> We believe that all agents will long more like this in the future - long running, asynchronous, more autonomous. Specifically, we think that they will:
That model will run, with decent conversation quality, at roughly the same memory footprint as a few Chrome tabs. It's only a matter of time until we get coding models that can do that, and then only a further matter of time until we see agentic capabilities at that memory footprint. I mean, I can already get agentic coding with one of the new Qwen3 models - super slowly, but it works in the first place. And the quality matches or even beats some of the cloud models and vibe coding apps.
And that model is just one example. Researchers all over the world are making new models almost daily that can run on an off-the-shelf gaming computer. If you have a modern Nvidia graphics card, you can run AI on your own computer totally offline. That's the reality.
Martinussen · 34m ago
Data storage has gotten cheaper and more efficient/manageable every year for decades, yet people seem content with having less storage than a mid-range desktop from a decade and a half ago, split between their phone and laptop, and leaving everything else to the "> cloud" - I wouldn't be so sure we're going to see people reach for technological independence this time either.
koakuma-chan · 49m ago
Do you know what "MCP-based methodology" is? I am skeptical of a 4B model scoring twice as high as Gemini 2.5 Pro
dabockster · 41m ago
Yeah I know about Model Context Protocol. But it's still only a small part of the AI puzzle. I'm saying that we're at a point now where a whole AI stack can run, in some form, 100% on-device with okayish accuracy. When you think about that, and where we're headed, it makes the whole idea of cloud AI look like a dinosaur.
koakuma-chan · 33m ago
I mean, I am asking what "MCP-based methodology" is, because it doesn't make sense for a 4B model to outperform Gemini 2.5 Pro et al by that much.
tevon · 23m ago
Very cool! Am using it now and really like the sidebar chat that allows you to add context during a run.
I hit an error that was not recoverable. I'd love to see functionality to bring all that context over to a new thread, or otherwise force it to attempt to recover.
cowpig · 25m ago
I was excited by the announcement but then
> Runs in an isolated sandbox Every task runs in a secure, isolated Daytona sandbox.
Oh, so fake open source? Daytona is an AGPL-licensed codebase that doesn't actually open-source the control plane, and the first instruction in the README is to sign up for their service.
> From the "open-swe" README:
Open SWE can be used in multiple ways:
* From the UI. You can create, manage and execute Open SWE tasks from the web application. See the 'From the UI' page in the docs for more information.
* From GitHub. You can start Open SWE tasks directly from GitHub issues simply by adding a label open-swe, or open-swe-auto (adding -auto will cause Open SWE to automatically accept the plan, requiring no intervention from you). For enhanced performance on complex tasks, use open-swe-max or open-swe-max-auto labels which utilize Claude Opus 4.1 for both planning and programming. See the 'From GitHub' page in the docs for more information.
* * *
The "from the UI" links to their hosted web interface. If I cannot run it myself it's fake open-source
esafak · 3m ago
It's a hosted service with an open source client?
mitchitized · 15m ago
Hol up
How can it be AGPL and not provide full source? AGPL is like the most aggressive of the GPL license variants. If they somehow circumvented the intent behind this license that is a problem.
> Run asynchronously in the cloud
> cloud
Reality check:
https://huggingface.co/Menlo/Jan-nano-128k-gguf
That model will run, with decent conversation quality, at roughly the same memory footprint as a few Chrome tabs. It's only a matter of time until we get coding models that can do that, and then only a further matter of time until we see agentic capabilities at that memory footprint. I mean, I can already get agentic coding with one of the new Qwen3 models - super slowly, but it works in the first place. And the quality matches or even beats some of the cloud models and vibe coding apps.
And that model is just one example. Researchers all over the world are making new models almost daily that can run on an off-the-shelf gaming computer. If you have a modern Nvidia graphics card, you can run AI on your own computer totally offline. That's the reality.
I hit an error that was not recoverable. I'd love to see functionality to bring all that context over to a new thread, or otherwise force it to attempt to recover.
> Runs in an isolated sandbox Every task runs in a secure, isolated Daytona sandbox.
Oh, so fake open source? Daytona is an AGPL-licensed codebase that doesn't actually open-source the control plane, and the first instruction in the README is to sign up for their service.
> From the "open-swe" README:
Open SWE can be used in multiple ways:
* From the UI. You can create, manage and execute Open SWE tasks from the web application. See the 'From the UI' page in the docs for more information.
* From GitHub. You can start Open SWE tasks directly from GitHub issues simply by adding a label open-swe, or open-swe-auto (adding -auto will cause Open SWE to automatically accept the plan, requiring no intervention from you). For enhanced performance on complex tasks, use open-swe-max or open-swe-max-auto labels which utilize Claude Opus 4.1 for both planning and programming. See the 'From GitHub' page in the docs for more information.
* * *
The "from the UI" links to their hosted web interface. If I cannot run it myself it's fake open-source
How can it be AGPL and not provide full source? AGPL is like the most aggressive of the GPL license variants. If they somehow circumvented the intent behind this license that is a problem.