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🤝 Top Collaborators
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🤓 Latest Submissions
Alpaca AI: Every Trade Debated
Alpaca AI transforms options-income trading into a transparent, risk-aware decision-making process powered by autonomous AI agents. Instead of relying on a single model or black-box trading bot, Alpaca AI employs a council of specialized agents—including Quant, Volatility, Bull, Bear, Risk Officer, and Portfolio Manager agents—to independently analyze opportunities and debate their merits. The system scans a curated universe of securities for covered call and cash-secured put opportunities, generates structured theses from each agent, facilitates adversarial debate, and synthesizes a final TRADE or NO TRADE decision. Before any order reaches the market, a deterministic Risk Gate validates portfolio exposure, capital requirements, position sizing, and contract suitability. Approved trades are executed through Alpaca Paper Trading, while all decisions, debates, and outcomes are recorded in an explainable audit trail. The platform continues monitoring positions and provides portfolio-aware management recommendations throughout the trade lifecycle. By combining autonomous agents, explainable reasoning, and strict risk governance, Alpaca AI demonstrates how AI can participate in financial decision-making responsibly rather than merely automating execution.
4 Sep 2026
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NeuralGate:Intelligent Multi-Model AI Routing Eng
NeuralGate solves a core AI tradeoff: routing every query to a costly cloud model wastes money, while relying only on small local models sacrifices quality. NeuralGate eliminates this by intelligently classifying each query and routing it to the most cost-efficient capable model, leveraging AMD GPU hardware for local inference with cascading fallback to larger models when needed. 3-Tier Classification Engine (100% uptime): SLM Classifier — A QLoRA fine-tuned Llama-3.2-1B (llama3-router), trained on 5,000+ synthetic prompts across Math, Coding, Research, and Casual Chat; runs on Ollama with millisecond latency. ChromaDB Semantic Fallback — Vector similarity matching using local nomic-embed-text embeddings if Tier 1 fails. Regex Safety Net — Deterministic keyword matching, zero dependencies, guarantees output always. Routing: Configs live in routing_rules.yaml. Math→gemma-4-31b-it, Coding→kimi-k2p7-code, Research→gemma-4-26b-a4b-it, Casual→minimax-m3 — each with fallback models. Failures cascade silently, verified via failure-simulation testing. Dashboard: React/Vite dashboard tracks model usage, cost savings, and classification distribution, backed by SQLite in WAL mode (150 concurrent writes, zero lock errors). MLOps: MLflow-backed retraining pipeline (validation → preprocessing → training → registry promotion) with rollback support. Quality: FastAPI backend, 84 tests, 76.48% coverage, CI/CD via GitHub Actions, Docker Compose deployment. A production-ready pattern for cost-aware AI on accessible hardware.
13 Jul 2026
