Learn how to track China AI developments in English efficiently—without endless scrolling. A practical guide for builders using focused sources, smart...
文章列表
Discover the most reliable English-language sources for tracking China's fast-moving AI industry. From newsletters to research hubs, get timely, accur...
不是每个热门 AI 仓库都值得投入时间验证。按顺序走这 6 关：License、能不能跑、是不是你真实的问题、维护风险、工作流位置、退出成本。遇到阻断就停，15 分钟内做完决定。
GitHub Trending 的价值不在于告诉你“哪个项目火”，而在于帮你更快筛掉不值得试的仓库。这篇给你一套 7 步判断法，从 license、可运行性到试点门槛，15 分钟做完初筛。
A practical team scorecard for turning AI updates into owned decisions using impact, urgency, evidence quality, action cost, and a forced next step.
如果你在找“最好的 AI 新闻聚合器”，真正要选的不是功能最多的工具，而是最适合你工作流的那一种：发现、验证、收口、团队分发，各自用法不同。
A 3-layer AI tracking system: product radar for launches, GitHub for OSS momentum, and blogs/changelogs for depth—combined without duplication.
Six criteria for comparing AI news aggregators: source diversity, deduplication, source links, update frequency, builder relevance, and transparency.
A practical team digest format that turns AI update decisions into a short internal brief: what changed, why it matters, what we are doing, and who ow...
Four criteria to identify launches that matter: primary source verifiable, touches your stack or users, technically distinct, usable artifact exists.
Build a durable AI monitoring habit using cue/routine/reward, minimum viable habit design, and the skip-don't-break rule.
A triage guide for developers: API breaking changes (act now), new models (evaluate), new tools (evaluate), trend pieces (ignore).
How to follow Chinese AI developments—Qwen, DeepSeek, Baidu, ByteDance—using English-language sources and accounting for translation lag.
A 30-minute weekly ritual for AI intelligence: collect → classify → shortlist → one action → document.
Four questions to evaluate any new AI tool: problem fit, stack fit, sustainability, and alternatives. Always prototype before committing.
How to separate signal from noise in AI news: define signal, identify the 5 noise types, and apply a 3-question filter.
Three rules for founders to beat AI FOMO: distinguish signal from noise, set hard limits on consumption, and know what to unsubscribe from.
What to capture for each AI model release: benchmarks, context window, cost per million tokens, license, and changelog URL.
How to follow open-source AI projects effectively: GitHub watch/star, OSS radar tools, and the metrics that signal real momentum.
Developer-specific AI monitoring: OSS signals, changelog monitoring, GitHub watch, and batched weekly reading to stay current without losing flow.
Trend tracking is pattern recognition over time; news reading is event consumption. Both have a place, but builders need trend tracking to make decisi...
Five criteria for evaluating AI radar tools: signal-to-noise ratio, source traceability, coverage, update cadence, and actionability.
A simple watchlist format for tracking AI tools and launches: what, why, source link, next step—capped at 15 items, reviewed weekly.
A PM-specific AI monitoring workflow focused on capability jumps, roadmap implications, user expectation shifts, and competitor feature signals.
A 5-step checklist for verifying AI news: find the primary source, check the date, check the author, cross-reference a second source, and review metho...
A founder's weekly AI monitoring routine: one signal source, a 20-minute timebox, competitive intelligence framing, and one concrete action per week.
Before adopting a new AI tool, evaluate fit: does it solve a real problem, integrate with your stack, and have a sustainable source and roadmap?
High-signal updates are those that affect what you can build or ship: launches, breaking changes, and repeated patterns—not volume or hype.