Yugo Ito@yugorou010420066

Most AI trading bots predict direction and bet on it. That is a coin flip in a nice costume, and it is why they lose. Naive hedging loses too: protection costs premium, so an always-on insurance bot just bleeds through a calm week. Liquidity Leak does neither. It holds a long book of eight liquid names (SPY, QQQ, IWM, DIA, AAPL, MSFT, DELL, MDB) and runs an options overlay on top. Protective puts step in as risk rises; defined-risk iron condors and covered calls harvest premium when it is genuinely overpriced. The two dials move in opposite directions, so the agent is never short premium into a sell-off. The split is the whole design. Deterministic Python owns every number: exposure, EWMA volatility, VaR with Cornish-Fisher tails, stress P&L, and all contract sizing. The model owns exactly one judgment, whether this risk is worth hedging right now and how much. It picks a single pre-sized candidate from an admissible set and writes down why. It never chooses a strike, an expiry, or a contract count, and it never does arithmetic. We picked GLM-5.3 on evidence, not vibes: a 30-call transport qualification per candidate, where both DeepSeek models failed on tool-schema instability. Every cap is enforced in code after the model decides. Defined risk stays under 10% of equity, hedge-cost drag under 5%, and selling premium is disallowed entirely above a risk score of 40. Seven named reject codes, all fail-closed. Four sessions on the Alpaca paper account: $100,000 to $106,501.58, up 6.5%, through a 7.2% trough on September 2. Thirty-two fills, every leg a real listed OCC contract. Four days is a sample, not evidence, so the next step is replaying the engine through 2020, 2022 and 2024.

YOUSUN Axion is an AI-powered developer platform built to help teams prepare AI workloads for AMD GPU environments. Many AI projects are still written with CUDA-specific assumptions, heavy FP32 memory usage, unclear benchmark results, and limited ROCm readiness. YOUSUN Axion solves this by giving developers a complete workflow instead of only generic suggestions. The platform scans AI code or error logs, detects CUDA/ROCm compatibility issues, identifies memory and precision problems, and gives an AMD readiness score. It then acts like an autonomous GPU engineer by diagnosing the root cause, recommending practical optimizations, generating code-level patch suggestions, and comparing before/after performance. YOUSUN Axion focuses on real developer needs: reducing debugging time, improving deployment confidence, and making AMD GPU migration easier. It can recommend changes like using BF16 precision, reducing batch size, replacing hardcoded CUDA usage, enabling inference mode, and preparing workloads for ROCm-based execution. The project also includes a benchmark and reporting layer. It can show latency, throughput, memory usage, readiness improvements, and generate a professional AMD Readiness Report that developers can share with teams, clients, or judges. In simple terms, YOUSUN Axion helps developers move from broken, slow, or non-optimized AI workloads to AMD-ready, optimized, benchmarked deployments. It is designed for AI developers, ML engineers, MLOps teams, and builders who want a clearer path to running AI applications on AMD infrastructure.

Lumi is a domain fine-tuned AI voice companion for dementia and Alzheimer's care. Built on AMD MI300X using QLoRA and GRPO reinforcement learning, Lumi handles confusion, repetition, and emotional fragility the way a trained caregiver would — not a generic chatbot. 55 million people live with dementia worldwide. Families cannot provide 24/7 care — and existing AI companions fail them. They reset every session, correct temporal confusion (which is clinically harmful), and leave patients vulnerable to scams costing $3 billion annually in elder fraud. Lumi is the first AI companion purpose-built for this population. Fine-tuned on AMD MI300X using QLoRA and GRPO reinforcement learning — the same technique behind DeepSeek-R1 — Lumi was trained on 8,540 dementia-specific samples processed through our EQ-Matrix framework, covering scenarios across severity levels, emotional states, and scam patterns. Persistent memory via ChromaDB injects prior session context into every new conversation — patients never repeat themselves. A structured output format fires the opening spoken line to TTS before the full response is generated, achieving time-to-first-audio under 1.5 seconds. A binary scam deflection classifier intercepts fraud attempts gently, without alarming the patient. On EQ-Bench 3, Lumi ranked 7th out of 46 models with a Rubric Score of 14.55 — confirming genuine emotional intelligence gains, not just surface fluency. The entire pipeline runs locally on AMD MI300X with ROCm and vLLM. No data leaves the device. No proprietary APIs. Fully private, fully open hardware.

🌎 Online Hackathon | 💻 Dates: 28 August–4 September 2026 | 🏆 $6,000 Prize Pool Build AI trading agents on Alpaca — autonomous agents and trading apps using Alpaca's Trading API, MCP server and CLI. 🌍 Fully online hackathon. Join and build from anywhere in the world.

🌎 Online Hackathon | 📅 August 3–10, 2026| 💻 Powered by native.builder The official native.builder hackathon, hosted by NativelyAI and lablab.ai. Join founders, developers, designers, and builders to create real AI-native applications powered by native.builder: from idea to deployed product, fully in the cloud. 💻 This hackathon is fully online. No travel required.

To participate in the hackathon, simply click Sign up with AMD. If you are not already a member of the AMD AI Developer Program (ADP), you will need to create an account. ADP members can access AI tutorials, experts, and community support. In addition, new members can claim a free month of DeepLearning.ai pro, $100 in AMD GPU cloud credits, and $50 in Fireworks AI API credits. All hackathon participants will receive on hackathon launch additional compute and API credits for their projects. Ready to build what's next on AMD? Sign up with AMD below to secure your spot.