Yep. All AI has done for me is give me the power of how good search engines were 10+ years ago, where I could search for something and find actually relevant and helpful info quickly.

I've seen lots of people say AI can basically code a project for them. Maybe it can, but that seems to heavily depend on the field. Other than boilerplate code or very generic projects, it's a step above useless imo when it comes to gamedev. It's about as useful as a guy who read some documentation for an engine a couple years ago and kind of remembers it but not quite and makes lots of mistakes. The best it can do is point me in the general direction I need to go, but it'll hallucinate basic functions and mess up any sort of logic.

My experience is the same. There are modest gains compensating for lack of good documentation and the like, but the human bottlenecks in the process aren't useless bureaucracy. Whether or not a feature or a particular UX implementation of it makes sense, these things can't be skipped, sped up or handed off to any AI.

Thinking of it, I haven’t seen as many “copy paste from StackOverflow” memes lately. Maybe LLMs have given people the ability to

1) Do that inside their IDEs, which is less funny

2) Generate blog post about it instead of memes

> All AI has done for me is give me the power of how good search engines were 10+ years ago

So the good old days before search engines were drowning with ads and dark patterns. My assumption is big LLMs will go in the same direction after market capture is complete and they need to start turning a profit. If we are lucky the open source models can keep up.

What language/engine did you try it with for gamedev? Just curious if it was weak in a popular engine.

> how good search engines were 10+ years ago

For me this is a huge boost in productivity. If I remember how I was working in the past (example of Google integration):

Before:

    * go through docs to understand how to start (quick start) and things to know
    * start boilerplate (e.g. install the scripts/libs)
    * figure out configs to enable in GCP console
    * integrate basic API and test
    * of course it fails, because its Google API, so difficult to work with
    * along the way figure out why Python lib is failing to install, oh version mismatch, ohh gcc not installed, ohh libffmpeg is required,...
    * somehow copy paste and integrate first basic API
    * prepare for production, ohhh production requires different type of Auth flow
    * deploy, redeploy, fix, deploy, redeploy
    * 3 days later -> finally hello world is working

    * Hey my LLM buddy, I want to integrate Google API, where do I start, come up with a plan
    * Enable things which requires manual intervention
    * In the meantime LLM integrates the code, install lib, asks me to approve installation of libpg, libffmpeg,....
    * test, if fails, feed the error back to LLM + prompt to fix it
    * deploy

It makes me wonder if the majority of all-in on AI folks are quite young and never experienced pre-enshittification search.

Are you using Claude Opus 4.5/6?

If not, then you’re not close to the cutting edge.