Again, here's what works for me.

When I get an idea for something I want to build, I will usually spend time talking to ChatGPT about it. I'll request deep research on existing implementations, relevant technologies and algorithms, and a survey of literature. I find NotebookLM helps a lot at this point, as does Elevenreader (I tend to listen to these reports while walking or doing the dishes or what have you). I feed all of those into ChatGPT Deep Research along with my own thoughts about the direction the system, and ask it to produce a design document.

That gets me something like this:

https://github.com/leynos/spycatcher-harness/blob/main/docs/...

If I need further revisions, I'll ask Codex or Claude Code to do those.

Finally, I break that down into a roadmap of phases, steps and achievable tasks using a prompt that defines what I want from each of those.

That gets me this:

https://github.com/leynos/spycatcher-harness/blob/main/docs/...

Then I use an adapted version of OpenAI's execplans recipe to plan out each task (https://github.com/leynos/agent-helper-scripts/blob/main/ski...).

The task plans end up looking like this:

https://github.com/leynos/spycatcher-harness/blob/main/docs/...

At the moment, I use Opus or GPT-5.4 on high to generate those plans, and Sonnet or GPT-5.4 medium to implement.

The roadmap and the design are definitely not set in stone. Each step is a learning opportunity, and I'll often change the direction of the project based on what I learn during the planning and implementation. And of course, this is just what works for me. The fun of the last few months has been everyone finding out what works for them.