zlacker

>>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.

>>segpha+J4
I feel like there are two realities right now where half the people say LLM doesn't do anything well and there is another half that's just using LLM to the max. Can everybody preface what stack they are using or what exactly they are doing so we can better determine why it's not working for you? Maybe even include what your expectations are? Maybe even tell us what models you're using? How are you prompting the models exactly?

I find for 90% of the things I'm doing LLM removes 90% of the starting friction and let me get to the part that I'm actually interested in. Of course I also develop professionally in a python stack and LLMs are 1 shotting a ton of stuff. My work is standard data pipelines and web apps.

I'm a tech lead at faang adjacent w/ 11YOE and the systems I work with are responsible for about half a billion dollars a year in transactions directly and growing. You could argue maybe my standards are lower than yours but I think if I was making deadly mistakes the company would have been on my ass by now or my peers would have caught them.

Everybody that I work with is getting valuable output from LLMs. We are using all the latest openAI models and have a business relationship with openAI. I don't think I'm even that good at prompting and mostly rely on "vibes". Half of the time I'm pointing the model to an example and telling it "in the style of X do X for me".

I feel like comments like these almost seem gaslight-y or maybe there's just a major expectation mismatch between people. Are you expecting LLMs to just do exactly what you say and your entire job is to sit back prompt the LLM? Maybe I'm just use to shit code but I've looked at many code bases and there is a huge variance in quality and the average is pretty poor. The average code that AI pumps out is much better.

>>ableto+gL
I've found, like you mentioned, that the tech stack you work with matters a lot in terms of successful results from LLMs.

Python is generally fine, as you've experienced, as is JavaScript/TypeScript & React.

I've had mixed results with C# and PowerShell. With PowerShell, hallucinations are still a big problem. Not sure if it's the Noun-Verb naming scheme of cmdlets, but most models still make up cmdlets that don't exist on the fly (though will correct itself once you correct it that it doesn't exist but at that point - why bother when I can just do it myself correctly the first time).

With C#, even with my existing code as context, it can't adhere to a consistent style, and can't handle nullable reference types (albeit, a relatively new feature in C#). It works, but I have to spend too much time correcting it.

Given my own experiences and the stacks I work with, I still won't trust an LLM in agent mode. I make heavy use of them as a better Google, especially since Google has gone to shit, and to bounce ideas off of, but I'll still write the code myself. I don't like reviewing code, and having LLMs write code for me just turns me into a full time code reviewer, not something I'm terribly interested in becoming.

I still get a lot of value out of the tools, but for me I'm still hesitant to unleash them on my code directly. I'll stick with the chat interface for now.

edit Golang is another language I've had problems relying on LLMs for. On the flip side, LLMs have been great for me with SQL and I'm grateful for that.