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mindXtrain Demo is Live — Qwen3-8B on a Single MI300X for Less Than $3

Day 5 of the AMD × lablab.ai Developer Hackathon. The demo URL is live: mindx.pythai.net/hackathon. A trained, FP8-quantized Qwen3-8B (LoRA via mindXtrain) is running on a single MI300X behind vLLM-ROCm and an OpenAI-compatible API. No auth required during the hackathon judging window. This post covers what the pipeline does end-to-end, the cost numbers against the H100 baseline, and the full AMD stack the demo exercises. 1. The pipeline you can poke at The endpoint is […]

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Abstract flow-state composition — chosen as the featured image for the Quantum Machine Learning Code Compendium 2026: a research-mastery visual for a reference and recovery atlas of QML code in the year before fault tolerance.

A canonical compendium of quantum machine learning code, in the year before fault tolerance

A canonical compendium of quantum machine learning code in the year before fault tolerance. Framework-agnostic, organized as both reference and recovery atlas — preserving the early code of QML (Wittek’s MOOC, Rigetti’s Grove, Zapata, Microsoft LIQUi|⟩, qiskit-aqua) before it vanishes. PDF mirror included.

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Gödel

core choice logging and self-improvement readiness Current state To show that mindX is or is not a Gödel machine, we need a single, accurate log of core choices: what was perceived, what options were considered, what was chosen, why, and (when available) outcome. 1. Gödel choice schema and global log 2. Instrument core decision points 3. Ollama-driven self-improvement readiness 4. API and UI (optional) 5. File and dependency summary Area File(s) Change Core directive docs/survive.md […]

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