Terrible stuff and a reddish flag. First of all, gpt signs all over the blog post, reads like a bottom of the barrel linkedin post.
But more importantly, why double and triple down on no RAG? As with most techniques, it has its merits in certain scenarios. I understand getting VC money so you have to prove differentiation and conviction in your approach, but why do it like this? What if RAG does end up being useful? You'll just have to admit you were wrong and cursor and others were right? I don't get it.
Just say we don't believe RAG is as useful for now and we take a different approach. But tripling down on a technique so early into such a new field seems immature to me. It screams of wanting to look different for the sake of it.
I build these systems for a living, and I just made a post about why code is different from natural text: https://news.ycombinator.com/item?id=44107379
RAG is useful for natural text because there is no innate logic in how it's structured. RAG chunking based on punctuation for natural language doesn't work well because people use punctuation pretty poorly and the RAG models are too small to learn how they can do it themselves.
Source code, unlike natural text, comes with grammar that must be followed for it to even run. From being able to find a definition deterministically, to having explicit code blocks, you've gotten rid of 90% of the reason why you need chunking and ranking in RAG systems.
Just using etags with a rule that captures all the scope of a function I've gotten much higher than sota results when it comes to working with large existing code bases. Of course the fact I was working in lisp made dealing with code blocks and context essentially trivial. If you want to look at blub languages like python and javascript you need a whole team of engineers to deal with all the syntactic cancer.
RAG does not just mean similarity search. It means retrieving all relevant content, including the AST dependencies. Whatever you would want to know if you were to answer the query yourself.
Than it must be able to search every book and paper ever written because when it comes to deciding if an algorithm is correct I need to read the original paper that defined it and any updates in the literature since then.
Since that rag system doesn't, and probably will never, exist we are stuck with vector embeddings as the common definition everyone working in the field uses and understands.
If you were to do this by hand, would you search every book and paper ever written? That is not feasible so you have to make a trade-off.
For alternatives to vector search, see GraphRAG and AST parsing; e.g., https://vxrl.medium.com/enhancing-llm-code-generation-with-r... or https://github.com/sankalp1999/code_qa
That's what google scholar is for. Use it to find the meta analysis papers and go from there.
Which incidentally shows why RAG just means vector store + embedding model, since your definition means different things to different people and an implementation can't exist until we figure out AGI.
Cline is, hands down, the most effective agentic coding tool out there. I've extensively daily driven any one of them you can name, and there's nothing even close. The reality is that no one knows how any of this stuff should work, and RAG is just a term that was made up a few years ago; it has no strict formal definition. There's a long way to go before anyone knows what the correct way to do things should be, but they are clearly on the right path from direct experience.
That’s not universally true. I’ve had a better experience with Amazon Q than I’ve had with Cline.
What are the options?
Cursor, Zed, Cline (VSCode), and anything else?
I have not tried either. I wanted to try Cursor but it has a bug that is a blocker.
My workflow involves manual copying and pasting from the browser into my own text editor.
>My workflow involves manual copying and pasting from the browser into my own text editor.
Cline with mcp-playwright: https://github.com/executeautomation/mcp-playwright
There is no free tier like other tools, but it's far more transparent in that regard; it uses an Anthropic/OpenAI/etc. API key directly, and your only costs are direct token usage.
The real "aha" moment with this kind of thing is in realizing that the agent can not just use the tool as provided, but it can write scripts to dynamically execute on the page as well. So it's not just going to a page and dumping a bunch of HTML back into the client; it actually analyzes the DOM structure and writes JS snippets to extract relevant information which are then executed in the browser's runtime.
Amazon Q CLI, Claude Code CLI, Goose with any model, OpenAPI released Codex CLI recently, and Google released Jules.
You can also just use Claude Desktop and hook it up to MCP servers.
I still need to catch up on MCPs, it is still new to me and truth be told, I am clueless about it.
I agree. This is the golden tool that doesn't get the hype. Well as golden as tools get. Hope they can keep delivering. It seems to hit my personal sweet spot.
How is it better than Claude Code? I have decent experience with CC, but haven't used Cline. Are there good tutorials for best practices with it?
What's incredible to me is that it's open source. I've learned a ton about prompting and agent workflows, tool use, etc. from its code.
The post may not be written very well (although it seems mostly fine to me) but it doesn't seem GPT-written at all. Especially as it has obvious typos that LLMs don't make:
> ...and this choice isn't an oversight's a fundamental design decision that delivers better code quality, stronger security, and more reliable results
The limitations of RAG / strengths of a more structured (AST) approach for the case of code are spelled out well. Its punchy and too the point. Don't see these issues.