Hey everyone! Yagil the founder of LM Studio here. If you want to take Bionic for a spin with GLM 5.2 / Kimi K2.6 / Kimi Coder K2.7, email your lmstudio.ai username to hn-jul16@lmstudio.ai and I'll load your account with some credits!
Try it out for coding (in a "Code" project) and document creation / manipulation (in a "Work" project). In Work projects we have automatic checkpointing for every change the agent makes. Would love to hear your feedback.
Yagil, thanks for the credits! Lots of fun to try running this locally. I don't understand the negativity in some of the other comments.
This is one of the better agent harnesses I've seen for inspecting reasoning chains, which is super useful for me. Sometimes reading the reasoning is better for my needs than reading the response. I appreciate how transparent that is in contrast with Claude Code/Codex/etc.
One question — I saw you mentioned negotiating ZDR with your "providers" — are you hosting the models yourselves, or is another entity hosting them? If another entity, which group?
I have never previously tried a agentic harness for local models yet, but I really love LM Studio so I gave Bionic a shot immediately after reading this!
First impression: it works great. I use Codex as my main agent, and the UI looks similar enough that it's familiar and simple to get started. I just pointed it to my existing LM Studio models library, ran Qwen3.6 35B, and the results are exactly what I would hope for.
I did notice some rough edges that might be worth improving, however:
- Current working directory is not the clearest on the main page of the app. It shows the project name, but is missing the prominent working directory label like Codex has.
- The model seems to load when you hit Enter, but it shows "Working" instead of of "Loading model".
- There doesn't seem to be a way to pre load the model, it seems like you have to send it something to load the model.
- I don't see a way to easily unload the model like the eject button in LM Studio without quitting the app
- I pointed it to a directory called "GitHub & Projects" and it somehow ended up making a new folder called "GitHub & Projects". Yes, I know the name is weird but it shouldn't have done that.
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?
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.
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
I disagree. The point of the frontier models is to do everything as well as possible ("AGI" race or whatever) but smaller models with some RL are going to be the clear winner for a ton of use cases. Think about all the use cases for LLMs that would never be economical at frontier inference costs, and in no way need it. You don't need or even want a phd polymath helping you with small productivity tasks that most people use computers for every day. It's often overwrought and annoying. I don't even really like the frontier models for coding for this reason. They're constantly blowing up scope and you have to fight it constantly.
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.
Neural machines were always going to be an alternative computing paradigm to von Neumann machines. Had it not been for Minsky we would arguably have gotten to a point where they're useful sooner. But why do you say that as if it's a small thing?
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.
Why wait? People are already doing their work on OpenAI and Anthropic's servers, Apple Intelligence servers could quickly subsume any "local" model work that you want to do.
That way everyone has access, even with older devices, and it's a subscription! Then Apple can tie their APIs into the ecosystem you love at a flat cost you can afford. No need to support local model integration in the first place, problem solved.
based on their apple intelligence demos they are optimizing their products for their core demo of 55-95 year old boomers who talk out loud to think and read every page of the nytimes. you are miles away from the US consumer product experience here.
Why would I use this over any other harness? I suppose they're wrapping it all up in a nice package for enterprise, especially ones that want to control their LLM usage for cost and data security compared to the cloud frontier models.
There’s actually not a lot of good harnesses that aren’t slopped together python or JS codebases, which are actually model-agnostic and don’t do some really silly things like bloating the context, too much compaction etc.
There is no way I’m running a python or JS agent which is probably vibe coded, it’s just too much risk from a security and supply chain standpoint.
I don’t vibe code though, to the extent I actually use agents to generate code it all gets reviewed and it’s essentially exactly what I would have written myself. It’s mostly things like mechanical refactors, boilerplate generation, etc.
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.
I am aware of it, and I dabble with Unsloth Studio and use the llama-server approach.
I would obviously prefer an open source, open weights stack.
But I guess a paradox is that as long as there are open source options I could use, a solid agentic environment that I can use with my own open weights is something I might pay for, in a similar sort of way to paying for a Mac when I could use only Linux.
If someone wanted to make their entire income from, say, making the BBEdit of LLM harnesses, that would be a viable strategy. Sooner or later people need to make an income somewhere. My own feeling is that Apple should acquire LM Studio, but if they said "this is $X per year" I might consider it, given the attention to detail.
I seriously don't understand the apple glazing. They are pretty much completely out of the picture and are one of the most anti consumer anti open-source companies. Yet developers STILL hope for Apple acquisitions and being saved... Or something like that...
We have llama.cpp, vLLM, opencode, hermes and countless other mature great open source tools that don't require a anti-consumer company's hands in the jar.
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.
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.
Social progress is literally the story of labor saving devices. Creating more with less labor means a society of relative abundance, with more time for education, creativity, leisure and personal growth. Don't be fooled into thinking LLMs are any different to the tractor, the loom, the calculator, or shoe factories in the third world.
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.
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.
> 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.
I agree. Incidentally, this is exactly what ollama are doing too.
I can definitely see a world where you run stuff local first, for all the reasons we know. Sometimes, you are going to want more powerful models, faster, you’re travelling, etc. You might only use these 5 to 10% of the time, but I’m guessing that’s the market they want.
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?
Yes. I don’t see it either. It looks like a competent app (converging on the same principles as others) but what they are advertising as differentiators simply isn’t relevant to its purpose.
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
Maybe they optimized it really hard for small models. That would be impressive, but hardly a good selling argument. I want a harness that can do both and OpenCode looks just fine for that purpose (and it also has everything else I need).
To me this looks like another case of bundling things that shouldn’t be bundled (the harness with the UI) making both worse off because you can’t individually focus on each component. They’ve done this before, bundling a decent UI with decent inference for IMO no good reason, combining the downsides of each instead of letting people mix+match.
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.
It does! We negotiated ZDR with our providers. We consider that a condition to make things available to our users: in this case cloud inference and web search/extract.
Hey everyone! Yagil the founder of LM Studio here. If you want to take Bionic for a spin with GLM 5.2 / Kimi K2.6 / Kimi Coder K2.7, email your lmstudio.ai username to hn-jul16@lmstudio.ai and I'll load your account with some credits!
Try it out for coding (in a "Code" project) and document creation / manipulation (in a "Work" project). In Work projects we have automatic checkpointing for every change the agent makes. Would love to hear your feedback.
Yagil, thanks for the credits! Lots of fun to try running this locally. I don't understand the negativity in some of the other comments.
This is one of the better agent harnesses I've seen for inspecting reasoning chains, which is super useful for me. Sometimes reading the reasoning is better for my needs than reading the response. I appreciate how transparent that is in contrast with Claude Code/Codex/etc.
One question — I saw you mentioned negotiating ZDR with your "providers" — are you hosting the models yourselves, or is another entity hosting them? If another entity, which group?
Can I use my z.ai coding plan API key?
Thanks! I have sent a mail :-D
I have never previously tried a agentic harness for local models yet, but I really love LM Studio so I gave Bionic a shot immediately after reading this!
First impression: it works great. I use Codex as my main agent, and the UI looks similar enough that it's familiar and simple to get started. I just pointed it to my existing LM Studio models library, ran Qwen3.6 35B, and the results are exactly what I would hope for.
I did notice some rough edges that might be worth improving, however:
- Current working directory is not the clearest on the main page of the app. It shows the project name, but is missing the prominent working directory label like Codex has. - The model seems to load when you hit Enter, but it shows "Working" instead of of "Loading model". - There doesn't seem to be a way to pre load the model, it seems like you have to send it something to load the model. - I don't see a way to easily unload the model like the eject button in LM Studio without quitting the app - I pointed it to a directory called "GitHub & Projects" and it somehow ended up making a new folder called "GitHub & Projects". Yes, I know the name is weird but it shouldn't have done that.
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?
i agree.
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.
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
I disagree. The point of the frontier models is to do everything as well as possible ("AGI" race or whatever) but smaller models with some RL are going to be the clear winner for a ton of use cases. Think about all the use cases for LLMs that would never be economical at frontier inference costs, and in no way need it. You don't need or even want a phd polymath helping you with small productivity tasks that most people use computers for every day. It's often overwrought and annoying. I don't even really like the frontier models for coding for this reason. They're constantly blowing up scope and you have to fight it constantly.
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.
Neural machines were always going to be an alternative computing paradigm to von Neumann machines. Had it not been for Minsky we would arguably have gotten to a point where they're useful sooner. But why do you say that as if it's a small thing?
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.
Why wait? People are already doing their work on OpenAI and Anthropic's servers, Apple Intelligence servers could quickly subsume any "local" model work that you want to do.
That way everyone has access, even with older devices, and it's a subscription! Then Apple can tie their APIs into the ecosystem you love at a flat cost you can afford. No need to support local model integration in the first place, problem solved.
> Does the LLM become another interface to computing?
It already is.
or the other way where primary interfaces ppl use computing arent apple devices like laptops and phones.
based on their apple intelligence demos they are optimizing their products for their core demo of 55-95 year old boomers who talk out loud to think and read every page of the nytimes. you are miles away from the US consumer product experience here.
Boomers were born up until 1965, so the youngest boomer is 61 this year.
Please don't slur us older GenX as boomers.
It was fun to try out which local models made better agents. Few thoughts.
* locked to a single dir, so no system wide access.
* no local web search, can be fixed ddg or local mcp.
* no ssh, I want to have it ssh into my server and do the work.
* doesnt show the model being loaded, needs a bar/% counter.
* Can you drag/drop documents in the work dirs, or only + add them?
I love lm-studio, so cant wait to see how this goes. For local I normally use opencode + lmstudio.
Why would I use this over any other harness? I suppose they're wrapping it all up in a nice package for enterprise, especially ones that want to control their LLM usage for cost and data security compared to the cloud frontier models.
There’s actually not a lot of good harnesses that aren’t slopped together python or JS codebases, which are actually model-agnostic and don’t do some really silly things like bloating the context, too much compaction etc.
There is no way I’m running a python or JS agent which is probably vibe coded, it’s just too much risk from a security and supply chain standpoint.
The harness is a security threat because it's vibecoded but the code you're having agents write for you isn't? Lol
I don’t vibe code though, to the extent I actually use agents to generate code it all gets reviewed and it’s essentially exactly what I would have written myself. It’s mostly things like mechanical refactors, boilerplate generation, etc.
I’m worried about the switch in business model here, which is part of the reason I just switched to LM Studio from Ollama.
> use the largest frontier open source models through LM Studio Secure Cloud
Use Unsloth Studio, it's actually open source and I trust Unsloth via their quantized models a lot more than LM Studio.
Any VC-backed company will eventually pivot.
Use now then switch later. Such is the life of a software user.
And also enshittify
At least code isn't a moat anymore. Have a weekend and want your own Discord? Doable.
Prove it.
Well, it’ll LOOK like discord!
A friendly reminder that both LM Studio app and now this new LM Studio Bionic app are closed source.
Since most people are unaware of this fact.
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.
Unsloth Studio is open source, run by the same Unsloth that produces some of the best quants in the business, I'd advise people to switch.
I am aware of it, and I dabble with Unsloth Studio and use the llama-server approach.
I would obviously prefer an open source, open weights stack.
But I guess a paradox is that as long as there are open source options I could use, a solid agentic environment that I can use with my own open weights is something I might pay for, in a similar sort of way to paying for a Mac when I could use only Linux.
If someone wanted to make their entire income from, say, making the BBEdit of LLM harnesses, that would be a viable strategy. Sooner or later people need to make an income somewhere. My own feeling is that Apple should acquire LM Studio, but if they said "this is $X per year" I might consider it, given the attention to detail.
I seriously don't understand the apple glazing. They are pretty much completely out of the picture and are one of the most anti consumer anti open-source companies. Yet developers STILL hope for Apple acquisitions and being saved... Or something like that... We have llama.cpp, vLLM, opencode, hermes and countless other mature great open source tools that don't require a anti-consumer company's hands in the jar.
Yeah, we already have many open-source agent systems. If you prefer a UI, OpenCode itself has a beta desktop app.
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.
Does anyone know about their stack - is it a native app? It's fairly well designed for what it is in terms of desktop apps.
The desktop app's GUI is electron.js based.
Perfect for when you have unified memory and you want to use as much of it as possible for running the LLM. </sarcasm>
And? Is that a scandal?
Not a scandal ofc, but people use local models mostly for privacy
and using a closed-source, VC-backed app that might change anything in the next update might not be best for privacy
It also seems weird to be closed source and then market yourself as a tool designed for things that are open
No, but you are a lot more vulnerable to enshittification and you don’t have the advantages of open source applications.
It’s an important criteria to have in mind when you select an application.
some people want to make money, others want to improve social progress.
Happy to clarify which is who and who is which.
You can do both
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.
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.
You can do both, until you can't, and then usually making money trumps social progress.
No one involved in LLMs want to improve social progress. That is simply not part of the equasion.
Social progress is literally the story of labor saving devices. Creating more with less labor means a society of relative abundance, with more time for education, creativity, leisure and personal growth. Don't be fooled into thinking LLMs are any different to the tractor, the loom, the calculator, or shoe factories in the third world.
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.
Yes.
Is that a problem for you comrad?
It is, cowboy
it sure is bootlicker
I am not sure I get this. It seems on first glance like just another harness ...
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.
> 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.
I agree. Incidentally, this is exactly what ollama are doing too.
I can definitely see a world where you run stuff local first, for all the reasons we know. Sometimes, you are going to want more powerful models, faster, you’re travelling, etc. You might only use these 5 to 10% of the time, but I’m guessing that’s the market they want.
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?
Yes. I don’t see it either. It looks like a competent app (converging on the same principles as others) but what they are advertising as differentiators simply isn’t relevant to its purpose.
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
Maybe they optimized it really hard for small models. That would be impressive, but hardly a good selling argument. I want a harness that can do both and OpenCode looks just fine for that purpose (and it also has everything else I need).
To me this looks like another case of bundling things that shouldn’t be bundled (the harness with the UI) making both worse off because you can’t individually focus on each component. They’ve done this before, bundling a decent UI with decent inference for IMO no good reason, combining the downsides of each instead of letting people mix+match.
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.
How does it work with lmstudio? Its a separate standalone application.
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.
It’s a harness + UI with really confusing messaging. Made for open models ... why specifically? Zero data retention ... well duh, it’s a client????
Maybe I missed it, but can we use this with other cloud api’s? Like using Deepseek directly from their platform via their OpenAI endpoints?
It says no data retention or training on your data, but I assume that doesn't hold true for the frontier cloud models you connect to?
It does! We negotiated ZDR with our providers. We consider that a condition to make things available to our users: in this case cloud inference and web search/extract.
(I’m the founder of LM Studio)