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Comment by Sheeny96 | Hacker News Reader

Forgive my naive understanding of LLMs - but how do you get semantic understanding of a codebase, such that it knows what changes to make/why/where, without a wider understanding of language more broadly?

I'm using the word understanding loosely there, but I couldn't think of another word.

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Depends on what exactly you want to change. Lsp can do a lot but only in very simple changes.

Intellij when I used to use it had a lot great features like refactoring, extracting part of code as a function, renaming and creating empty classes/boilerplate but that's it

In current job I can order LLM to take data sink from other endpoint and write new with given URL. It will fetch from endpoint, check what it gives, compare with other and write new sink. Then it needs polishing because it always create something as awful as possible with cloning data all around but the most boring and soul sucking part is done

This is why I am working on https://github.com/spockz/semantic-editor. To bring more powerful editing functionality to agents. In a way that attaches to their chain of thought and deals with their probabilistic framing in json so it all works out cheaper and faster.

LLMs at my work have access to Slack chat history, the Wiki, the complete git history, (AI generated) notes from every in person meeting team meeting, JIRA, all code reviews, and all production logs, as well as your personal email (when you run it yourself)

They can typically explain the history of something faster than any human

How does your agent wire all the information? Does it use MCP for all of them or do you use another method to access or collect the data? and how does it resolve contradictions (one source say one thing, another say otherwise).

It's a totally fair question. I'm personally wondering if there's a half way point. Some kind of structured language that isn't plain English that a "dumb" LLM is able to parse. It could be human written, or it could be written by a "smart" LLM at a greater cost.

The more we go in that direction we closer we get to reinventing frameworks in a more compute heavy way.

It just so happens that there's no framework (outside silos) for many specific tasks, and AI is the workaround to surface those patterns.

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