It seems like you're saying "This is a platform to iterate on your agents and make them better over time", but the headline of your site does not communicate that at all.
Your website is full of AI-isms, which makes it hard to parse what you're doing. (3 punchy sentence headline, "X is not Y." , "never able to claim").
Sounds like you have a cool product, try writing at least the front page of your site yourself and it'll be much much more engaging.
What does "changing the specialist" mean? changing the model? The prompt?
Thank you - you are right on both the headline issue and AI-isms. We will work on these.
Changing the specialist means changing: the prompt, the tools, the model, or some harness configurations we expose. In practice, coding agents typically try different tool sets in the first few dozens of variants, then mostly play with the system prompt with some set of "good" tools they find.
Interesting approach. The iterative refinement + verifiable constraints combination seems especially useful. Curious what kinds of tasks have worked best in practice.
The approach seems to work reasonably well on tasks with evals or benchmarks (public or hand-made). We don't have a very large set of use cases yet, but we got good results on seemingly far-apart domains like game level design, predictive modeling and some toy problems on bioinformatics.
It seems like you're saying "This is a platform to iterate on your agents and make them better over time", but the headline of your site does not communicate that at all.
Your website is full of AI-isms, which makes it hard to parse what you're doing. (3 punchy sentence headline, "X is not Y." , "never able to claim").
Sounds like you have a cool product, try writing at least the front page of your site yourself and it'll be much much more engaging.
What does "changing the specialist" mean? changing the model? The prompt?
Thank you - you are right on both the headline issue and AI-isms. We will work on these.
Changing the specialist means changing: the prompt, the tools, the model, or some harness configurations we expose. In practice, coding agents typically try different tool sets in the first few dozens of variants, then mostly play with the system prompt with some set of "good" tools they find.
Interesting approach. The iterative refinement + verifiable constraints combination seems especially useful. Curious what kinds of tasks have worked best in practice.
The approach seems to work reasonably well on tasks with evals or benchmarks (public or hand-made). We don't have a very large set of use cases yet, but we got good results on seemingly far-apart domains like game level design, predictive modeling and some toy problems on bioinformatics.