I agree completely.
I just did my first “AI native coding project”. Both because for now I haven’t run into any quotas using Codex CLI with my $20/month ChatGPT subscription and the company just gave everyone an $800/month Claude allowance.
Before I even started the implementation I:
1. Put the initial sales contract with the business requirements.
2. Notes I got from talking to sales
3. The transcript of the initial discovery calls
4. My design diagrams that were well labeled (cloud architecture and what each lambda does)
5. The transcript of the design review and my explanations and answering questions.
6. My ChatGPT assisted breakdown of the Epics/stories and tasks I had to do for the PMO
I then told ChatGPT to give a detailed breakdown of everything during the session as Markdown
That was the start of my AGENTS.md file.
While working through everything task by task and having Codex/Claude code do the coding, I told it to update a separate md file with what it did and when I told it to do something differently and why.
Any developer coming in after me will have complete context of the project from the first git init and they and the agents will know the why behind every decision that was made.
Can you say that about any project that was done before GenAI?
That sounds really powerful, but also like burden shifts to the people that will maintain all this stuff after you're done having your fun.
Tbh, I'm not exactly knocking it, it makes sense that leads are responsible for the architecture. I just worry that those leads having 100x influence is not default a good thing.
> Can you say that about any project that was done before GenAI?
yes. the linux kernel and it's extensive mailing lists come to mind. in fact, any decent project which was/is built in a remote-only scenario tends to have extensive documentation along these lines, something like gitlab comes to mind there.
personally i've included design documents with extensive notes, contracts, meeting summaries etc etc in our docs area / repo hosting at $PREVIOUS_COMPANY. only thing from your list we didn't have was transcripts because they're often less useful than a summary of "this is what we actually decided and why". edit -- there were some video/meeting audio recordings we kept around though. at least one was a tutoring session i did.
maybe this is the first time you've felt able to do something like this in a short amount of time because of these GenAI tools? i don't know your story. but i was doing a lot of this by hand before GenAI. it took time, energy and effort to do. but your project is definitely not the first to have this level of detailed contextual information associated with it. i will, however, concede that these tools can make it it easier/faster to get there.
> Can you say that about any project that was done before GenAI?
… a project with a decomposition of top level tasks, minutes and meeting notes, a transcript, initial diagrams, a bunch of loose transcripts on soon to be outdated assumptions and design, and then a soon-to-be-outdated living and constantly modified AGENT file that will be to some extent added to some context and to some extent ignored and to some extent lie about whether it was consulted (and then to some extent lie more about if it was then followed)? Hard yes.
I have absolutely seen far better initial project setups that are more complete, more focused, more holistically captured, and more utilitarian for the forthcoming evolution of design and system.
Lots of places have comparable design foundations as mandatory, and in some well-worn government IT processes I’m aware of the point being described is a couple man-months or man-years of actual specification away from initial approval for development.
Anyone using issue tracking will have better, searchable, tracking of “why”, and plenty of orgs mandate that from day 1. Those orgs likely are tracking contracts separately too — that kind of information is a bit special to have in a git repo that may have a long exciting life of sharing.
Subversion, JIRA, and basic CRM setups all predate GPTs public launch.