valuable even if you eventually choose another framework. 3. LangGraph Repository: https://github.com/langchain-ai/langgraph This is probably one of the repositories I would recomm

2026-08-17 原文 →
开源项目

🔥 liustack / modlens - The first vision plugin for DeepSeek Harness, and the vision

GitHub热门项目 | The first vision plugin for DeepSeek Harness, and the vision bridge for every text-only coding agent. Paste an image, get structured JSON evidence (OCR, layout, semantics). | 全网最强 DeepSeek Harness 外挂视觉插件，为 DeepSeek、GLM 等纯文本模型外挂视觉能力，粘贴图片即得结构化 JSON 证据（OCR、版面、语义）。 | Stars: 2,222 | 590 stars today | 语言: TypeScript

2026-08-16 原文 →
开源项目

🔥 IRNova / Nova-Proxy - یک پنل گرافیکی کاربردی برای ارائه اشتراک‌های Worker با پروکس

GitHub热门项目 | یک پنل گرافیکی کاربردی برای ارائه اشتراک‌های Worker با پروکسی‌های ، Trojan و Warp به همراه زنجیره پروکسی، ارائه دهنده تنظیمات کامل DNS، IP تمیز و روتینگ پیشرفته برای کاربران تمامی پلتفرم‌ها با استفاده از هسته‌های Amnezia، Wireguard، Sing-box، Clash/Mihomo و Xray. | Stars: 3,039 | 24 stars today | 语言: JavaScript

2026-08-16 原文 →
AI 资讯

Four patterns that keep my YouTube longform JSON queue from going stale

I manage the YouTube longform queue for my BuilderStack channel as JSON files in content/yt-longform-queue/ . A spec file lands there when a generator script commits a new dialogue; the publish workflow picks the file, renders it to MP4, uploads it, then moves the file to uploaded/ . No external queue service, no database rows, no management dashboard. This has worked for three months without a major incident. Four patterns kept it from collapsing. Archetype-priority picking, not FIFO First-in, first-out publishing breaks when you have product walkthrough videos, educational deep-dives, and weekly recap specs all in the queue simultaneously. A recap spec committed yesterday would block a product walkthrough from two weeks ago if the queue ran FIFO — and the product content is what actually grows the channel. The picker uses an explicit priority rank: RANK = { " product_findindiegame " : 0 , " product_ossfind " : 1 , " hidden-gem " : 1 , " build_in_public " : 2 , " technical " : 3 , " curated " : 4 , " meta " : 4 , " contrarian " : 6 , " recap " : 7 , " ai_tools " : 7 , } DEFAULT_RANK = 5 Archetypes not in the dict fall to DEFAULT_RANK = 5 — the middle, not the bottom. New formats I haven't classified yet still air rather than sitting perpetually at the end. Within each rank tier, files sort by filename (oldest-first). The archetype value comes from the spec JSON's top-level archetype field, falling back to a prefix match on the filename for older files that predate the field. One consequence: adding a new archetype name to the dict can reorder the queue overnight. I've done this intentionally to let a backlogged product video jump ahead of a stale recap. 21-day stale expiry Queue files include a date prefix: YYYY-MM-DD-<slug>.json . The picker removes files whose date is more than 21 days old before selecting what to publish: MAX_AGE_DAYS = " ${ QUEUE_MAX_AGE_DAYS :- 21 } " CUTOFF = $( date -u -d " ${ MAX_AGE_DAYS } days ago" +%Y-%m-%d ) for f in content/yt-longform

2026-08-16 原文 →
AI 资讯

The actual cost of shipping an iOS app in 2026

"How much does it cost to put an app on the App Store" gets answered inconsistently online because most answers either only count Apple's fee, or only count hardware, or quietly assume you're renting expensive cloud infrastructure you don't actually need. Here's every cost, split into what's mandatory and what's a choice. Mandatory: Apple Developer Program — $99/year This is the one cost nobody can avoid. To submit any app to the App Store — free or paid, one app or fifty — you need an active Apple Developer Program membership, which is $99/year, billed annually, direct to Apple. There's no one-time version and no way around it. (There is a free-tier Apple ID for personal on-device testing without paying this, but it doesn't let you submit to TestFlight external testers or the App Store — for an actual public release, the $99/year membership is required.) Required, but where you have genuine choices: building and signing To submit a build, something has to run Xcode's command-line signing and archive tools — that part isn't optional. Where you have a choice is what runs it: Option Cost Recurring? Buy a Mac ~$799+ (Mac mini, entry price) No Rent a cloud Mac ~$20–100+/month Yes GitHub Actions, public repo $0 No GitHub Actions, private repo $0 up to a monthly allowance, then per-minute Only if you exceed the free allowance The short version on that last row: on a public repo, this line item can legitimately be $0, indefinitely. Optional or one-time: the things people assume cost more than they do App Store screenshots and marketing assets. You can generate these yourself for free — from the Simulator or a physical device — no paid tooling required. A physical iPhone for testing. Not strictly required to submit, but you'll want one to sanity-check the finished app before release. TestFlight itself. Free, included in the $99/year membership. App Store listing itself. Free — no fee to list an app beyond the membership. Adding it up For someone shipping a side project on a

2026-08-16 原文 →
AI 资讯

GitHub Actions' free macOS minutes, explained

GitHub Actions is GitHub's built-in CI/CD system — it spins up a fresh virtual machine, runs whatever commands you tell it to, and tears the machine down when it's done. It supports Linux, Windows, and macOS runners. The macOS runners are the interesting part here, because they're actual macOS machines with Xcode's command-line build tools available, which means they can build and sign iOS apps — not just run tests. The headline rule: public repos are free On a public repository, standard GitHub-hosted runner minutes — including macOS — don't cost anything, on any plan, including the free plan. It's a genuine free tier, not a trial or a limited allowance that runs out. One nuance: this covers standard runners. GitHub also offers "larger runners" (more CPU/RAM) — those are billed regardless of repo visibility. A default macOS build for signing and archiving a typical app doesn't need one, so this rarely matters in practice. What changes on a private repo If your repository is private, you get a monthly allowance of free minutes instead of unlimited free usage: Plan Included minutes / month Free 2,000 Pro 3,000 Team 3,000 The number that actually matters for iOS builds is how fast macOS runners burn through that allowance. macOS minutes cost roughly 10x GitHub applies a multiplier against your included minutes: Linux runners run at the baseline rate, macOS runners run at roughly 10x that rate. A 6-minute macOS build eats through the same allowance as roughly 60 minutes of Linux CI. Applied to the table above, a Free-plan private repo effectively gets around 200 macOS-runner-minutes worth of free build time per month before you're billed per minute past it (Pro/Team works out to roughly 300). The practical upshot: if you're fine building in the open, a public repo gets you unlimited macOS build minutes at zero cost, indefinitely. If you'd rather keep the code private, everything about the pipeline still works the same way — you're just drawing from a metered allowance

2026-08-16 原文 →
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