UV script enabled me to distribute a MCP client or server in a single file[0].

[0]: https://blog.toolkami.com/mcp-server-in-a-file/

I think both Cursor and Cognition and going in the same direction of SWE-grep[0].

SWE-grep was able to hit ~700tokens/s and Cursor ~300token/s, hard to compare the precision/recall and cost effectiveness though, considering SWE-grep also adopted a "hack" of running it on Cerebras.

I'm trying to kickstart a RL-based code search project called "op-grep" here[1], still pretty early, but looking for collaborators!

[0]: https://cognition.ai/blog/swe-grep [1]: https://github.com/aperoc/op-grep

One of the core design principles at https://github.com/aperoc/toolkami

Great to see progress being made here! I had tons of fun using AlphaEvolve to optimize Perlin Noise[0]

[0]: https://blog.toolkami.com/alphaevolve-toolkami-style/

I recommend reading Shopify CEO Tobi's try[0] for good example of how Ruby's block behavior and meta-programming makes it easy to create a single file, shell wrapper.

[0]: https://github.com/tobi/try/blob/main/try.rb

And I'm looking for a problem to spend my next decade on ...

Inspired by SWE-grep, I've started a repo to educate myself on it here https://github.com/aperoc/op-grep.

I've drafted an architecture, with the steps mainly as so: 1. Collect actions (grep/glob/read) policies either from usage logs or open datasets 2. Optimize by removing redundant actions or parallelization 3. Train model on optimized action policy 4. Release model as a single file, MCP tool (Refer to repo for visual diagram of the architecture)

I've just released the base model and added `openai_forwarder.py` to start collecting action policies.

Looking for more eyes and contributors to make this a reality, thanks!