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Factory Pilot 3D
FactoryPilot 3D is an AI maintenance copilot for production lines. It turns raw factory sensor data into a clear alert, an explained diagnosis, and a safe action, in seconds. A live 3D digital twin of the plant shows every machine in real time. When a sensor goes out of range, FactoryPilot traces the fault back along the line to the machine that caused it, not just the first alarm. It diagnoses the root cause citing the exact page of the machine manual, prices the stop in euros per minute to prioritize by business impact, and either fixes minor issues with safe, reversible actions or escalates risky ones to a human. A rule in code, not the AI, always has the final word on what needs human approval. Every repair is remembered and cited in future diagnoses. The AI runs on AMD Token Factory: DeepSeek for diagnosis, Gemma for safety and visual checks, with Evolus for the factory memory. Autonomy is configurable per machine, from alert-only to fully autonomous. The platform is modular and scalable: a factory is just a configuration, so it adapts to any line from a manual and a sensor list. The water bottling line shown is one example.
18 Oct 2026
Riskgate
Riskgate scans a watchlist of liquid underlyings, builds real Bull Put Spread candidates from Alpaca’s live option chain (real strikes, greeks, and quotes), scores them deterministically, and asks an LLM for a structured second opinion. Every candidate passes through a deterministic Risk Engine checking liquidity, DTE, credit, defined risk, daily loss limits, portfolio risk, duplicate exposure, and position sizing before anything can be submitted as a real (paper) order. The AI never has direct access to `submit_order`. The core rule is simple: the LLM proposes, it never decides. Its output is validated against a strict schema before it can influence anything, and even a valid “APPROVE” can be overridden by the Risk Engine. This was verified through live paper-account testing, which exposed and fixed two real bugs: AI calls on hopeless candidates that should have been pre-screened, and a duplicate-exposure check that failed to update mid-scan. Built on `alpaca-py` (Trading + Market Data APIs, including multi-leg MLEG orders), the official Alpaca MCP Server (read-only query/demo path), and Featherless.ai (open-weight models such as Qwen3). The autonomous loop runs continuously across the multi-day scoring window via GitHub Actions, triggered by cron-job.org after GitHub’s own scheduler proved unreliable at short intervals. Every decision is journaled in a shared Supabase Postgres database with full market/option inputs, AI rationale, and itemized risk checks. The same live journal is accessible from GitHub Actions, local development, and a public Vercel dashboard with four pages—Overview, Daily KPIs, Positions & Trades, and Decision Journal. All pages support real date-range, symbol, and decision filters, making the reasoning behind every trade fully reconstructable.
4 Sep 2026
UrbanAIdea
UrbanAIdea è uno strumento innovativo che consente ai cittadini di partecipare attivamente alla trasformazione e al miglioramento della loro città. Attraverso un'interfaccia semplice e intuitiva, la piattaforma raccoglie segnalazioni, suggerimenti e proposte per affrontare le sfide quotidiane, migliorare i servizi pubblici e promuovere la qualità della vita nei quartieri. Ogni idea o segnalazione viene elaborata grazie a un sistema intelligente basato sull'intelligenza artificiale, che analizza, categorizza e prioritizza le informazioni per facilitarne la gestione da parte delle amministrazioni locali. Il risultato? Quartieri più vivibili, soluzioni più rapide e una città sempre più vicina alle reali esigenze di chi la vive. UrbanAIdea rappresenta un ponte tra cittadini e istituzioni, dove ogni voce conta e ogni azione ha un impatto positivo. Una città più smart, inclusiva e sostenibile è a portata di clic!
1 Dec 2024
