A developer built a private AI assistant to query their project management and Git history data using only local LLMs. The system leverages a Text-to-SQL approach, translating natural language questions into SQL queries executed against a local SQLite database. This method ensures all data remains on the user's machine, prioritizing privacy and avoiding cloud-based APIs. The assistant uses Ollama to run models like Qwen2.5-coder locally, with a system prompt that includes the database schema, sample values, and few-shot examples to guide the LLM in generating accurate SQL queries and summarizing results. AI
IMPACT Enables developers to build custom, private AI tools for managing structured data, reducing reliance on cloud services.
RANK_REASON The cluster describes a personal project building a tool using existing LLMs and technologies, rather than a new model release or significant industry event.
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