This kind of thing just makes me think Apple will get to a point where they have good enough local models and good enough harnesses for doing things, and most normal people will just use them… Does the LLM become another interface to computing?
This question hinges on whether model advancement plateaus enough for machine sized models to compare to frontier performance. If it does, the answer is yes. If it doesn’t, the answer is no
More likely, it's going to be whether frontier models advance enough that most people would be willing to pay for them. Right now they don't, but a model you can run locally for free on hardware you already own is very compelling because, while they're not as good as Frontier Models, they're still pretty good.
Tools like Opencode demonstrate that when you box them in tightly enough they can actually be pretty competent.
i believe that for most people on the street, for most tasks, a Chat GPT 3.5 era LLM is sufficient enough. sprinkle in tool calling and other things, and that becomes enough. if you can prioritize that level of a model on-device (baking it in etc), then you can bifurcate AI users between those unwilling to pay and those who are willing to pay A LOT for frontier model performance.
I have thought this for a while. Computing 1.0 meant that we needed to learn the computer’s language to interact with the computer fully. Computing 2.0 is that now the computer has learned our language instead.
Ultimately, the onus at every VC backed local LLM startup is to launch a cloud based offering, because that's the only potential path in sight for venture scale returns.
For now, it seems that direction that lm studio is taking for enterprise market is "local ai deployment support". They recently launched "lm link" which basically uses tailscale to create e2ee connections between computers running lm studio where you are logged in. Granted one can also setup tailscale or their own vpn themselves and use llama-server, but I guess it is simpler to provide it out of the box. In any case I am not sure pivoting from running local models to "cloud offering" (as in providing llm inference at their severs) is a sensible choice granted there is already competition in that space and they have no leverage there. The highest expected path imo would be to be bought by a company that makes (esp open weights) llms and has a similar business plan around enterprise contracts with local deployments.
Built to work with lmstudio, one of the leading easy to use local model servers. LMStudio is the closest to plug-and-play without sacrificing play that I've seen; a harness that works well with it is nothing to sniff at. Its not earth shattering either.
Wouldn't most opensource harnesses work with lmstudio? I assume it has an "openai" style chat API like every other model provider? What's special about it vs langchain deep agents or pi or pydantic-ai?
I guess it lives or dies by the harness quality then - on open models run locally by plug and players and models that fit onto peoples laptops - that is going to be quite the handicap to overcome.
I run lmstudio personally with a range of harnesses (open and closed) and can't say there is that much of a leap to getting everything talking https://lmstudio.ai/docs/integrations
built to work with an OpenAI API compatible endpoint, just like any other harness...
and if someone can't figure out how to write down an address it's very likely they also can't figure out how to make local models not suck for coding, and would likely switch back to codex/cc after 15 minutes anyways.
If you want to use local models, it's more ergonomic than fussing with GGUFs or using LM Studio as a server host and setting up the link to an agent yourself. Although, the model selector is the same as with LM Studio itself which can be overwhelming if you don't know what to look for.
Yup, it's the main reason I don't use LM studio more. I only use it to try out new models/quants, then use llama.cpp directly to host them. LM Studio also doesn't do stuff like audio input and often has bugs that pure llama.cpp doesn't so it can be a net negative for certain use cases.
Sure you can ... simply start by taking donations to benefit all mankind and then once you have done enough of that go private, ipo and join the tres commas club.
Sure. I still don't think it's particularly controversial to acknowledge that the two don't necessarily align either, and that neither really incentivizes the other.
Less unanimous and debatable, but many would say they more often do not align than the opposite.
I'm interested in your thought process. How did you get from his initial statement opening of "A friendly reminder ..." to thinking that this was a scandal?
It's a common discussion trope to imply malfeasance in response to good news, which is a way to non-constructively shut down a conversation particularly without elaboration. In this particular case I legit didn't understand what the OP was actually implying because they did not elaborate.
Tools like Opencode demonstrate that when you box them in tightly enough they can actually be pretty competent.
i believe that for most people on the street, for most tasks, a Chat GPT 3.5 era LLM is sufficient enough. sprinkle in tool calling and other things, and that becomes enough. if you can prioritize that level of a model on-device (baking it in etc), then you can bifurcate AI users between those unwilling to pay and those who are willing to pay A LOT for frontier model performance.
> use the largest frontier open source models through LM Studio Secure Cloud
I run lmstudio personally with a range of harnesses (open and closed) and can't say there is that much of a leap to getting everything talking https://lmstudio.ai/docs/integrations
and if someone can't figure out how to write down an address it's very likely they also can't figure out how to make local models not suck for coding, and would likely switch back to codex/cc after 15 minutes anyways.
Since most people are unaware of this fact.
I don't think we need closed-source developer tools, especially ones where they might restrict access if they decide to start charging for them later.
and using a closed-source, VC-backed app that might change anything in the next update might not be best for privacy
It’s an important criteria to have in mind when you select an application.
Happy to clarify which is who and who is which.
Less unanimous and debatable, but many would say they more often do not align than the opposite.
Is that a problem for you comrad?