A model router that learns how to route around your codebase based on your requests. Runs silent evals in the background. Extracts learnings. Locally, your machine. I've been working on this for the past 2 months. Coming soon in @rolemodeldev

Flash models from DeepSeek, GLM and Qwen are creating a problem: they overperform on benchmarks which makes it hard to know when and how to use them. Even in this benchmark in @rolemodeldev which uses a judge to grade the solutions from each model side by side and set scores relative to each other, V4 Flash outperforms V4 Pro and Terra High. It certainly isn't better for long-running work because it eventually loses the plot and stops following instructions. It reinforces the idea that benchmark scores cannot be used for model routing; instead we must run replay and evaluations on actual traces from work you have been doing in your repo. That is coming in a soon to be released version of role-model.

This AI review rated @rolemodeldev 8.5/10 so you know it's good aiindigo.com/blog/role-model…

Stop thinking. Let @rolemodeldev decide.

99% cache hit rate with @rolemodeldev and DeepSeek Harness

the flat line at around 98% is the cache hit rate in @rolemodeldev router it's actually higher than that, usually >99% per request. the reason the graph shows 98% is that it averages the cache hit rate per request, and hit rate is always lower for the first few requests in a new session since every part of the context is new, or when you start doing something new in the same session. so yes, the role-model router helps you maintain a cache hit rate above 99%.

BOOM. Cache hit.