I’m an engineering lead, and I kept ending the day with the same questions: What did the team ship? Which projects moved forward? What new issues appeared? Which pull requests were waiting for me? The answers existed, but they were scattered across GitHub, Linear, email, RFCs, and notes. None of the work required to find them was especially difficult. It was fragmented, repetitive, and easy to postpone. After a while, I realized I had become the manual integration layer between our engineering tools. I started using an AI agent to handle context gathering without delegating the decisions themselves.
- Daily engineering context Every night at 10 PM, the agent pulls the day’s commits, connects changes to features and modules, identifies risks or unfinished work, and produces a structured engineering report. The report covers what changed, where it changed, what looks risky, and what still needs follow-up. The next morning starts with a consistent record of the previous day instead of another round of reconstruction from commits, messages, and memory. OpenLoomi product screenshot: generated daily engineering brief and email delivery
- GitHub-to-Linear synchronization We use GitHub for code and technical issues, and Linear for planning and prioritization. Copying an issue manually usually moved the title and description but lost related commits, pull requests, comment history, reproduction details, and project context. An hourly job now checks for new GitHub issues, gathers that context, and creates or updates the matching Linear task. The source URL is used as the identifier, so existing tasks are updated instead of duplicated. Repository-level configuration can override the default field mapping. OpenLoomi product screenshot: scheduled GitHub-to-Linear issue synchronization
- Event-driven PR preparation PR reviews were not a scheduling problem. They were an interruption problem. If I checked GitHub constantly, I broke focused work. If I did not, an important review could sit unnoticed. The agent reacts when a PR is assigned to me, when I am mentioned, or when an important CI check fails. It prepares the CI results, a code summary, possible risks, and draft review comments. Nothing is posted automatically. I can approve the draft, edit it, defer the review, or skip it. The agent handles repetitive preparation; I keep the technical judgment. OpenLoomi product screenshot: PR review decision card awaiting human confirmation The overall loop is simple: Observe → assemble context → propose an action → ask for confirmation → execute and record. This saves more than an hour or two each week. More importantly, I spend less attention switching tools, checking notifications, and wondering what I missed. I used to think an AI engineering assistant was mainly a better way to ask questions about a project. Now I think the more useful model is an agent that follows the work, assembles current context, handles low-risk repetition, and returns control when experience and judgment matter. OpenLoomi is open source: https://github.com/melandlabs/openloomi submitted by /u/Yuuyake
Originally posted by u/Yuuyake on r/ArtificialInteligence
You must log in or # to comment.