AI subscriptions add up faster than you notice. I'm paying $236/month across seven AI tools — and every company I'm giving money to says they're losing money on me. The math only works because of VC subsidies that won't last. When the bill catches up, you'll wish you'd built your own stack. This playlist tracks what AI actually costs, why it's about to cost more, and what happens when you stop renting and start owning your compute. Topics covered: • What you're really paying every month (the subscription audit) • Why "$20/mo" turns into $200/mo (the enshittification cycle) • Self-hosting the AI tools you actually use • Replacing SaaS with a homelab — what works, what doesn't • When AI is genuinely cheaper than humans, and when it isn't If you've ever stared at a credit card statement wondering when "just one more subscription" became $100+/month, this playlist is for you.
Curated by: Codacus (15 videos)
Most tutorials show you how to build ONE AI agent. But what happens when that agent hits the wall — context overflowing, quality degrading, tasks too complex? You break it apart. And the patterns you need? You already know them. In this video, I break down the architecture patterns behind production multi-agent AI systems — the same patterns software engineers have used for decades with microservices. If you've ever decomposed a monolith, you already know how to build multi-agent systems. ⏱️ TIMESTAMPS 00:00 The Monolith Problem 00:22 From One Agent to Many 01:19 The Anthropic Data 01:38 It's the Architecture, Not the Model 01:50 The Software Engineering Parallel 02:35 Decomposition Strategies 03:34 Orchestration Patterns 04:12 Pattern 2: Parallel Fan-Out 04:52 Parallel Tradeoffs 05:06 Pattern 3: Orchestrator 05:33 My AI Coworker Pipeline 06:07 Pattern 4: Handoff / Relay 06:40 Error Handling That Actually Works 08:08 Communication Protocols 08:43 The Honest Take 09:45 Framework Landscape 10:17 How to Evaluate Frameworks 10:31 Final Takeaway 📊 KEY STATS • Anthropic found multi-agent outperforms single agent by 90.2% on complex tasks • Teams using error handling patterns saw failures drop from 23% to under 2% • MCP has 97M+ monthly downloads — becoming the standard 🔗 RESOURCES • Anthropic Multi-Agent Research: https://docs.anthropic.com/en/docs/build-with-claude/agentic-systems • MCP (Model Context Protocol): https://modelcontextprotocol.io • A2A (Agent-to-Agent Protocol): https://google.github.io/A2A • Gartner AI Agent Report: https://www.gartner.com/en/topics/ai-agents 📋 PATTERNS COVERED Sequential Pipeline — assembly line, each agent refines the previous output Parallel Fan-Out — split work across agents, aggregate results Orchestrator / Director — central coordinator delegates to specialists Handoff / Routing — triage agent routes to the right specialist Error Handling — retry, fallback chains, error classification, checkpoint recovery 💡 THE RULE Start with one agent. Push it until it breaks. Decompose when you have evidence. Not before. — 🔔 Subscribe for more on AI systems engineering 💬 Drop a comment if you've built a multi-agent system — what patterns worked for you? #AIAgents #MultiAgent #SoftwareArchitecture #AgentPatterns #MCP #AI #MachineLearning #Production #Microservices