Ron Bee Software

EdgeFit AI — Hardware-Aware Intelligence for On-Device AI EdgeFit AI solves a critical problem in mobile artificial intelligence: determining which AI models and configurations actually work best on real devices. Rather than relying on manufacturer specifications or generic benchmarks, EdgeFit AI combines real Android hardware profiling, native on-device LLM benchmarking, Meta Muse Spark intelligence, and a community-driven evidence network to deliver hardware-aware AI recommendations. Built from scratch for the 2026 Meta Global AI Developer Hackathon, EdgeFit AI introduces an evidence-driven workflow: 1. Profile: Analyze device hardware, memory, storage, and runtime constraints. 2. Recommend: Identify compatible AI models and configurations. 3. Predict: Use Meta Muse Spark to estimate inference performance before execution. 4. Benchmark: Measure actual on-device LLM performance using native llama.cpp inference. 5. Compare: Evaluate predicted versus measured performance and preserve reproducible results. 6. Improve: Use Muse Spark to propose bounded follow-up experiments, subject to explicit user authorization. 7. Contribute: Allow users to voluntarily share verified benchmark evidence through a secure, consent-controlled Global Evidence Network. The Global Evidence Network aggregates real-world performance measurements under strict provenance and qualification rules. The backend is deployed and has accepted genuine device contributions; public aggregate publication remains gated until cross-device evidence requirements are met. EdgeFit AI's distinguishing innovation is the connection between AI-generated predictions, physical-device measurements, and verifiable evidence. Meta Muse Spark provides intelligent predictions and experimental reasoning, while the Android runtime independently measures performance. Estimated results are never misrepresented as observed measurements. My vision is to make running AI locally more accessible, efficient, and trustworthy across the diverse Android device ecosystem. Especially when the hardware on a fifty-dollar android and a thousand-dollar android phone each perform running large language models differently. Your Device. Your Model. Your Evidence. GitHub: https://github.com/Ron-Bee/EdgeFit-AI Live website: https://edgefit-ai.pages.dev/ Official demonstration: https://youtu.be/KyUwmhIL6SM