A builder ran an autonomous AI agent 24/7 for 28 days. Here’s what actually happened.
r/SideProject founders are listing agents for $10,000 on ClawMart, managing six revenue streams by proxy, and running entire solo businesses through MCP servers. One builder just documented 28 days of fully autonomous agent runtime — costs, failures, and all.
A builder ran a fully autonomous AI agent continuously for 28 days and documented the full experience — what failed first, what cost more than expected, what ran without any human intervention for an entire month.
This isn’t a demo or a pitch. It’s an operational post-mortem from someone who actually did it. The failure modes they hit are the ones you’ll hit. The costs are real. The surprises are the curriculum you need before you deploy anything autonomous for longer than a weekend.
Why it matters: Every founder building with agents designs for the upside. This post is the other side — the drift, the edge cases, the unexpected bills. Read it before you go live. Read →
Simon Willison published a practical experiment combining Starlette 1.0 with Claude skill files. The challenge: Starlette 1.0 broke its API significantly enough that Claude’s training data couldn’t reliably generate working code. His fix — have Claude clone the Starlette repo and build a skill document from current source — produced a working GitHub Issues-style app with SQLite, aiosqlite, and Jinja2.
The pattern is the point. Claude skills let you hand an agent current documentation as executable context. Any library that moves fast enough to outrun training data is now a candidate for this approach.
Why it matters: Skills are becoming the distribution layer for agent capabilities. If you expose your API as a Claude skill, every Claude Code user can call it without writing a client — no SDK required. Read →
Sashiko is a live, production system monitoring Linux kernel mailing lists and reviewing proposed patches using AI agents. It’s open source, backed by the Linux Foundation, and Google provides the compute.
The benchmark: tested against 1,000 upstream commits with confirmed bugs, Sashiko caught 53.6% of them. Every single one of those bugs had previously passed human-driven code review.
Why it matters: The “agents can’t handle serious engineering work” argument now has a very specific rebuttal. If agents are catching bugs in Linux kernel patches that human reviewers missed, they can review your code. Read →
A Claude Code skill file that uses parallel agents to write long-form structured documents. The builder’s benchmark: 86 pages in 6 hours. Built as a thesis writer, but the parallel-agent architecture is directly stealable for any long-form generation task — documentation, reports, proposals. Drop the skill file into your Claude Code setup and it runs immediately. Read →
This edition was produced by a fully autonomous 5-agent pipeline — the same kind of agentic system we cover every day.
Atlas (DeepSeek) scanned ~50 sources and surfaced 80+ stories. Curator (Claude) filtered to 7. Scribe (Claude) wrote the draft — one restart after brief mismatch, caught automatically. Mercury (DeepSeek) formatted for delivery.
Cost: Claude agents run on the founder’s Max subscription (~$0 marginal). DeepSeek API costs tracked separately — reported starting next edition.
The Heartbeat is the daily pulse of the agentic economy. Built on Paperclip.
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