Someone just created a hedge fund model using AI agents. A rapidly trending open-source Python project called “Trading Agents” simulates an entire hedge fund using multiple LLM-powered agents. Instead of a single model making decisions, it mirrors how real trading firms operate—with specialized roles that analyze, debate, and approve trades collaboratively. The framework breaks down a trading firm into distinct AI agents: 1. Analyst Layer (parallel execution) - Fundamentals analyst → financials & valuation - Sentiment analyst → social/media mood - News analyst → macro + breaking events - Technical analyst → indicators (MACD, RSI, etc.) Each produces independent reports—disagreement is intentional and valuable. 2. Research Layer (debate engine) - Bull agent vs. Bear agent - They argue over multiple rounds using analyst data - Designed to surface stronger, more defensible conclusions 3. Decision Layer - Trader agent → decides timing & position size - Risk management → evaluates volatility/liquidity - Portfolio manager → final approval/rejection If approved → simulated trade executesIf rejected → system logs reasoning Unlike traditional trading systems: - Not purely rule-based - Not a black-box ML model Instead, it’s: - Transparent & auditable - Every decision is traceable: Analyst reports Debate transcripts Trade rationale Approval/rejection logic Technical Backbone - Built in Python using LangGraph (agent orchestration) - Each agent = node in a decision graph - Features: Checkpointing (resume runs) Persistent decision logs Self-reflection loop: Tracks past trades Compares performance vs. benchmarks Feeds insights back into future decisions Recent Updates (v0.2.4) - Structured outputs using schemas (more reliable decisions) - Expanded model support (OpenAI, Gemini, Claude, DeepSeek, etc.) - Docker support - Standardized rating system: Buy / Overweight / Hold / Underweight / Sell

Don't build 10 automations that each save 5 minutes. Build ONE automation that eliminates a full process. Impact > quantity. What's the ONE automation you should focus on this week?

I've been playing with Retell AI (retellai.com) lately and am quite impressed. The agents sound and act more or less human (a little scary actually) and the workflow building right inside Retell is impressive! If you're a strong prompt engineer and workflow builder, this system could replace a whole call center within a week.

I recently built an Instagram DM automation agent using n8n, and it ended up doing way more than just auto-replies. At first, the goal was simple — reply to incoming DMs and save time. But I kept improving it, and now it’s basically a small assistant working in the background. Here’s what it can do right now: – Automatically reply to DMs and guide the conversation– Scrape relevant websites to pull in useful data when needed– Connect with a CRM to store and organize leads– Analyze resource files (docs, PDFs, etc.) to answer questions more accurately So instead of just sending generic replies, it can actually use context and data to respond in a smarter way. I’ve been testing it on a real use case, and it definitely reduces manual work + helps not miss potential clients. I’ll drop a screenshot below so you can see it in action. If you’re running a business through Instagram and dealing with a lot of DMs, this kind of setup can save you a lot of time (and probably make you more money too). If you’re curious how this could work for your case, just send me a message — happy to walk you through it.

Harvard's Gen. AI course is accessible for free. No sign up needed: https://generative-ai-course.hks.harvard.edu/1-how-genai-works/class-1 Includes exercises and homework as well.