I am planning on doing a masters but I need some undergrad CS credits to be a qualified candidate. I don’t think I’m going to do the whole undergrad.
Overall my experience has been positive. I’ve really enjoyed Discrete Math and coming to understand how I’ve been using set theory without really understanding it for years. I’m really looking forward to my classes on assembly/computer architecture, operating systems, and networks. They did make me take CS 101-102 as prereqs which was a total waste of time and money, but I think those are the only two mandatory classes with no value to me.
Traditional RAG for code uses vector embeddings and similarity search. We use filesystem traversal and AST parsing - following imports, tracing dependencies, reading files in logical order. It's retrieval guided by code structure rather than semantic similarity.
I highly recommend checking out what the Claude Code team discovered (48:00 https://youtu.be/zDmW5hJPsvQ?si=wdGyiBGqmo4YHjrn&t=2880). They initially experimented with RAG using embeddings but found that giving the agent filesystem tools to explore code naturally delivered significantly better results.
From our experience, vector similarity often retrieves fragments that mention the right keywords but miss the actual implementation logic. Following code structure retrieves the files a developer would actually need to understand the problem.
So yes -- I should have been clearer about the terminology. It's not "no retrieval" -- it's structured retrieval vs similarity-based retrieval. And with today's frontier models having massive context windows and sophisticated reasoning capabilities, they're perfectly designed to build understanding by exploring code the way developers do, rather than needing pre-digested embeddings.
It would be wondeful if some of the tools the projects uses are exposed to build on. Like the tools related to AST, finding definitions, and many more
Your iPhone is crazy powerful with a beautiful picture- if you want to look great during meetings, use it.
No wires- just works
I am not an LLM guy but as far as I understand, RLHF did a good job converting a base model into a chat model (instruct based), a chat/base model into a thinking model.
Both of these examples are about the nature of the response, and the content they use to fill the response. There are so many differnt ways still pending to see how these can be filled.
Generating an answer step by step and letting users dive into those steps is one of the ways, and RLHF (or the similar things which are used) seems a good fit for it.
Prompting feels like a temporary solution for it like how "think step by step" was first seen in prompts.
Also, doing RLHF/ post training to change these structures also make it moat/ and expensive. Only the AI labs can do it