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Visual QA
Input Text · Image→ Output Text
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1. Read the ConjectureBench paper (arXiv:2510.11986) — nobody had implemented it locally 2. Built the first MLX-native LEAN-FIRE pipeline on Apple Silicon 3. Fine-tuned the first conjecture generation model that exists 4. Verified Dmitry Rybin's breaking counterexample to the 30-year-old Dinitz-Garg-Goemans conjecture within hours The core insight: **conjecture generation IS prediction.** Same pipeline whether you're breaking a 30-year-old math conjecture or sizing a Polymarket position. **The killer app.** Conjecture = Prediction. Formalization = Resolution criteria. Counterexample = The trade.

Text generation
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> **The most comprehensive open-source security agent model — in GGUF.** Runs in Ollama, LM Studio, llama.cpp, vLLM, and any GGUF runtime. 51/51 LoRA tensors merged. Identical to the MLX version. **Both versions are identical** — same 51/51 LoRA tensors, same 745K+ training data, same 12 training rounds. **People are NOT getting the most out of local LLMs.** A 35B MoE at Q4_K_M gives dramatically better output than a 7B model at the SAME speed — because only 3B params activate per token.

Text generation
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