<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>mightwork.io</title><description>Experiments in building things that probably shouldn&apos;t work.</description><link>https://mightwork.io/</link><item><title>The Signal Machine</title><link>https://mightwork.io/experiments/organizational-signal-intelligence/</link><guid isPermaLink="true">https://mightwork.io/experiments/organizational-signal-intelligence/</guid><description>I built a production agentic pipeline that ingests customer signals from five sources — NPS surveys, customer success notes, win/loss data, in-app feedback, and internal dogfood — clusters them semantically, runs them through 23 LLM agents, and delivers prioritized product specs as structured documents routed to the right PM weekly. The question wasn&apos;t whether AI could help a PM work faster. It was whether a PM could build the intelligence infrastructure an entire organization runs on.</description><pubDate>Sun, 01 Mar 2026 00:00:00 GMT</pubDate></item><item><title>Seven APIs, One Binary</title><link>https://mightwork.io/experiments/seven-apis-one-binary/</link><guid isPermaLink="true">https://mightwork.io/experiments/seven-apis-one-binary/</guid><description>I built a production CLI tool in Go that migrates data from Asana, Monday.com, Trello, Jira, Airtable, Notion, and Wrike into Smartsheet — non-destructive, resumable, and full-fidelity. I am not a Go engineer. The question I was actually running was whether product thinking transfers directly to engineering problems when the implementation layer is handled by AI, and what that handoff actually looks like in practice.</description><pubDate>Sun, 01 Feb 2026 00:00:00 GMT</pubDate></item></channel></rss>