Someone is going to get fired tomorrow!

I've been beta testing the new OpenFreedom (a fully local AI assistant, core under 1MB) and decided to run a fair head-to-head against two other local agents, Openclaw and the brand-new DeeSeek Harnes. Setup: same all-in-one PC, same model (deepseek-v4-flash), same prompts, caches cleared between runs. Tasks: knowledge, math, financial planning, 3 web-app builds (offline HTML/JS) and repairing a 100KB Python file with 5 planted bugs (verified with a 14-test suite on disk). Both other agents failed all 3 creation tasks (timeouts / failed final calls). OpenFreedom solved everything, 81–88% faster, 88–90% cheaper, using 2 LLM calls per task, I wouldn't have believed it if I hadn't seen it! I tested and re-tested it to be sure. The architecture is the interesting part: the model plans and answers, a deterministic gate executes the tools (no LLM in the loop for actions), memory is a weighted graph. It runs fully local, no cloud. It's still beta (V.8.62 beta) and I'm not hiding the rough edges but the numbers are real and reproducible, full methodology in the PDF below. Full benchmark (PDF): https://openfreedom.it/benchmark/risultati/REPORT-BENCH-20_08.pdf I've never seen architecture like this!