想高效追踪AI进展却没时间？这份指南给出用10分钟/天筛选高质量AI日报，推荐实用信源，并附操作步骤。含最新Gemini月活7.5亿、GPT-5.2优化等动态参考。
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Stay updated on China's AI progress with this practical guide for builders and PMs. Learn how to track key developments, assess relevance, and act on ...
Learn how to track China AI developments in English efficiently—without endless scrolling. A practical guide for builders using focused sources, smart...
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...