@samuelcolvin Built the TS equivalent of Monty — same architecture, same sandbox philosophy, but for JS/TS agent stacks. ~2µs cold start, <2KB snapshots, runs from Rust/Node.js/Python/WASM Your work on Monty was the direct inspiration. 🙏

I've been building Zapcode — open source, Monty's architecture for TypeScript: - 2µs cold start (vs 200-500ms Docker) - Sandboxed by default — no fs, net, env, eval - Snapshot execution to <2KB, resume anywhere - Works from Rust, Node.js, Python, WASM https://t.co/KmnxBUFqIk

Three implementations of this idea exist today: → Code Mode (Cloudflare) — V8 on Workers, full TS, but locked to their infra → Monty (Pydantic) — Rust VM, Python subset, snapshots → Zapcode — Rust VM, TypeScript subset, ~2µs cold start, suspend/resume

MCP isn't dead — it stays as the tool discovery protocol. What changes is the last mile: instead of the LLM calling tools one by one, it writes a code block that calls them all. Cloudflare's Code Mode already does exactly this with MCP servers.

Cloudflare's argument: LLMs have seen millions of code examples but almost no tool-calling examples. Their analogy: "Asking an LLM to use tool calling is like putting Shakespeare through a one-month Mandarin course and then asking him to write a play in it."

With code execution: const tokyo = await getWeather("Tokyo"); const paris = await getWeather("Paris"); tokyo.temp < paris.temp ? "Tokyo" : "Paris"; One LLM call instead of three. Intermediate values stay in the code — never pass back through the LLM.

Today with classic tool calling, the LLM does: Call tool A → result back to LLM → reads it → calls tool B → result back → reads it → calls tool C Every intermediate result passes through the neural network just to be forwarded. Wastes tokens, adds latency.

Perplexity's CTO just dropped MCP internally to go back to classic APIs. Meanwhile Cloudflare, Anthropic, HuggingFace, and Pydantic are converging on a different pattern: Let the LLM write code that calls tools. Here's why this matters 👇

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