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Nobody is monitoring Bluesky, so I built a mentions scraper for it

I wanted to know when people mention a brand on Bluesky. Simple ask. Turns out Brandwatch, Mention, Hootsuite, basically every social listening tool, still doesn't cover it. They're all busy with X and Instagram while Bluesky sits at 27M+ monthly users. So I looked at doing it myself and found out something most people miss: you don't need to scrape anything. Bluesky runs on the AT Protocol, which is open by design. Public posts are searchable through a documented endpoint. No login, no API key. GET https://api.bsky.app/xrpc/app.bsky.feed.searchPosts?q=YOUR_BRAND&sort=latest&limit=100 That returns full post objects. Text, author handle, timestamps, like/repost/reply counts, embedded links, hashtags. Everything you need. Two things that broke my first version Worth writing down because most tutorials get this wrong now: The public.api.bsky.app host that older guides point to returns 403 for search. Use api.bsky.app instead. As of July 2026, unauthenticated search rejects cursor pagination. Page one works fine, page two gets you a 403 with "request forbidden by administrative rules". The nasty part is it looks like rate limiting, but it isn't. The workaround: paginate by time. Use sort=latest , then pass until= with the createdAt of the oldest post from the previous page. Dedupe on uri because the boundary post shows up twice. If you don't want to maintain any of that I packaged the whole job as an Apify actor: Bluesky Mentions Scraper . Keywords in, clean JSON out. It handles the pagination and retry stuff above, filters replies if you want, scores basic sentiment, and can pull follower counts for each author so you can sort mentions by reach. Runs on a schedule, exports CSV, plugs into Slack or n8n through Apify's integrations. It also works as an MCP tool inside Claude or Cursor. Pricing is per result, $4 per 1,000 mentions. No subscription. What I actually monitor Brand and product names plus the common misspellings. Competitor names, because share of voice on Blu

2026-07-05 原文 →
AI 资讯

Presentation: Fine Tuning the Enterprise: Reinforcement Learning in Practice

The speakers discuss Agent RFT, OpenAI’s platform for fine-tuning reasoning models via real-time tool interactions and custom reward signals. They explain how reinforcement learning solves complex credit assignment challenges within the context window. They share enterprise success stories, showing how Agent RFT eliminates long-tail token loops and drives extreme efficiency. By Wenjie Zi, Will Hang

2026-07-03 原文 →
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Achieving operational excellence with AI

Frameworks like Lean Six Sigma and business process management (BPM) first gained traction because they promised clarity in the chaos—a structured way to bring order to messy, sprawling operations. Lean Six Sigma emphasized statistical rigor and quality control; BPM created end-to-end maps of how work should flow across departments. Both offered a repeatable way to…

2026-07-02 原文 →
AI 资讯

Teaching AI to run with the turbines

Artificial intelligence may have captured the public imagination through chatbots and image generators, but some of its most consequential use cases are unfolding far from consumer-facing tools. In industries where physical infrastructure, operational continuity, and safety are paramount, AI is becoming a core operating layer. With its sprawling industrial systems and constant stream of operational…

2026-07-02 原文 →