Feedback Wanted: Building for easier local AI
Posted by Signal_Ad657@reddit | LocalLLaMA | View on Reddit | 9 comments
Just what the post says. Looking to make local AI easier so literally anyone can do “all the things” very easily. We built an installer that sets up all your OSS apps for you, ties in the relevant models and pipelines and back end requirements, gives you a friendly UI to easily look at everything in one place, monitor hardware, etc.
Currently works on Linux, Windows, and Mac. We have kind of blown up recently and have a lot of really awesome people contributing and building now, so it’s not just me anymore it’s people with Palatir and Google and other big AI credentials and a lot of really cool people who just want to see local AI made easier for everyone everywhere.
We just finished automatic multi GPU detection and coordination as well, so that if you like to fine tune these things you can, but otherwise the system will setup automatic parallelism and coordination for you, all you’d need is the hardware. Also currently in final tests for model downloads and switching inside the dashboard UI so you can manage these things without needing to navigate a terminal etc.
I’d really love thoughts and feedback. What seems good, what people would change, what would make it even easier or better to use. My goal is that anyone anywhere can host local AI on anything so a few big companies can’t ever try to tell us all what to do. That’s a big goal, but there’s a lot of awesome people that believe in it too helping now so who knows?
Any thoughts would be greatly appreciated!
OAKI-io@reddit
the biggest win would be boring reliability. people don’t fail at local ai because they lack another launcher, they fail because drivers, model paths, ports, disk space, and weird python deps break in silent ways. if your installer can diagnose and recover cleanly, that is way more valuable than a huge feature list.
Normal-Ad-7114@reddit
I imagine an installer downloading a tuned model in order to debug issues while setting up a local model
Signal_Ad657@reddit (OP)
100% agreed.
amberdrake@reddit
For multi os do you use APE?
Signal_Ad657@reddit (OP)
System detection with dedicated clean paths for setup for different systems and OS configurations. Detects the path needed, and sets things up with a vetted / happy path.
Top_Training5738@reddit
This is probably the direction local AI needs honestly. Most people give up before they even finish setting up CUDA, ROCm, Python deps, or model configs.
The biggest thing I’d focus on is reliability over features though. A simple “it just works” experience with sane defaults, automatic troubleshooting, and clean model management would matter way more than adding 50 integrations nobody fully uses.
Also please don’t let it become another Electron app using 8GB RAM just to launch llama.cpp 😭
Signal_Ad657@reddit (OP)
100% fully agreed. We have definitely reached the “let’s chill on new features” point. Everything lately has been how can we take what we have, and make it as boring and predictable and resource efficient as possible. 100% if someone has a specific extension or use case they want it could probably be added in 20 minutes with modern tools or an afternoon the old fashioned way. We have lots of people building all kinds of cool and creative stuff on their own forks so I agree, our job at this point is just to be a good stable thing people could fork or copy from and let them add all the herbs and spices. If we add all that stuff into the core it just dirties it up. Thank you and agreed.
Ok-Internal9317@reddit
Ok but link?
Signal_Ad657@reddit (OP)
There’s a link my friend, the image that pops up on the post. Adding it here too: https://github.com/Light-Heart-Labs/DreamServer