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
Re hallucinating APIs that don't exist - I find this with Golang sometimes. I wonder if it's because the training data doesn't just consist of all the docs and source code, but potentially feature proposals that never made it into the language.

Regexes are another area where I can't get much help from LLMs. If it's something common like a phone number, that's fine. But anything novel it seems to have trouble. It will spit out junk very confidently.

>>tastys+Q91
Since it's trained on a vast a mount of code (probably all publicly accessible Go code and more), it's seen a vast amount of different bespoke APIs for doing all kinds of things. I'm sure some of that will leak into the output from time to time. And to some extent can generalize, so it may just invent APIs.