Building a Personal AI Factory

Okay, what is he actually building with this?

I have a problem where half the times I see people talking about their AI workflow, I can't tell if they are talking about some kind of dream workflow that they have, or something they're actually using productively

I keep coming to the same conclusion, which basically is: if I had an LLM write it for me, I just don't care about it. There are 2 projects out of the maybe 50 or so that are LLM generated, and even for those two I cared enough to make changes myself without an LLM. The rest just sit there because one day I thought huh wouldn't it be neat if, and then realized actually I cared more about having that thought than having the result of that thought. Then you end up fighting with different models and implementation details and then it messes up something and you go back and forth about how you actually want it to work, and somehow this is so much more draining and exhausting than just getting the work done manually with some slight completion help perhaps, maybe a little bit of boilerplate fill-in. And yes, this is after writing extensive design docs, then having some reasoning LLM figure out the tasks that need to be completed, then having some models talk back and forth about what needs to happen and while it's happening, and then I spent a whole lot of money on what exactly? Questionably working software that kinda sorta does what I wanted it to do? If I have a clear idea, or an existing codebase, if I end up guiding it along, agents and stuff are pretty cool I guess. But vibe coding? Maybe I'm in the minority here but as soon as it's a non trivial app, not just a random small script or bespoke app kind of deal, it's not fun, I often don't get the results I actually wanted out of it even if I tried to be as specific as I wanted with my prompting and design docs and example data and all that, it's expensive, code is still messy as heck, and at the end I feel like I just spent a whole lot of time actually literally arguing with my computer. Why would I want to do that?

I’ve written a full stack monorepo with over 1,000 files alone now. I’ve started with AI doing a lot of the work, but the percentage goes down and down. For me a good codebase is not about how much you’ve written, but about how it’s architectured. I want to have an app that has the best possible user and dev experience meaning its easy to maintain and easy to extend. This is achieved by making code easy to understand, for yourself, for others.

In my case it’s more like developing a mindset building a framework than to push feature after feature. I would think it’s like that for most companies. You can get an unpolished version of most apps easily, but polishing takes 3-5x the time.

Lets not talk about development robustness, backend security etc etc. Like AI has just way too many slippages for me in these cases.

However I would still consider myself a heavy AI user, but I mainly use it to discuss plans,(what google used to be) or to check it if I’ve forgotten anything.

For most features in my app I’m faster typing it out exactly the way I want it. (with a bit of auto-complete) The whole brain-coordination works better.

I guess long talk, but you’re not alone trust your instinct. You don’t seem narrow minded.

What does the full stack monorepo do?

We just had a story last night about a Python cryptography maintainer using Claude to add formally-verified optimizations to LLVM. I think the ship has sailed on skepticism about whether LLMs are going to produce valuable code; you can follow Simon Willison's blog for more examples.

I don't understand people who are sceptical about whether LLMs can give value. We're way past that, now at the stage where we're trying to figure out how to extract the most value out of them, but I guess humans don't like change much.

It’s nothing special. Not in the realm of anything technical outstanding. I just stated that to emphasize that it’s a slightly bigger project than default single-dev coded SAAS projects which are just a single wrapper. We have workers, multiple white-labeled applications sharing a common infrastructure, data scraping modules, AI-powered services, and email processing pipelines.

I’ve had an impossible learning curve the last year, but as I should rather be vibe-coded biased I still use less AI now to make sure it’s more consistent.

I think the two camps are different in terms of skill honestly, but also in terms of needs. Like of course you are faster vibe-coding a front-end then to write the code manually, but build a robust backend/processing system its a different kind of tier.

So instead of picking a side it’s usually best to stay as unbiased as possible and choose the right tool for the task

They jury is still out, they have spent hundreds of billions, trillions. And want trillions in ROI.

It does really cool stuff now when it is given away for free, but how cool is it when they want you to pay what it actually costs? With ROI and profits on top.