Top Tweets for #GettingSmarter

Everyone is learning how to write better prompts. That’s becoming table stakes. The real advantage in 2026 is understanding what happens beyond the prompt. If you want to build production-grade AI systems, learn these 9 concepts: 1. Agentic Loops — Plan → Act → Observe → Reflect 2. MCP — Give AI a standard way to connect with tools and data 3. Subagents — Break complex tasks across specialized agents 4. AI Gateways — Route, control, secure, and optimize model calls 5. Inference Economics — Understand the real cost of running AI at scale 6. Evals — Measure whether your AI actually works 7. Guardrails — Keep agents reliable, safe, and within boundaries 8. Observability — Trace what your AI is doing and why 9. Agent Harness — Orchestrate the entire system around the model The biggest shift is happening here: Old AI: User → Prompt → Model → Answer Modern AI: Plan → Act → Use Tools → Observe → Reflect → Evaluate → Guard → Trace → Improve And there’s one concept tying everything together: The Agent Harness. The model is not the entire AI system. The harness controls: → Planning → Memory → State → Tools → Execution → Guardrails → Evals → Observability AI companies will keep releasing better models. But the competitive advantage increasingly comes from how you build around those models. The vendor owns the model. You own the loop. Master these 9 concepts and you’ll be thinking about AI like a systems engineer, not just a prompt engineer. Save this as your 2026 AI learning roadmap. 🚀 Which one are you learning first?