Hey everyone 👋 I’ve just released an open-source AI-powered crypto hedge fund manager that uses Google Gemini 1.5 Flash for market analysis and Bitquery’s on-chain data for live price, volume, and volatility feeds. The system runs as a terminal-based AI trading manager, capable of: Real-time analysis across 10 cryptocurrencies Automated stop-loss, take-profit, and portfolio rebalancing Dual strategy modes — High Risk–High Return and Low Risk–Low Return Integrated on-chain analytics from Bitquery (OHLC, SMA, volatility) 💻 Built with: Node.js, Bitquery API, Google Gemini 🎯 Goal: To show how AI and on-chain data can power institutional-grade crypto hedge fund logic. 👉 Check it out: https://github.com/Kshitij0O7/ai-crypto-hedge-fund-manager

Would love to hear feedback from quants or data engineers experimenting with AI in trading systems.

heynavtoor on X.

Warren Buffett. Charlie Munger. Michael Burry. Cathie Wood. Bill Ackman. All running on your laptop.

It's called AI Hedge Fund. You give it stock tickers. 18 AI agents analyze the company from every angle. Then they vote on whether to buy, sell, or hold.

Not a toy. Not a dashboard. A full multi-agent investment research system.

No Bloomberg Terminal. No $25K brokerage minimums. No financial advisor fees. Just AI agents doing what hedge funds charge 2-and-20 for.

Here's who's on your team:

→ Warren Buffett Agent. Only buys wonderful businesses at fair prices → Charlie Munger Agent. Demands a margin of safety on every pick → Michael Burry Agent. The Big Short contrarian hunting deep value → Cathie Wood Agent. Innovation and disruption. High conviction growth → Bill Ackman Agent. Activist investor. Takes bold positions → Ben Graham Agent. The godfather of value investing. Hidden gems only → Aswath Damodaran Agent. The Dean of Valuation. Story meets numbers → Plus 11 more specialized agents covering technicals, sentiment, risk, and fundamentals

Here's how it works:

→ You enter stock tickers (AAPL, NVDA, TSLA, whatever you want) → Agents pull real financial data. Earnings, balance sheets, insider trades, news → Each agent analyzes the data through their own investment philosophy → A Risk Manager agent checks position sizing and portfolio exposure → A Portfolio Manager agent takes all signals and makes the final call → You get a buy/sell/hold decision with full reasoning from every agent

Here's the wildest part:

You can turn on --show-reasoning and watch each agent explain their logic step by step. Warren Buffett agent breaks down the moat. Michael Burry agent flags the hidden risks. Cathie Wood agent finds the disruption angle. They literally argue with each other.

It has a full backtester. Run your strategy against historical data and see how it would have performed.

Full web UI included. Not just a terminal tool. A real dashboard.

Works with OpenAI, Claude, Groq, DeepSeek, or fully local with Ollama. Your data never has to leave your machine.

Data for AAPL, GOOGL, MSFT, NVDA, and TSLA is completely free. No API key needed.

46.7K GitHub stars. 8.1K forks. Actively maintained.

100% Open Source. MIT License.

If you're into AI agents that actually do real work instead of just chatting, I've curated 10 GitHub repos that are straight fire for automation, data analysis, and building systems that can run (mostly) on autopilot. These range from full-blown trading swarms to stealth browsers and video generators, perfect for anyone experimenting with passive income ideas, side hustles, or just leveling up their workflows. Most are free, self-hostable, and built with modern agent frameworks. They all require some setup (API keys, Docker, etc.), but the payoff is huge if you tinker. Here's a no-fluff breakdown of each:

1. AutoHedge (github.com/The-Swarm-Corporation/AutoHedge)

An autonomous AI hedge fund in a box. Uses swarm intelligence + specialized agents (Director, Quant, Risk Manager, Execution) to analyze markets in real-time, manage risk, and execute trades (currently Solana-focused, more exchanges coming).

Best for: Hands-off algorithmic trading and portfolio management. Install via pip, add your keys/wallet, and let the swarm run. Stars: ~1.6k.

2. Vibe-Trading (github.com/HKUDS/Vibe-Trading)

Your personal AI trading agent. Turn natural language prompts ("Backtest a moving average crossover on AAPL") into full strategies, backtests, portfolio analysis, and exports to TradingView/MetaTrader. Includes 29 swarm presets, 71 finance skills, and persistent memory across sessions.

Best for: Retail traders who want an AI quant team on demand. Docker one-click or pip install. Stars: ~2.9k.

3. Claude Ads (github.com/AgriciDaniel/claude-ads)

A beast-mode paid ads auditor and optimizer built as a Claude Code skill. Runs 250+ checks across Google, Meta, YouTube, LinkedIn, TikTok, Microsoft & Apple Ads. Gives you a health score, parallel sub-agents, industry templates, AI creative generation, financial modeling (CPA/ROAS), A/B test design, and PDF reports.

Best for: Marketers and agencies tired of manual audits. One-command install into Claude Code. Stars: ~3.2k.

4. Toprank (github.com/nowork-studio/toprank)

Claude Code plugin that connects directly to Google Search Console + Google Ads for automated SEO/SEM optimization. It audits accounts, pauses wasteful keywords, rewrites meta tags, adds schema, detects broken links, and even does weekly performance reviews with Gemini cross-checks.

Best for: Anyone running Google Ads or SEO who wants AI to actually fix things. Quick marketplace install. Stars: ~1.1k.

5. Fincept Terminal (github.com/Fincept-Corporation/FinceptTerminal)

A native desktop Bloomberg-style terminal on steroids: C++ speed with 100+ data connectors, 37 AI agents (Buffett-style, quant, geopolitics), real-time trading (16 brokers), QuantLib analytics, portfolio optimization, and a visual node editor for workflows.

Best for: Serious investors and quants who want everything in one beautiful app. Pre-built binaries for Windows/Mac/Linux. Stars: ~15.3k.

6. Agentic Inbox (github.com/cloudflare/agentic-inbox)

Self-hosted email client that runs entirely on Cloudflare Workers + an AI agent that reads your inbox, searches threads, drafts replies, and auto-generates responses (you approve before sending). Fully isolated mailboxes with SQLite + R2 storage.

Best for: Automating email overload and customer support. Deploy to Cloudflare in minutes. Stars: ~1.5k.

7. ClawRouter (Context Mode) (github.com/mksglu/context-mode)

Context window optimizer for AI coding agents. Sandboxes tool outputs, compresses huge responses by up to 98%, tracks session history, and keeps long conversations coherent across 14 platforms (Claude Code, Cursor, VS Code Copilot, etc.).

Best for: Developers building or using long-running AI agents without blowing their context budget. Global npm install. Stars: ~10.3k.

8. Camofox Browser (github.com/jo-inc/camofox-browser)

Stealth headless browser purpose-built for AI agents. Bypasses Cloudflare, bot detection, and anti-scraping with C++-level fingerprint spoofing. Drop-in Puppeteer/Playwright replacement with stable element refs, sessions, proxies, and OpenAPI.

Best for: Agents that need to scrape or interact with the real web undetected. Docker or npm start. Stars: ~3.2k.

9. Open Higgsfield AI (github.com/Anil-matcha/Open-Generative-AI)

Uncensored, self-hosted alternative to Higgsfield/Freepik/OpenArt. 200+ models for text-to-image/video, image-to-video, lip-sync, and cinematic workflows. Node-based Workflow Studio, local inference options, and agent-friendly API. No content filters.

Best for: Creating unlimited marketing videos, ads, or social content automatically. Desktop app or npm run dev. Stars: ~8.6k.

10. Hyperframes (github.com/heygen-com/hyperframes)

HTML → video rendering engine built for AI agents. Write (or have Claude/Cursor generate) HTML + GSAP animations, preview live, and render deterministic MP4s. Perfect for turning prompts, PDFs, or CSVs into polished videos with zero traditional video editing.

Best for: Automated content creation pipelines. npx skills add or quick init command. Stars: ~11.1k.

These repos show how far agentic AI has come - from trading bots that run 24/7 to tools that audit your ads or generate videos while you sleep. None are "set and forget" money printers out of the box (you still need strategy, monitoring, and risk management), but they’re incredible foundations for building real automated systems.

Hi r/SideProject! 👋

I wanted to share a weekend experiment that evolved into a fully autonomous trading organism.

The Concept: I call it the "Three-Agent Stack". I designed this framework to replicate the "Perception → Strategy → Execution" loops used by institutional quants, but I implemented it using purely consumer-grade tools.

Instead of one monolithic AI, I split the system into three distinct agents to reduce hallucinations:

Agent 1 (Perception): A Python script using OpenCV. It doesn't rely solely on APIs; it literally "looks" at screens to capture Liquidity Heatmaps and Orderbooks, converting visual patterns into data.

Agent 2 (The Brain): The Strategy Layer. I'm using a mix of Gemini and the new Grok.

Why Grok? As you can see in the chart (Image #3), the "Mystery Model" is currently crushing the Alpha Arena leaderboard, so I use it for the heavy reasoning logic.

Feature: It uses a Self-Prompting Loop. The AI analyzes the market and generates its own specific prompts for the next cycle (e.g., "Ask Qwen about math EV" or "Check sentiment on X").

Agent 3 (The Hands): The Executor. It parses the strategy and executes clicks on the exchange UI (or API), handling logic like "Cancel All" or "Update TP/SL".

The "Dirty Hack" (IPC): I didn't want to mess with sockets or local servers. The agents communicate via the System Clipboard. Agent 2 thinks and copies a JSON command like [AGENT3_START] {action: "OPEN_LONG"...}, and Agent 3 executes it instantly. It’s stupidly simple but incredibly fast and robust.

Status: It successfully ran a autonomous 12h loop. The UI in the screenshots is in Polish 🇵🇱 (as it's my personal tool), but the logic is universal.

Let me know what you think about this architecture! 🤖📈

Captions:

Screen Shot 1. (Agent1.png):

Caption: "The Orchestrator (Agent 1)" – Gathers data from 4 different AI models and visual sensors in real-time.

Screen Shot 2. (Grok_about_the_idea...):

Caption: "The Architecture" – Grok analyzed my code and mapped out the logic flow of the stack.

Screen Shot 3. (Alpha Arena):

Caption: "Why I use Grok" – The 'Mystery Model' (Grok 3) is currently #1 in reasoning performance, which is why it powers Agent 2.

Screen Shot 4. (Three-Agent_Stack.jpg):

Caption: "Full Stack View" – The entire ecosystem running on my desktop: Binance, Console, and Agent 3 Executor.

Full origin story and code screenshots (Polish, but Google Translate works great):

https://wykop.pl/wpis/84145067/it-s-a-live-zamiast-bawic-sie-w-filozofa-pończy

https://wykop.pl/wpis/83964233/eksperyment-czy-ai-gemini-3-widzi-wiecej-8-dni-z-a

https://wykop.pl/wpis/83939349/tak-prawdopodobnie-dzialaja-systemy-hft-ktore-ogry