alt Hacker NewsIt feels like the last mile is so long in this space. I'm a SWE with a ton of experience and I still haven't gotten over the decision paralysis of figuring out which model to run locally, and which inference platform should I use, and will my M1 Macbook Pro actually run anything well enough to be useful, and on and on.
It's kind of embarrassing to admit, but having something Just Work(tm) makes getting started so much easier.
Shouldn’t be embarrassing to admit.
It’s a huge new subject area and it’s completely exhausting to learn. I still feel like a total novice.
I have an M1 Max (more memory bandwidth) so I am not sure how much advice I can give about models, but I would suggest maybe trying the new LFM2.5-24B-A2B. It’s surprisingly good. If you want to mess about with multimodal work, Gemma 4 12B is really impressive. (I mostly use Muse Glimmer but it might be rather slow on an M1 Pro)
If you want to have the space to learn the terminology, tinker with what you can control etc., then you have three easy options: LM Studio, Unsloth Desktop or the llama.cpp native Mac app. But don’t get put off by the llama.cpp command line; most of these tools are running it behind the scenes, and most entries on Hugging Face have recommended parameters.
MLX is an option but so far I haven’t found it to have an edge over simple GGUF on Metal on the M1 at least; it seems it might have more advantages on the later M series chips, but it is usually slower for me.
I have explored running OpenCode inside a VM (you can “remote into it” from the OpenCode GUi desktop app). You might want to look at Paseo, which can do a similar thing but with many more coding agents.
I am quite intrigued by Osaurus; on macOS 26 and up it looks like it will manage a container for you.
You might also want to look at Orchard. Not done this myself yet but I can see the appeal.
https://orchard.andon.dev/ai.html
I am an experienced web dev in my fifties. Six months ago everything I have written above would have been total gibberish to me. Learning some of the open weights stuff has made me feel much less helpless than I think I might if I just developed a Claude dependency. Nothing matters more to me than grasping the fundamentals.
I still don’t really use LLMs for hands-off coding. But I have found that a local LLM can be a useful sidekick; even at the most basic level it’s a better Google that remembers the context of queries.