Thanks Built both solo LangGraph for orchestration FastMCP for the REPL server Code's on GitHub if you want to dig into the architecture.

Official reason: national security. But reality is complex US small yard, high fence' strategy restricts even allies (Canada, UK included in tiered chip rules). In 2026, Trump loosened China restrictions, causing congressional backlash. It's part safety, part tech dominance. Trust concern is valid.

This is a national security–driven export control decision, not retaliation against any company or a political reaction, and there’s no verified evidence supporting the revenge narrative.

In my work building AI agents — the wrong outputs aren't the obvious ones.

It's when the model confidently returns a subtly broken tool call, or hallucinates an API parameter that almost exists.

Looks right. Runs without error. Does the wrong thing.

Calibration is the real problem, not accuracy.

100% agree — local, predictable runtimes are what separate toy agents from production-ready ones. FastMCP nailed this for Python. Curious which orchestration layer you think wins long-term? 🤔

That token-by-token generation is one of those moments where you realize the architecture is doing something almost biological. Intelligence emerging from scale rather than explicit design is still mind-bending. How much headroom do you think is left in this paradigm before the next shift? 🤔

Local models are a game-changer for agent development — no latency, no API costs, full privacy. Once you pair them with LangGraph for stateful orchestration the possibilities are wild. What model did you go with? Curious how it handles multi-step reasoning 🤔

DSA is foundation but in 2026, pairing it with AI agent skills is what sets candidates apart. LangGraph + Python = unstoppable combo for any developer entering the market 💪