[larve]

I have linked my github above. I don't know how that fares in the bigger scope of things, but I went from 0 opensource to hundreds of tools and frameworks and libraries. Putting a number on "productivity" makes no sense to me, I would have no idea what that means.

I generate between 10-100k lines of code per day these days. But is that a measure of productivity? Not really...

[sarchertech]

>I generate between 10-100k lines of code per day these days.

That’s absolute nonsense.

[irthomasthomas]

He said "generate". This is trivial to do. And probably this is what Amodei meant when he said 90% of code would be AI by now. It doesn't meant that generated code is actually useful and gets checked in.

[larve]

Trivial is a pretty big word in this context. Expanding an idea into some sort of code is indeed a matter of waiting. The idea, the prompt, the design of the overall workflow to leverage the capabilities of llms/agents in a professional/long-lived codebase context is far from trivial, imo.

[larve]

You can look at my GitHub, and I stream full unedited sessions on https://youtube.com/@program-with-ai

[saulpw]

I tuned in to a random spot at a random episode, didn't see any coding but did get to hear you say:

"I'm a person who hates art now...I never want to see art again. All I want to see is like, AI stuff. That's how bad it's gotten. Handmade? nuh-uh. Handmade code? ... anything by humans, just over. I'm just gonna watch pixels."

https://www.youtube.com/live/APkR4qRg1vM?si=XLGmH9uEjG08q-6x...

I watched a little more but was, uh, not impressed.

[larve]

I'm always a very serious person while I wait for people to join the stream. I'm sorry you weren't impressed, but tbf that's not really my goal, I just like building things and yapping about it.

[saulpw]

I'm not sure why you bother yapping about it yourself. It's too human. Just give an LLM a list of lowercase bullet points and have an AI voiceover read them. It'll be 10x more efficient.

[coffeebeqn]

Who’s reviewing 10-100k lines of code per day? This sounds like a slop nightmare

[larve]

I only review what needs to be reviewed, I don’t need to fully review every prototype, shell script, dev tool etc… only what is in the critical path.

But if llms show us one thing, it’s how bad our code review tools are. I have a set of tree sitter helpers that allow me to examine different parts of a PR more easily (one that allows me to diff semantic parts of the code, instead of “files” and “lines”, one that gives me stats on what subsystems are touched and crosscorrelation of different subsystems, one for attaching metadata and which documents are related to a commit, one for managing our design documents, llm-coding intermediary documents, long lasting documents, etc… the proper version of these are for work but here’s the initial yolo from Manus: https://github.com/go-go-golems/vibes/tree/main/2025-08-22/p... https://github.com/go-go-golems/vibes/tree/main/2025-08-22/c... https://github.com/go-go-golems/vibes/tree/main/2025-08-15/d... https://github.com/go-go-golems/vibes/tree/main/2025-07-29/p...).

I very often put some random idea into the llm slot machine that is manus, and use the result as a starting point to remold it into a proper tool, and extracting the relevant pieces as reusable packages. I’ve got a pretty wide treesitter/lsp/git based set of packages to manage llm output and assist with better code reviews.

Also, every llm PR comes with _extensive_ documentation / design documents / changelogs, by the nature of how these things work, which helps both humans and llm-asssisted code review tools.

[larve]

Since I get downvoted because I guess people don’t believe me, I’m sitting at breakfast reading a book. I suddenly think about yaml streaming parsing, start a gpt research, dig a bit deeper into streaming parser approaches, and launch a deep research on streaming parsing which I will print out and read tomorrow at breakfast and go through by hand. I then take some of the gpt discussion and paste it into Manus, saying:

“ Write a streaming go yaml parsers based on the tokenizer (probably use goccy yaml if there is no tokenizer in the standard yaml parser), and provide an event callback to the parser which can then be used to stream and print to the output.

Make a series of test files and verify they are streamed properly.”

This is the slot machine. It might work, it might be 50% jank, it might be entire jank. It’ll be a few thousand lines of code that I will skim and run. In the best case, it’s a great foundation to more properly work on. In the worst case it was an interesting experiment and I will learn something about either prompting Manus, or streaming parsing, or both.

I certainly won’t dedicate my full code review attention to what was generated. Think of it more as a hyper specific google search returning stackoverflow posts that go into excruciating detail.

https://chatgpt.com/share/68b98724-a8cc-8012-9bee-b9c4a77fe9...

https://manus.im/share/kmsyzuoRHfn1FNjg5NWz17?replay=1