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1. segpha+J4[view] [source] 2025-05-06 15:34:48
>>meetpa+(OP)
My frustration with using these models for programming in the past has largely been around their tendency to hallucinate APIs that simply don't exist. The Gemini 2.5 models, both pro and flash, seem significantly less susceptible to this than any other model I've tried.

There are still significant limitations, no amount of prompting will get current models to approach abstraction and architecture the way a person does. But I'm finding that these Gemini models are finally able to replace searches and stackoverflow for a lot of my day-to-day programming.

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2. jstumm+jH[view] [source] 2025-05-06 19:23:17
>>segpha+J4
> no amount of prompting will get current models to approach abstraction and architecture the way a person does

I find this sentiment increasingly worrisome. It's entirely clear that every last human will be beaten on code design in the upcoming years (I am not going to argue if it's 1 or 5 years away, who cares?)

I wished people would just stop holding on to what amounts to nothing, and think and talk more about what can be done in a new world. We need good ideas and I think this could be a place to advance them.

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3. ssalaz+gg1[view] [source] 2025-05-06 23:55:42
>>jstumm+jH
I code with multiple LLMs every day and build products that use LLM tech under the hood. I dont think we're anywhere near LLMs being good at code design. Existing models make _tons_ of basic mistakes and require supervision even for relatively simple coding tasks in popular languages, and its worse for languages and frameworks that are less represented in public sources of training data. I am _frequently_ having to tell Claude/ChatGPT to clean up basic architectural and design defects. Theres no way I would trust this unsupervised.

Can you point to _any_ evidence to support that human software development abilities will be eclipsed by LLMs other than trying to predict which part of the S-curve we're on?

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4. fragme+8k1[view] [source] 2025-05-07 00:40:29
>>ssalaz+gg1
https://chatgpt.com/c/681aa95f-fa80-8009-84db-79febce49562

it becomes a question of how much you believe it's all just training data, and how much you believe the LLM's got pieces that are composable. I've given the question on the link as an interview questions and had humans been unable to give as through an answer (which I chose to believe is due to specialization on elsewhere in the stack). So we're already at a place where some human software development abilities have been eclipsed on some questions. So then even if the underlying algorithms don't improve, and they just ingest more training data, then it doesn't seem like a total guess as to what part of the S-curve we're on - the number of questions for software development that LLMs are able to successfully answer will continue to increase.

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