bogdanoff_2

> fn int foo(){

Eh? What's the point of a `fn` keyword if I can't `grep -r "fn foo"`?

To make your comment more high value, could you explain why you think langchain is bad?

It's just that the choice of object is weird. "Pacing our progress" or "pacing development" is more usual. "Pacing the frontier" I guess is a shorthand for "Pacing [the development of] frontier [models]"

So the issue really isn't "social media", it's the blackbox personalized algorithmic feed.

So that is what we should be addressing.

Things like -> no personalized algorithmic feed for minors. The ability to disable algorithmic feed and revert to historical feed from contacts. Giving the user control over suggestions and feed via the requirement that it should be possible to switch to a "third party algorithm provider" or self-host, while still being able to have full access to the contents and social features of the platform.

I'm curious, what programming language and model/harness do you use, and are there some specific practices that you use?

This makes me think of "we're all just temporarily abled". This proves the point -- sometimes we're just typing on our phone, lol

I could imagine (in a commercial organization) a case for something like: "we're obviously going to be relying more and more on AI, so if bad code comes out of it, instead of just shaming the engineer, let's study together what happened -- what was the chat context, the model, the system prompt, etc... ? What seemed to cause the issue? What processes or systems could we put in place to improve it? Etc... -- and then use the learnings to improve AI use throughout the whole org."

Saying "the language of LLMs", while technically correct, is not exactly precise. It should really be the language of "AI agents", or "chatbots". OG LLMs would just mimic the style of its context based off of (pre)training from text on the Internet.

The kind of quirks you see came from crowd-sourced human-in-the-loop fine-tuning, with not very good work conditions or level of qualification (so resulting in "what non-writers thought good writing looked like", before people had developed the flair to detect these patterns) as well as feedback loops during agentic reinforcement learning and RLVR.

To all the people in the HN comments saying negative things about Claude: why don't you just use other models instead?

Like what?

Is that the experience you had? What if you ask it questions about the codebase?

I find AI agents really great for codebase exploration and understanding how it works. In some ways even moreso than from manual implementation since it's easier to get a global picture.

You can also ask it questions like "are there recurring patterns of how X is done in the codebase?" to which it might answer sometime like "there are actually three competing patterns" and tell you what they are and the exact files, and then you can choose to refactor them if you want.

That's why you first ask the LLM: "tell me how you would do this, and ask me any clarifications and questions you may have, and share any other thoughts you may have. Don't implement yet."

The LLM will look through the codebase, think, and tell you what it would do and if there's any design decisions you would have to make, as well as other things you probably need to be aware of. Then you go through the LLM's output and address all those decisions point by point, asking the LLM more questions if something isn't clear or requires more investigation or you're unsure what to do. And end with "address what I wrote, and share any other thoughts or questions or things to clarify you might still have, don't implement yet"

And you do this back and forth until all the design decisions have been addressed and you feel confident of what the code and architecture will look like, and only then say "ok, implement"

That way you get a lot of the benefits of writing it by hand (being forced to think through what the best design would be and how it would integrate with existing code, and increasing understanding of how existing code works) but it's still much faster. The tool I personally use is Cursor in auto mode.

P.S. actually before even that you first ask the LLM "what is the current state of X in the codebase" and then you ask follow-up questions until you have a good understanding of all the details that are relevant to you. And then you can start having the design/implementation conversation in the same chat context, since having the above information in context is useful.

P.P.S. and you can also ask a bridge question like "can XYZ be cleanly added with how things are currently structured?" or "what would it take to add XYZ to the project?"