15:I[981,["658","static/chunks/658-a698693f56d0116a.js","183","static/chunks/183-6beeeb801c274aaa.js","474","static/chunks/app/%5Btype%5D/layout-631c4730b83c8223.js"],"Posts"] 16:T7ff,I have a project with ~200k LoC, entirely written with AI codegen.

I have a programming background, have worn many hats including being a founder, CEO and product manager.

These days I use Codex, with GPT-5-Codex + $200 Pro subscription. I code all day every day and haven't yet seen a single rate limiting issue. Have worked with Cursor + Opus/Sonnet a lot before.

We've come a long way. Just 3-4 months ago, LLMs would start doing a huge mess when faced with a large codebase. They would have massive problems with files with +1k LoC (I know, files should never grow this big).

Until recently, I had to religiously provide the right context to the model to get good results. Codex does not need it anymore.

Heck, even UI seems to be a solved problem now with shadcn/ui + MCP or magicui + MCP.

My personal workflow when building bigger new features:

1. Describe problem with lots of details (often recording 20-60 mins of voice, transcribe) 2. Prompt the model to create a PRD 3. CHECK the PRD, improve and enrich it - this can take hours 4. Actually have the AI agent generate the code and lots of tests 5. Use AI code review tools like CodeRabbit, or recently the /review function of Codex, iterate a few times 6. Check and verify manually - often times, there are a few minor bugs still in the implementation, but can be fixed quickly - sometimes I just create a list of what I found and pass it for improving

With this workflow, I am getting extraordinary results.

The project is an "AI meets BI" tool for businesses that need to analyze lots of their business data at scale and coordinate data-based objectives called EdenLM - https://www.edenlm.com/. For that I use AI agents with access to different tools (e.g. for generating and running SQL queries).

The next "frontier" is

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I have a project with ~200k LoC, written with AI codegen. AMA

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