AI-Documentation-Agent

DOZAi is a full-stack AI platform that automates technical documentation from code, APIs, and prompts. It runs entirely on an AMD GPU Droplet (AMD Developer Cloud), hosting: Spring Boot backend (REST API, auth, processing), Python Flask app (visualization engine), PostgreSQL (metadata & history), and Ollama with TinyLlama (local LLM inference). An integration layer connects to cloud AI (Gemini API, OpenRouter). How It Works: Input Collection – Users upload files (PDF, images, text). Spring Boot handles uploads and JWT authentication. Document Analysis – Python Flask extracts text/code blocks, identifies APIs/patterns, prepares data for AI. AI-Powered Generation – Dual-path: local (AMD GPU + TinyLlama for fast, offline gen) or cloud (Gemini 3 Pro via OpenRouter for complex docs). Visualization & Output – Python renders architecture diagrams (PlantUML, Mermaid), flow charts, API docs, and PDF reports with embedded visuals. Storage & Retrieval – PostgreSQL stores docs, metadata, and user history. Users retrieve, regenerate, or export to PDF/DOCX/Markdown. The agent can reverse-engineer code into docs, generate API references, create diagrams from prompts, summarize papers, and translate comments. Running all components on a single AMD GPU Droplet reduces costs while leveraging both local and cloud AI. This demonstrates a complete, production-ready AI documentation workflow.

Autonomous agent for financial prediction

basically the agent has a database of memories from report and research papers from a lot of stocks. The goal is to focus the project into the south american stock market, this agent can predict,manage and invest into MERVAL(Argentina stock exchange). The agent will show u the portfolio, how its going, and also chat about different topics regarding the stocks, having knowledge in almost all stocks regarding Argentina and USA, the bot can make decision based on this reports, give u insights and suggestions on what to do, move or act based on the information. The Agent can also make predictions into the stock market, giving u almost a 360 ecosystem where the agent basically makes everything.

NEXUS AI trading agent

NEXUS is a trust-aware autonomous AI trading agent built for the next generation of financial agents that must do more than just trade. It was designed around a core challenge in AI finance: enabling agents to interact with capital safely, execute strategies autonomously, and demonstrate transparent, verifiable behavior. The project combines three strategy modes (algo, llm, hybrid), four risk profiles, DEX-based execution through a RiskRouter-compatible flow, ERC-8004 agent identity, and EIP-712 checkpoint logging. This means NEXUS is not just an automated trader, but a system built to make every important decision inspectable and accountable. One of its biggest strengths is trust infrastructure. NEXUS aligns with the ERC-8004 vision of identity, reputation, and validation for financial agents. Each agent can operate with a registered on-chain identity, while decision checkpoints create an auditable trail of actions, confidence, and reasoning. That gives the system a verifiable record instead of a black-box trading loop. Another major strength is risk-aware execution. NEXUS is built around policy controls rather than blind automation. It includes risk profiles, decision guards, a local kill-switch, recovery controls, and checkpointed runtime behavior so the agent can prioritize capital protection, drawdown awareness, and safer execution. This matches the hackathon’s emphasis on risk-adjusted performance, validation quality, and transparent agent behavior. NEXUS also stands out through its modular architecture. It combines PRISM-backed market and signal inputs, Groq-hosted Llama reasoning, algorithmic scoring modules, and a live Next.js dashboard for monitoring logs, checkpoints, system state, and controls in real time. In short, NEXUS shows that an autonomous trading agent can be configurable, transparent, risk-aware, and verifiable by design.

Aegis

Clawgov

ClawGov is an end-to-end governance toolkit that brings decentralized security and community oversight to OpenClaw AI agents. Built for the SURGE × OpenClaw Hackathon, it acts as an intelligent firewall and governance layer that prevents agents from executing dangerous or unapproved community skills. The core problem in open AI ecosystems is determining which agent capabilities (skills) are safe to run. Relying on centralized blacklists is slow and vulnerable. ClawGov shifts this power to the community through a four-step pipeline: First, the system Scans external skill registries like Moltbook to flag potentially risky code. Next, it allows users to Propose governance actions (e.g., banning a specific, high-risk scraping tool). The community then Votes on these proposals using SURGE-backed quadratic voting, which ties governance weight to network participation and ensures fair, Sybil-resistant outcomes. Finally, and most importantly, the system Enforces the community's will. Once a vote passes, ClawGov compiles the decision into a strict policy file deployed directly to the OpenClaw Gateway. If an agent attempts to execute a banned skill, the Gateway hook identifies the unauthorized action and blocks it at the network layer. ClawGov bridges Web3 incentive models with cutting-edge AI infrastructure, proving that decentralized communities can successfully govern autonomous AI behaviors in real-time.

DeMemo

DeMemo is a memory layer in AI that is an enterprise-scale, decentralized, artificial intelligence that is aimed at redefining the manner in which AI systems store, own, and reuse knowledge. It is based on the Arc Network blockchain as an infrastructure, introducing identity management through wallets, where users and applications have complete ownership and control over their data. The system implements end-to-end encryption on all conversational and contextual memory, which guarantees privacy and security on all levels. DeMemo uses permanent and decentralized storage with the help of IPFS but indexes all memory records immutably on-chain. This architecture ensures the persistent storage of data in the long term, verifiable integrity, censorship resistance, and easy portability of AI memory across applications, platforms, and ecosystems without depending on the centralized intermediaries.