seems expensive. distillation is inherently impossible to defend against. sit back and let your competitors do the hard work. they'll whine and say it's illegal, but they shouldn't complain, they will reap what they sowed.
I've been wondering how they've been able to be so generous with Composer usage with it still making business sense. Seems like this is the answer: presumably they think they'll have a competitive advantage in not just the UX space but the model space as well soon. It's a great strategy, but I do wonder if the moat will be big enough with how fast things are moving and how competitive the model landscape is.
After seeing the last few releases for GPT and Claude, I’m not sure how anyone (else) is gonna build a durable advantage on proprietary model quality.
The capabilities of the top labs’ models have improved so much in just the last few releases, and I definitely foresee a world where they gate those models away behind 1st-party harnesses/tooling.
Real-time or continuous learning is great on paper, but to get this to work without extremely expensive regression testing and catastrophic forgetting is a real challenge.
Credit to the team for taking this on, but I’d be skeptical of announcements like this without at least 3–6 months of proven production deployments. Definitely curious how this plays out.
>We used a Kimi base, with midtraining and RL on top. Going forward, we'll include the base used in our blog posts, that was a miss. Also, the license is through Fireworks.
[0]
And still no mention of Kimi in a new blog post :)
Also apparently the inference provider they use, Fireworks AI, already has built-in API for RL tuning Kimi [1], so I wonder which parts are Cursor's own effort and where Fireworks AI actually deserves credit, especially since they repeatedly brag about being able to create a new checkpoint every 5 hours, which would be largely thanks to Fireworks AI's API/training infrastructure.
I mean, I'm genuinely curious how much effort it would actually take me to go from "here, lots of user data" to "the model gains +1% on benchmarks" to produce my own finetune, assuming I already use a good existing foundational model, my inference provider already handles all the tuning infrastructure/logic, and I already have a lot of usage logs.
What do you think actually happened here in the past week?
They used Kimi, failed to acknowledge it in the original Composer announcement. Kimi team probably reached out and asked WTF? Their only recourse was to publicly disclose their whitepaper with Kimi mentioned to win brownie points about being open about their training pipeline, while placating the Kimi team.
seems expensive. distillation is inherently impossible to defend against. sit back and let your competitors do the hard work. they'll whine and say it's illegal, but they shouldn't complain, they will reap what they sowed.
Back in my day we called this real time training from implicit user feedback.
The engineering challenge here is an order of magnitude bigger though. An LLM is orders of magnitude bigger than a recommender system model. Kudos.
I've been wondering how they've been able to be so generous with Composer usage with it still making business sense. Seems like this is the answer: presumably they think they'll have a competitive advantage in not just the UX space but the model space as well soon. It's a great strategy, but I do wonder if the moat will be big enough with how fast things are moving and how competitive the model landscape is.
After seeing the last few releases for GPT and Claude, I’m not sure how anyone (else) is gonna build a durable advantage on proprietary model quality.
The capabilities of the top labs’ models have improved so much in just the last few releases, and I definitely foresee a world where they gate those models away behind 1st-party harnesses/tooling.
Real-time or continuous learning is great on paper, but to get this to work without extremely expensive regression testing and catastrophic forgetting is a real challenge.
Credit to the team for taking this on, but I’d be skeptical of announcements like this without at least 3–6 months of proven production deployments. Definitely curious how this plays out.
>We used a Kimi base, with midtraining and RL on top. Going forward, we'll include the base used in our blog posts, that was a miss. Also, the license is through Fireworks. [0]
And still no mention of Kimi in a new blog post :)
Also apparently the inference provider they use, Fireworks AI, already has built-in API for RL tuning Kimi [1], so I wonder which parts are Cursor's own effort and where Fireworks AI actually deserves credit, especially since they repeatedly brag about being able to create a new checkpoint every 5 hours, which would be largely thanks to Fireworks AI's API/training infrastructure.
I mean, I'm genuinely curious how much effort it would actually take me to go from "here, lots of user data" to "the model gains +1% on benchmarks" to produce my own finetune, assuming I already use a good existing foundational model, my inference provider already handles all the tuning infrastructure/logic, and I already have a lot of usage logs.
[0] https://news.ycombinator.com/item?id=47459529
[1] https://fireworks.ai/blog/kimi-k2p5
What do you think actually happened here in the past week?
They used Kimi, failed to acknowledge it in the original Composer announcement. Kimi team probably reached out and asked WTF? Their only recourse was to publicly disclose their whitepaper with Kimi mentioned to win brownie points about being open about their training pipeline, while placating the Kimi team.
I'd love to see some data for how much it has improved via this process in the last week
It would be the same as kimi k2.5, the underlying model