Reading through the thread right now.

Commenter: > What % of the code is written by you and what % is written by ai

OP: > Good question!

>

> All the code, architecture, logic, and design in minikv were written by me, 100% by hand. I did use AI tools only for a small part of the documentation—specifically the README, LEARNING.md, and RAM_COMMUNITY.md files—to help structure the content and improve clarity. >

> But for all the source code (Rust), tests, and implementation, I wrote everything myself, reviewing and designing every part. >

> Let me know if you want details or want to look at a specific part of the code!

Oof. That is pretty damning.

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It’s unfortunate that em-dashes have become a shibboleth for AI-generated text. I love em-dashes, and iPhones automatically turn a double dash ( -- ) into an em dash.

I didn’t learn much about icons in this thread, but I did learn that iconographers are a feisty bunch

The author felt pressure to build an explosive startup with tons of early funding. For those feeling similar pressure, look into the concept of “slow burn startups.” These are startups that stick around and make long-term impact.

This repo is fantastic. The README should be the gold standard for OSS. Not to mention how stunning the outputs are. Thanks for sharing.

That’s awesome

I love it

Are there tradeoffs between individual-element and group-element mindsets with regards to project scale? Does the group-element/global architecture mindset require holding the whole program in my head? Is that even possible for large projects?

I have no experience working in C. Obviously, some of the biggest and most important codebases on earth are C.

Q: What’s the smallest step I can take towards my goal?

A: Spend a minute stressing about my goal.

If you aren't any closer to the goal after the step than you were before it, you didn't take a step towards the goal.

Nope! uv takes care of that. uv is a work of art.

Then I should seriously take a look at it. I figured it was just another package manager.

It’s an interesting challenge. Modern recommendation systems grew powerful because of enormous amounts of instant feedback. You can capture clicks and view time on the web. You don’t get that in books.

I see three possible solutions:

1. Google approach: scrape the web for book recommendations and somehow create an ML recommendation system that’s better than Goodread’s 2. Pandora Radio approach: (semi-)manually create classifiers for books (genre, tone, character traits, etc.) and build a recommendation system with that. 3. Practical approach: find book reviewers whose opinions you trust and follow their recommendations.