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by sinuhe695 hours ago |
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upvoteby amluto59 minutes ago|
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> And sometimes it changed the existing code base to make its new code "pass", when it actually should fix its own code instead.

I haven’t tried MiniMax, but GPT-5.2-Codex has this problem. Yesterday I watched it observe a Python type error (variable declared with explicit incorrect type — fix was trivial), and it added a cast. (“cast” is Python speak for “override typing for this expression”.) I told it to fix it for real and not use cast. So it started sprinkling Any around the program (“Any” is awful Python speak for “don’t even try to understand this value and don’t warn either”).

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upvoteby osti5 hours ago|
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That's what I found with some of these LLM models as well. For example I still like to test those models with algorithm problems, and sometimes when they can't actually solve the problem, they will start to hardcode the test cases into the algorithm itself.. Even DeepSeek was doing this at some point, and some of the most recent ones still do this.
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upvoteby edoceo5 hours ago|
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Sounds exactly what a junior-dev would do without proper guidance. Could better direction in the prompts help? I find I frequently have to tell it where to put what fixes. IME they make a lot of spaghetti (LLMs and juniors)
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upvoteby throawayonthe2 hours ago|
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wtf kinda juniors are you interacting with
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upvoteby edoceo2 hours ago|
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Lots of self-taught; looking for an entry level.
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upvoteby heliumtera5 hours ago|
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Maybe the Juniors you have seen are actually retarded?
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upvoteby qinsignificance5 hours ago|
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I have asked GLM4.7 in opencode to make an application to basically filter a couple of spatial datasets hosted at a url I provided it, and instead of trying to download read the dataset, it just read the url, assumed what the datasets were (and got it wrong) is and it's shape (and got it wrong) and the fields (and got it wrong) and just built an application based on vibes that was completely unfixable.

It wrote an extensive test suite on just fake data and then said the app is perfectly working as all tests passed.

This is a model that was supposed to match sonnet 4.5 in benchmarks. I don't think sonnet would be that dumb.

I use LLMs a lot to code, but these chinese models don't match anthropic and openai in being able to decide stuff for themselves. They work well if you give them explicit instructions that leaves little for it to mess up, but we are slowly approaching where OpenAI and anthropic models will make the right decisions on their own

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upvoteby hsaliak1 hours ago|
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this aligns perfecly with my experience, but of course, the discourse on X and other forums are filled with people who are not hands on. Marketing is first out of the gate. These models are not yet good enough to be put through a long coding session. They are getting better though! GLM 4.7 and Kimi 2.5 are alright.
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upvoteby esafak4 hours ago|
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It really is infuriatingly dumb; like a junior who does not know English. Indeed, it often transitions into Chinese.

Just now it added some stuff to a file starting at L30 and I said "that one line L30 will do remove the rest", it interpreted 'the rest' as the file, and not what it added.

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upvoteby XCSme4 hours ago|
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MiniMax 2.1 didn't really work for my data-parsing tasks, a lot of errors.

Instead, this one works surprisingly well for the cost: https://openrouter.ai/xiaomi/mimo-v2-flash

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