alt Hacker NewsThe way I write code with AI is that I start with a project.md file, where I describe what I want done. I then ask it to make a plan.md file from that project.md to describe the changes it will make (or what it will create if Greenfield).
I then iterate on that plan.md with the AI until it's what I want. I then ask it to make a detailed todo list from the plan.md and attach it to the end of plan.md.
Once I'm fully satisfied, I tell it to execute the todo list at the end of the plan.md, and don't do anything else, don't ask me any questions, and work until it's complete.
I then commit the project.md and plan.md along with the code.
So my back and forth on getting the plan.md correct isn't in the logs, but that is much like intermediate commits before a merge/squash. The plan.md is basically the artifact an AI or another engineer can use to figure out what happened and repeat the process.
The main reason I do this is so that when the models get a lot better in a year, I can go back and ask them to modify plan.md based on project.md and the existing code, on the assumption it might find it's own mistakes.
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Sounds like the spec driven approach. You should take a look at this https://github.com/github/spec-kit
I also do that and it works quite well to iterate on spec md files first. When every step is detailed and clear and all md files linked to a master plan that Claude code reads and updates at every step it helps a lot to keep it on guard rails. Claude code only works well on small increments because context switching makes it mix and invent stuff. So working by increments makes it really easy to commit a clean session and I ask it to give me the next prompt from the specs before I clear context. It always go sideways at some point but having a nice structure helps even myself to do clean reviews and avoid 2h sessions that I have to throw away. Really easier to adjust only what’s wrong at each step. It works surprisingly well
I do something similar but I get Claude to review Codex every step of the way and feed it back (or visa versa depending on day)
>I then iterate on that plan.md with the AI until it's what I want.
Which tools/interface are you using for this? Opencode/claude code? Gas town?
I do something similar, but across three doc types: design, plan, and debug
Design works similar to your project.md file, but on a per feature request. I also explicitly ask it to outline open questions/unknowns.
Once the design doc (i.e. design/[feature].md) has been sufficiently iterated on, we move to the plan doc(s).
The plan docs are structured like `plan/[feature]/phase-N-[description].md`
From here, the agent iterates until the plan is "done" only stopping if it encounters some build/install/run limitation.
At this point, I either jump back to new design/plan files, or dive into the debug flow. Similar to the plan prompting, debug is instructed to review the current implementation, and outline N-M hypotheses for what could be wrong.
We review these hypotheses, sometimes iterate, and then tackle them one by one.
An important note for debug flows, similar to manual debugging, it's often better to have the agent instrument logging/traces/etc. to confirm a hypothesis, before moving directly to a fix.
Using this method has led to a 100% vibe-coded success rate both on greenfield and legacy projects.
Note: my main complaint is the sheer number of markdown files over time, but I haven't gotten around to (or needed to) automate this yet, as sometimes these historic planning/debug files are useful for future changes.