GPT-4 is over a trillion parameters. Is there any reason to think they have 2.5x more parameters but somehow use 6x less energy?
Again, it's talking about "median Gemini" while being very careful not to name any specific numbers for any specific models.
I suppose it depends on the definition of model.
I currently do consider the transformer weights to be a world model, but having a rigid one based on statistical distributions tend to create pretty wonky behavior at times.
That's why I do agree, relying on your own understanding the code is the best way.
It's amazing seeing these things produce some beautiful functions and designs, and then promptly forget that it exists, and then begin writing incompatible, half re-implemented non-idiomatic code.
If you're blind to what they are doing, it's just going to be layers upon layers of absolute dreck.
I don't think they will get out of cul-de-sacs without a true deductive engine, and a core of hard, testable facts to build on. (I'm honestly a bit surprised that this behavior didn't emerge early in training to be honest).
Though I think humans minds are the same way, in this respect, and fall for the same sort of traps. Though at least our neurons can rewire themselves on the fly.
I know a LOT of people who sparingly use their more advanced reasoning faculties, and instead primarily rely on vibes, or pre-trained biases. Even though I KNOW they are capable of better.
I've always had a subscription to both ChatGPT and Claude, but Claude has recently almost one-shotted major toxic waste dumps from the previous models.
I'll still use ChatGPT, it seems to be pretty good at algorithms, and bouncing ideas back and forth. but when things go off the rails Opus 4.1 bails me out.
https://gist.github.com/WolframRavenwolf/0ee85a65b10e1a442e4...
We gave Gemini CLI a spin, it is kinda unhinged, I am impressed you were able to get your results. After reading through the Gemini CLI codebase, it appears to be a shallow photocopy knockoff of Claude Code, but it has no built in feedback loops or development guides other than, "you are an excellent senior programmer ..." the built in prompts are embarrassingly naive.
Qwen has it's own agent which I haven't used https://github.com/QwenLM/qwen-code
Another is https://github.com/sst/opencode
Yeah but I wouldn't get a generous free tier, and I am Poor lmao.
> I am impressed you were able to get your results
compared to my brief stint with OpenCode and Claude Code with claude code router, qwen-code (which is basically a carbon copy of gemini cli) is indeed unhinged, and worse than the other options, but if you baby it just right you can get stuff done lol
https://www.youtube.com/watch?v=EL7Au1tzNxE
I don't have the energy to do that for most things I am writing these days which are small PoC where the vibe is fine.
I suspect as you do more, you will create dev guides and testing guides that can encapsulate more of that direction so you won't need to micromanage it.
If you used Gemini CLI, you picked the coding agent with the worst output. So if you got something that worked to your liking, you should try Claude.
Definitely. Prompt adherence to stuff that's in an AGENTS/QWEN/CLAUDE/GEMINI.md is not perfect ime though.
>If you used Gemini CLI, you picked the coding agent with the worst output. So if you got something that worked to your liking, you should try Claude.
I'm aware actually lol! I started with OpenCode+GLM 4.5 (via OpenRouter), but I started burning through cache extremely quickly, and I can't remotely afford Claude Code, so I was using qwen-code mostly just for the 2000 free requests a day and prompt caching abilities, and because I prefer Qwen 3 Coder to Gemini... anything for agentic coding.
Typically, I just use something like QwenCode. One of the things I like about it, and I assume this is true of Gemini CLI as well, is that it's explicitly designed to make it as easy as possible to interrupt an agent in the middle of its thought or execution process and redirect it, or to reject its code changes and then directly iterate on them without having to recapitulate everything from the start. It's as easy as just hitting escape at any time. So I tell it what I want to do by usually giving like a little markdown formatted you know paragraph or so that's you know got some bullet points or some numbers maybe a heading or two, explaining the exact architecture and logic I want for a feature, not just the general feature. And then I let it kind of get started and I see where it's going. And if I generally agree with the approach that it's taking, then I let it turn out a diff. And then if I like the diff after reading through it fully, then I accept it. And if there's anything I don't like about it at all, then I hit Escape and tell it what to change about the disc before it even gets to merge it in.
There are three advantages to this workflow over the chat GPT copy and paste workflow.
One is that the agent can automatically use grep and find and read source files, which makes it much easier and more convenient to load it up with all of the context that it needs to understand the existing style architecture and purpose of your codebase. Thus, it typically generates code that I'm willing to accept more often without me doing a ton of legwork.
The second is that it allows the agent to automatically of its own accord, run things like linters, type checkers, compilers, and tests, and automatically try to fix any warnings or errors in that result, so that it's more likely to produce correct code that adheres to whatever style guide I've provided. Of course, again I could run those tools manually, manually and copy and paste the output into a chat window, but that's just enough extra effort and friction after I've gotten what's ostensibly something working that I know I would be likely to be lazy and not do that at some point. This sort of ensures that it's always done. Some tools like OpenCode even automatically run LSPs and linters and feed that back into the model after the diff is applied automatically, thus allowing it to automatically correct things.
Third, this has the benefit of forcing the AI to use small and localized diffs to generate code, instead of regenerating whole files or just autoregressively completing or filling in the middle for things, which makes it way easier to keep up with what it's doing and make sure you know everything that's going on. It can't slip subtle modifications past you, or, and doesn't tend to generate 400 lines of nonsense.