Vals Smith

Vals Smith is an advanced AI benchmarking platform designed to transform GitHub repositories into custom evaluation environments. This tool allows developers and technical teams to test various Large Language Models (LLMs) and coding agents directly on their own source code. The primary goal is to accurately determine which model performs best in a real-world development environment before final implementation, avoiding total reliance on generic metrics.

The operation of Vals Smith follows a structured three-phase process. First, the user connects their repository—whether public or private—using their GitHub account. The tool analyzes the code structure, programming languages, and test surface, ensuring that the source code remains under the user's control at all times. Next, users select the models and evaluation harnesses they wish to compare, allowing for a mix of cutting-edge models and agents within the same test suite. Finally, the platform generates detailed reports featuring pass rates and task-specific breakdowns to facilitate a direct technical comparison.

Vals Smith's technical methodology is characterized by its rigor. The tool identifies merged pull requests that represent verifiable engineering tasks and converts them into autonomous tasks. Each task includes a problem statement, hidden tests, and a reproducible configuration. To ensure benchmark reliability, it validates that tests fail before the original fix and pass after it is applied. During evaluation, the AI receives the repository in its initial state and must resolve the issue without access to the original solution; a task is only marked as valid if it passes the tests and generates no regressions.

Key features and benefits of Vals Smith include:

• Custom Evaluation: Generating test suites based on real business logic rather than public datasets that models may have already memorized.
• Guaranteed Privacy: Data is kept private, ensuring that no external service can access the repository's intellectual property.
• Multilingual Support: The ability to work with various languages such as Python, JavaScript, Shell, Roff, or Jinja, adapting to the tech stack of any computer or server.
• Side-by-Side Comparison: Performance analysis of models like Claude or GPT against the same set of tasks to make data-driven architectural decisions.

This tool is essential for software engineers and AI leads looking to optimize the use of coding agents in professional workflows. By utilizing real-world use cases extracted from repository history, Vals Smith provides a practical and functional performance metric for validating AI-assisted software engineering tools.