Add SignalBrain Evolver to Machine Learning section

[whitestone1121-web]

Added SignalBrain Evolver - a self-evolving Python code generator (875 lines, zero dependencies, MIT licensed) that demonstrates self-critique scars, GPU proprioception, recursive deliberation, and SHA-256 Merkle audit chain.

[JinyangWang27]

@whitestone1121-web Thanks for you submission. However, this project does not meet our acceptance criteria:

Repository age: The repository was created 1 days ago. We require a minimum of 3 months (or 6 months for Hidden Gem submissions) to ensure project stability.

GitHub stars: 0 stars (minimum 100 required, or strong justification for Hidden Gem).

Please see our CONTRIBUTING.md for full requirements. You're welcome to resubmit once the project has matured and gained community traction.

[whitestone1121-web]

Thank you for the detailed feedback, @JinyangWang27. Understood on the criteria — wanted to add a notable update since submission:

Since opening this PR, the same core architecture powering SignalBrain Evolver was used to achieve a Top-3 finish on OpenAI's Parameter Golf leaderboard (PR #604: github.com/openai/parameter-golf/pull/604) — compressing a 24M-parameter LLM into 15MB using the same self-modifying, GPU-aware optimization principles.

I recognize the age and stars thresholds exist to ensure quality. A few questions:

1. Does the Hidden Gem pathway apply here given the technical depth (875 lines, zero dependencies, SHA-256 audit chain, GPU proprioception)?
2. Is there a "Resubmit" process once the repo crosses the 3-month mark, or should we open a fresh PR?

Happy to make any formatting improvements to the README.md entry in the meantime.

[whitestone1121-web]

Thank you for the detailed feedback, @JinyangWang27. Understood on the criteria — wanted to add a notable update since submission:

Since opening this PR, the same core architecture powering SignalBrain Evolver was used to achieve a Top-3 finish on OpenAI's Parameter Golf leaderboard (PR #604: github.com/openai/parameter-golf/pull/604) — compressing a 24M-parameter LLM into 15MB using the same self-modifying, GPU-aware optimization principles.

I recognize the age and stars thresholds exist to ensure quality. A few questions:

1. Does the Hidden Gem pathway apply here given the technical depth (875 lines, zero dependencies, SHA-256 audit chain, GPU proprioception)?
2. Is there a "Resubmit" process once the repo crosses the 3-month mark, or should we open a fresh PR?

Happy to make any formatting improvements to the README.md entry in the meantime.

[JinyangWang27]

@whitestone1121-web Thanks for the update. The inclusion criteria focus on broadly useful, mature, and widely applicable Python projects.

At the moment, this repository reads more like an experimental/demo implementation of genetic programming for symbolic regression rather than a tool or reference that would benefit most users of the list.

You’re welcome to open a new PR once the project matures further (e.g. clearer practical use cases, benchmarks, or broader adoption).