This post describes a local-first approach to personal genomics. Large-scale analysis (processing raw genotype data against the GWAS Catalog, >1M traits) runs entirely on the user’s device.
LLMs are used only for exploration and summarization of selected results and they never see raw genetic data. Users can choose different LLM backends depending on their preferences, including a TEE-based option ( nilAI), local models via Ollama, or hosted models via HuggingFace.
Happy to answer technical questions about the pipeline, privacy tradeoffs, or limitations.
This post describes a local-first approach to personal genomics. Large-scale analysis (processing raw genotype data against the GWAS Catalog, >1M traits) runs entirely on the user’s device.
LLMs are used only for exploration and summarization of selected results and they never see raw genetic data. Users can choose different LLM backends depending on their preferences, including a TEE-based option ( nilAI), local models via Ollama, or hosted models via HuggingFace.
Happy to answer technical questions about the pipeline, privacy tradeoffs, or limitations.