I threw Claude Code at an existing codebase a few months back and quickly quit— untangling its output was slower than writing from scratch. The fix turned out to be process, not model horsepower.

Iteration timeline

==================

• 50 % task success - added README.md + CLAUDE.md so the model knew the project.

• 75 % - wrote one markdown file per task; Codex plans, Claude codes.

• 95 %+ - built Backlog.md, a CLI that turns a high-level spec into those task files automatically (yes, using Claude/Codex to build the tool).

Three step loop that works for me 1. Generate tasks - Codex / Claude Opus → self-review.

2. Generate plan - same agent, “plan” mode → tweak if needed.

3. Implement - Claude Sonnet / Codex → review & merge.

For simple features I can even run this from my phone: ChatGPT app (Codex) → GitHub app → ChatGPT app → GitHub merge.

Repo: https://github.com/MrLesk/Backlog.md

Would love feedback and happy to answer questions!

Like the idea of a self hosted kanban in git, one item you should do in your repo is add the installation instructions to the readme :)

I see its a TS app so I am sure the bun bundle is the install, but always good to include in your 5 min intro.

Is there an alternative that integrates with a Jira instance?

Many of my tasks already exists in forms of a Jira ticket, would be interesting to prompt it to take over a specific ticket & update its ticket progress as well.

Brilliant! Thank you for sharing.

Had similar success with making some more markdown files to help guide the agent but never would have thought of something this useful.

Will try your workflow and backlog on a build this week.

Ooh, definitely trying this out! I ended up homebrewing a whole context maintainance ritual, but that was a pain to get an AI agent to consistently apply, so it spun out into building a whole project management... thing.

This looks much more thought out, thanks for sharing!

That's look fascinating. I will certainly be testing it in the morning! Thanks!

Seems like a great idea. How would that work with multiple branches ? One task might be implemented in a different branch, we might want to have a global overview of all the tasks being coded in the main branch

  All data is saved under backlog folder as human‑readable Markdown with the following format task-<task-id> - <task-title>.md (e.g. task-12 - Fix typo.md).

If every "task" is one .md file, I believe AI have issues editing big files, it can't easily append text to a big file due to context window, we need to force a workaround launching a command line to append text instead of editing a file. So this means the tasks have to remain small, or we have to avoid putting too much information in each task.

This is a good idea. But the screenshots you have show lots of tasks in a project; how are you dispatching tasks (once planned) to an agent, and how are agents navigating the large number of markdown task content you're producing without blowing out their context budget?

Can we change the title to include that this is a tool for AI? I thought it was just gonna be a visualizer.

The tagline from the repo seems fine: "A tool for managing project collaboration between humans and AI Agents in a git ecosystem"