Everyone is shipping AI-generated code right now. Most of it is going straight to main.
Quick verdict: Qodo catches production-grade bugs in AI-g...
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Yeah that's amazing ... so, would Qodo use different "models" (LLMs), or the same models but trained differently, how does that work?
From what I can tell, foundation models (Claude, GPT-5-class) . The differentiation is in how they orchestrate multiple specialized agents and index your codebase for context, not in the underlying weights.
So the difference is not in the underlying capabilities, but really in how you utilize them ...
I'm asking because, if the LLM is able to find those bugs (when orchestrated and directed by Qodo), you'd think that that same LLM should be capable of not making those bugs in the first place when generating the code! :-)
Guess it goes to show that it matters a lot what you're asking of an LLM in affecting what it does or 'can do', even though the fundamental capabilities are all there ... maybe an LLM can be "really good" only at one clearly defined task at the same time, compared to the human brain which just more naturally does multiple things simultaneously?
Generation is autocomplete . The model optimizes for the next plausible token. Review is inversion . The model looks for where "plausible" breaks down at runtime. Same weights, opposing objectives.
Your "one task at a time" framing is close but I'd put it differently: it's not capacity, it's optimization direction. A model writing code isn't asking "where could this fail?" It's asking "what comes next?" Switch the prompt, switch the question.
The human parallel holds . same dev, same brain, writes a bug at 2pm and catches it in review at 4pm. The question is whether you'd actually want a generator that paused mid-write to second-guess itself.
Right, so in the end the difference is in the context that you feed into it ...
Still baffles me that we now have these enormous and opaque artificial "brains", and nobody really understands how it's doing its magic, but we're somehow getting good at coaxing it into doing what we want ;-)
P.S. but with something like Qodo, is it only about the different context that they're supplying to the LLM, as in, a clever prompt? ;-)
Or would they do some additional 'training' on the model, creating a new variant of it? (at this point I realize I might be talking total nonsense, lol)
(well I'm asking something which nobody might have the answer to, because Qodo is probably not disclosing their "secret sauce" ...)
To use the "human brain" analogy again:
You might give that (human) developer, who has to do code reviews, but has little experience with it (yeah okay, this is just fictitious ...) a detailed checklist telling him/her how to do code reviews - or, you might send that developer on a 1 week course, to learn best practices and basic principles of doing code reviews - where the checklist is analogous to a "prompt", or 'context', while the 1 week course would be "additional training of the model" (assuming that the latter is even technically possible at all ...)
The checklist analogy is closer than you're giving yourself credit for. Most of what tools like Qodo do is retrieval and orchestration . figure out which code is relevant, package it with structured review instructions, dispatch specialized agents per concern. The underlying model stays the same.
Fine-tuning (your "1-week course") is expensive and goes stale fast as codebases evolve. RAG and prompt engineering age better because the context is dynamic. You don't retrain the model; you get better at telling it what to look at and what to ask.
The opaque brain does the same thing with a better briefing packet. That's most of the magic.
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Issue resolved. Thanks Daniel!
I find interesting your post because I did not know that you had issues with claude.
The opposite actually . Claude generated the code well. Qodo reviewed it and found six bugs in what Claude produced. The issue was with the generated code, not the tool.