I'm confused why they are working on their own frontier models if they are going to be bought by OpenAI anyway. I guess this is something they were working on before the announcement?

> Enabled from the insight from our heavily-used Windsurf Editor, we got to work building a completely new data model (the shared timeline) and a training recipe that encapsulates incomplete states, long-running tasks, and multiple surfaces.

This data is very valuable if you're trying to create fully automated SWEs, while most foundation model providers have probably been scraping together second hand data to simulate long horizon engineering work. Cursor probably has way more of this data, and I wonder how Microsoft's own Copilot is doing (and how they share this data with the foundation model providers)...

it was only a matter of time, they have too much good data to not train their own models, not to mention that claude API calls were probably killing their profitability.

open source alternative https://huggingface.co/SWE-bench/SWE-agent-LM-32B

though I haven't been able to find a mlx quant that wasn't completely broken.

It's a shame that my development work needs a specific VSCode extension (domain specific language for ERP systems) so my options are VSCode+Copilot or Cursor.

So because they need to have a better business model, they will try to move users to weaker models compared to the best available? This "AI inside the editor" thing makes every day less sense in many dimensions: it makes you not really capable of escaping the accept, accept, accept trap. It makes the design interaction with the LLM too much about code and too little about the design itself. And you can't do what many of us do: have that three subscriptions for the top LLMs available (it's 60$ for 3, after all) and use each for it's best. And by default write your stuff without help if LLMs are not needed in a given moment.

Two takes here. Cynical and optimistic.

Cynical take: describing yourself as a full stack AI IDE company sounds very invest-able in a "what if they're right" kind of way. They could plausibly ask for higher valuations, etc.

Optimistic take: fine tuning a model for their use-case (incomplete code snippets with a very specific data model of context) should work. Or even has from their claims. It certainly sounds plausible that fine-tuning a frontier model would make it better for their needs. Whether it's reasonable to go beyond fine-tuning and consider pre-training etc. I don't know. If I remember correctly they were a model company before Windsurf, so they have the skillset.

Bonus take: doesn't this mean they're basically training on large-scale gathered user data?