Building tools for constraint-first AI architecture — starting with L-BOM 💘.
We focus on privacy-preserving, locally-run tools that can be used to analyze and optimize the compute requirements of large language models. We are in early development, but we are excited to share our work and collaborate with others in the space.
| Repo | Description | Status |
|---|---|---|
| L-BOM 💘 | LLM Bill of Materials Generator | ✅ v0.2.0 |
| GUI-BOM | GUI Implmentation of L-BOM | ✅ v0.2.0 |
| HissCheck | AI Powered Python Test Validation | ✅ v0.1.0 |
| Repo | Description | Status |
|---|---|---|
| Ridge Sight ⛰️ | Cross-Repo Pull Request Management Dashboard for GitHub | ✅ Production |
We allow the use of AI code generation tools (e.g. GitHub Copilot) to assist in development, but we require that all code generated by such tools be fully reviewed by a human before being committed to our repositories. This is to ensure that all code meets our quality standards and does not introduce any unintended issues.
Submission of code generated by AI tools should be marked in the pull request description, and the reviewer should verify that the generated code is appropriate and does not contain any security vulnerabilities or other issues.
In addition, pull requests should be limited in scope to a single concern, to make it easier for reviewers to understand the context and implications of the changes being made. This helps maintain the quality and integrity of our codebase while still allowing for the benefits of AI-assisted development. Pull requests that are too broad or that contain multiple unrelated changes may be requested to be split into smaller, more focused pull requests.