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Gemini 2.5 Pro Preview

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Re: Gemini 2.5 Pro Preview

#721

Earlier quoted context omitted.

No, it's just bad. I've been writing a lot of Python code past two days with Gemini 2.5 Pro Preview, and all of its code was like: ```python def whatever(): --- SECTION ONE OF THE CODE --- ... --- SECTION TWO OF THE CODE --- try: [some "dangerous" code] except Exception as e: logging.error(f"Failed to save files to {output_path}: {e}") # Decide whether to raise the error or just warn # raise IOError(f"Failed to save…

I'm seeing it trying to catch blind exceptions in Python all the time. I see it in my colleagues code all the time, it's driving me nuts.

What’s a blind exception?

Re: Gemini 2.5 Pro Preview

#723

Earlier quoted context omitted.

> * "my coworkers will just ruin it" This turns out to be a big issue. I read everything about software design I could get my hands on in years, but then at an actual large company it turned out to not help, because I'd never read anything about how to get others to follow the advice in my head from all that reading.

Indeed. The LLMs will ruin it. They still very much struggle to grasp a code set of any reasonable size. Asking one to make changes to such a code set, and you will get whatever branch the dice told the tree to go down that day. To paraphrase, “LLMs are like a box of chocolates…”. And if you have the patience to try and tack the AI to get back on track, you probably could have just done the work faster yourself.

Hmm, I can't see this as a real problem, because if you let it randomly change your APIs to different APIs the project is going to break. Not everyone is writing client apps.

Re: Gemini 2.5 Pro Preview

#724

Earlier quoted context omitted.

On Limitations of the Transformer Architecture https://arxiv.org/abs/2402.08164 Theoretical limitations of multi-layer Transformer https://arxiv.org/abs/2412.02975

Only skimmed, but both seem to be referring to what transformers can do in a single forward pass, reasoning models would clearly be a way around that limitation. o4 has no problem with the examples of the first paper (appendix A). You can see its reasoning here is also sound: https://chatgpt.com/share/681b468c-3e80-8002-bafe-279bbe9e18... . Not conclusive unfortunately since this is in date-range of its training data…

Your unwillingness to engage with the limitations of the technology explains a lot of the current hype.

Re: Gemini 2.5 Pro Preview

#725

Earlier quoted context omitted.

It seems like the fix is straightforward (check the output against a machine readable spec before providing it to the user), but perhaps I am a rube. This is no different than me clicking through a search result to the underlying page to verify the veracity of the search result surfaced.

Why coding agents et al don't make use of the AST through LSP is a question I've been asking myself since the first release of GitHub copilot. I assume that it's trickier than it seems as it hasn't happened yet.

Microsoft is working on it

Re: Gemini 2.5 Pro Preview

#726
post #664

Earlier quoted context omitted.

> I'm waiting for LLMs to integrate directly into programming languages. What do you mean? How would this look like in your view?

Not OP, but probably similar to how tool calling is managed: You write the docstring for the function you want, maybe include some specific constraints, and then that gets compiled down to byte code rather than human authored code.

[deleted]

Re: Gemini 2.5 Pro Preview

#727
post #496

Earlier quoted context omitted.

From the linked tweet the author seems to be using Gemini through another layer called OpenRouter - it seems quite possible that the issue around lack of clarity of billing/caching could be from that extra layer of indirection.

OpenRouter lets you fund a wallet and spend no more than that. Google will let it go out of control and they purposely delay the billing console by up to 24 hours so if you don't track it all yourself you can get hit big, especially if it is a coding error that uses up to the rate limits.

There are better solutions in the market if you're looking for in-depth observability for LLM inference. For example, use Requesty (requesty at ai) to get very in-depth analytics, breakdowns and logs. You can also set spend limits, create routing policies or allow only a sub-set of models that do not retain data.
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