Show HN: Bound – local code autocomplete LLM fine-tuned on your repository (bound.sh)

A new tool called Bound has been introduced, designed to enhance local code autocomplete capabilities by fine-tuning a language model specifically on a user's code repository. Developed by engineers, Bound addresses the limitations of traditional cloud-based autocomplete solutions, which often require data transmission to external servers and rely on generic suggestions. By allowing users to send their repository to a secure cloud for model training, Bound customizes autocomplete suggestions to align with specific coding patterns and conventions, significantly improving workflow efficiency without incurring ongoing cloud costs. This innovation represents a significant shift in how developers can leverage AI for coding assistance. By running the tailored model locally post-training, Bound minimizes data privacy concerns and dependency on continuous internet access. The process involves collecting unique code patterns, which means developers receive personalized and contextually relevant suggestions. As AI continues to evolve, tools like Bound illustrate the potential for localized and more secure applications of machine learning in software development, ultimately aiming to boost productivity and reduce reliance on generic AI solutions.