用一张表把信号分级（影响面、紧急度、可验证性、行动成本、Owner），让周会从「分享链接」变成「可排期的决策记录」。
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从阅读器、聚合站、官方源、GitHub、社区到一体化雷达，列出 6 条路线及适用人群，避免只换工具不换习惯。
目录型站点帮你「发现工具」；雷达型产品帮你「持续跟进变化」。本文用决策表说明两者如何搭配，而不是二选一。
从信息结构、订阅方式、可追溯来源、团队分发四个维度对比 RadarAI 与 Feedly，给出 20 秒内可执行的选型结论。
如果你在找 2026 年“最好的 AI 新闻聚合器”，这篇给你直接答案：用一个对比表看 7 款工具各自最适合谁，并告诉你如何按“来源可追溯、通知控制、行动转化”来选。
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.
How to produce a team AI digest: 5-item weekly format (what/why/source), delivery via Slack or doc, clear ownership, and when to stop.
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.
Turn scattered updates into a shared watchlist: one owner, one signal source, and a simple format (item, why it matters, source link, next step).