Show HN: Trainlint, static checks for ML bugs that don't crash (github.com)

Trainlint, a new offline checker for identifying subtle bugs in machine learning (ML) scripts, has been released to address common pitfalls that don't result in crashes. It evaluates code based on 14 specific rules, targeting issues such as improper data splitting, not clearing gradients, and misconfigured training modes. The tool operates entirely offline without dependencies, making it accessible for use in various environments, and can be easily integrated into existing ML workflows through simple command-line instructions. The checker helps developers avoid these issues proactively by providing guidance on best practices through its coding agent skills. This release is significant for the AI/ML community as it addresses a critical gap: traditional linters struggle to identify context-specific bugs in ML code, which often operates silently without crashing. Trainlint's focused approach not only diagnoses these common mistakes but also educates users on avoiding them in future projects. By introducing structured outputs and modes specifically designed for ML development, Trainlint enhances code reliability, thereby improving the overall quality of ML applications. With its capabilities, AI practitioners can streamline their development processes and focus more on building robust models.