Original Summary

Most of the automation tools I tried started after I had already done the annoying part. A customer email, GitHub issue or Slack message would come in. I still had to decide whether it mattered, turn it into a clean task, choose the right coding agent, point it at the right repo, watch the run, move the result back to the original thread, and remember what still needed a reply. The agent could write code. I was still the router. I ended up building an open-source local app around that gap. Incoming work lands on one timeline, gets triaged into a task, draft reply or ignore, and coding tasks can run through Claude Code, Codex, Gemini, Qwen Code or another CLI in the actual checkout. The result comes back beside the source request. The boundary that has worked best is reversibility: - inspect, edit and test locally: let the agent work - send, close, push or deploy: put it in a review queue That has been more useful than chasing a fully autonomous worker. The hard part at work is often not whether the model can perform one task. It is keeping context, ownership and the final irreversible action in the right place. I built Taskuary, and a lot of the code was written with AI, then reviewed and tested by me. It runs locally with SQLite, has no required account or telemetry, and is MIT licensed. The repo crossed 102 stars this week: https://github.com/ldbumble/taskuary   submitted by   /u/Appropriate-Path-461 [link]   [comments]

  • 情报分类：工作与职业机会
  • 分类依据：内容涉及招聘、求职或职业发展
  • 信息来源：Reddit · SaaS
  • 发布时间：2026/9/19 03:26:02