𝗧𝗵𝗲 𝗗𝗿𝗶𝗳𝘁 𝗳𝗿𝗼𝗺 𝗖𝗵𝗮𝘁 𝘁𝗼 𝗕𝗮𝗰𝗸𝗹𝗼𝗴

Three months ago, my task management was just a chat window. If I closed the tab, the plan was gone.

Today, it is a Postgres backlog. Three different AI agents—Claude Code, Codex, and Grok—pull work from it. They stamp it with attribution and close it against git history.

I did not set out to build a project management system. I just kept hitting walls. Each time I patched a problem, a new one appeared.

My work is heavy. I run a personal data platform called Nexus. I manage around 100 repositories. During one stretch, I shipped 557,000 lines of code in 35 days. That volume broke every planning method I tried.

Here is how my system evolved:

Phase 1: Conversational Planning The plan lived in chat history. I would think out loud, get a good idea, and start building.

Phase 2: Per-Repo TODO Files I started using TODO.md files in every repository. I stopped using simple checklists. Instead, I wrote small specs. Each item included:

Phase 3: The Operator Backlog (OB) I moved tasks into a Postgres database. This created a global queue. I added an approval gate. A task only becomes real after I review it. This prevents AI from filing garbage into the backlog. I used status lanes:

Phase 4: Multi-Agent Execution The backlog is now a shared queue for multiple AI agents.

The lesson is simple: You do not need Phase 4 to succeed.

If you steal one thing, steal the Phase 2 format. Write your tasks with a status, a trigger, pre-decided steps, and risks. It costs nothing and changes everything.

The most important rule is this: Always plan against the truth. Never plan based on a guess or a summary. A perfect plan built on stale data will fail just as fast as no plan at all.

Source: https://dev.to/niclydon/the-drift-from-chat-to-backlog-how-my-ai-task-planning-evolved-over-three-months-2akg

Optional learning community: https://t.me/GyaanSetuAi