I built an AI automation that conducts real‑time video interviews — complete with an AI interviewer that listens, analyzes, and scores candidates automatically. Meet Laura, a digital recruiter powered by @bey_chat (real‑time avatars) + @n8n_io (workflow automation). She

Powerful question to reframe how we look at a problem.

A Practical Guide for Designing, Developing, and Deploying Production-Grade Agentic AI Workflows Very interesting research paper with the link attached. Interesting observations: - consortium model where multiple LLMs write script and reasoning agent serves as judge to pick out

Turn your n8n workflows into a real SaaS in minutes with the next @Lovable MCP integration with @n8n_io Just dropped a new tutorial showing how to build a full job-matching app where users: - Sign up and upload a resume - Trigger an @n8n_io workflow over MCP - Get

Full video tutorial: https://t.co/oTSONSe08i

Great collaboration @augmentedcamel @DHStx_Tech ! Excited about the future combining AI and Smart Glasses 🚀

2. Built with: - n8n automation - Model Context Protocol (MCP) - Financial Modeling Prep API - Multiple LLMs (GPT-4, GPT-4o mini) - Self-hosted on Hostinger VPS Full tutorial on YouTube 👇 https://t.co/t8cL8XdRgz *Not investment advice - educational only

1. The system uses 5 AI agents, each modeling a different investment legend: 🔸 Warren Buffett - Value & moat analysis 🔸 Peter Lynch - Growth opportunities 🔸 Bill Ackman - Activist catalysts 🔸 Charlie Munger - Psychology & quality 🔸 Aswath Damodaran - Valuation metrics

I built AI agents that analyze stocks like Warren Buffett, Peter Lynch, and 3 other legendary investors. One click → 15 minutes → Wall Street-quality analysis report. Here's what happened when I tested it on Google (GOOGL): 🧵

Watch full video: https://t.co/yJNMtb5kCX

🎯 Most businesses are flying blind when it comes to competition. While you're building, competitors are changing pricing and launching features that could impact your market position. I built 2 AI agents that monitor competitors 24/7: ✅ Catch pricing changes before customers

🧵8/8 I documented the entire build process in a step-by-step tutorial. Every tool, every configuration, every automation - because I believe every business deserves access to enterprise-level intelligence. Watch the full tutorial: https://t.co/k0pN5ooeV0 RT if this helped!

🧵7/8 This isn't just about saving money. It's about democratizing business intelligence. Small e-commerce brands can now access the same competitive insights as Amazon itself. Solo entrepreneurs can monitor markets like Fortune 500 research teams. The playing field is

🧵6/8 What this system actually does: ✅ Monitors competitor prices 24/7 ✅ Detects seasonal pricing patterns ✅ Sends instant Slack/email alerts ✅ "Show me products with biggest price drops this week" ✅ Generates charts and insights automatically ✅ Scales from 100 to

🧵5/8 The tech stack (all no-code): - n8n → Workflow automation ($10 self-hosted) - Replit → Dashboard + database ($20/month) -Decodo API → Web scraping ($30/month) - Claude API -> LLM ($10/month)

🧵4/8 The solution: AI agents + no-code automation. I built a complete competitor analysis system that: → Tracks 1,000+ products automatically → Uses AI to spot pricing patterns → Sends real-time alerts when competitors move → Answers business questions in plain

🧵3/8 The problem: Small businesses can't afford proper competitive intelligence. Enterprise platforms can cost $2000/month just for basic competitor monitoring. Meanwhile, Amazon sellers are making pricing decisions blind, losing thousands in revenue daily. There had to be a

2/8 Tutorial: https://t.co/k0pN5ooeV0

I just built an AI system that monitors 1,000+ Amazon products and sends me alerts when competitors change prices. Cost: $70/month Time to build: One evening Coding required: Zero Enterprise tools charge $2000+/month for this. Here's how I did it with step-by-step

Bullish energy sector long term. 'In the end, intelligence will scale as far as the grid allows.'