Show HN: Zs3 – S3 server in ~1K lines of Zig, 250KB binary, zero dependencies

Show HN: Agent MCP Studio – build multi-agent MCP systems in a browser tab

Show HN: Agent MCP Studio – build multi-agent MCP systems in a browser tab I built a browser-only studio for designing and orchestrating MCP agent systems for development and experimental purposes. The whole stack — tool authoring, multi-agent orchestration, RAG, code execution — runs from a single static HTML file via WebAssembly. No backend.<p>The bet: WASM is a hard sandbox for free. When you generate tools with an LLM (or write them by hand), the studio AST-validates the source, registers it lazily, and JIT-compiles into Pyodide on first call. SQL tools run in DuckDB-WASM in a Web Worker. The built-in RAG uses Xenova&#x2F;all-MiniLM-L6-v2 via Transformers.js for on-device embeddings. Nothing leaves the browser; close the tab and the stack is gone. The WASM boundary is what makes it safe to execute LLM-generated code locally — no Docker, no per-tenant container, no server.<p>Above the tool layer sits an agentic system with 10 orchestration strategies:<p>- Supervisor (router → 1 expert) - Mixture of Experts (parallel + synthesizer) - Sequential Pipeline - Plan &amp; Execute (planner decomposes, workers execute) - Swarm (peer handoffs) - Debate (contestants + judge) - Reflection (actor + critic loop) - Hierarchical (manager delegates via ask_&lt;persona&gt; tools) - Round-Robin (panel + moderator) - Map-Reduce (splitter → parallel → aggregator)<p>You build a team visually: drag tool chips onto persona nodes on a service graph, pick a strategy, and the topology reshapes to match. Each persona auto-registers as an MCP tool (ask_&lt;name&gt;), plus an agent_chat(query, strategy?) meta tool. A bundled Node bridge speaks stdio to Claude Desktop and WebSocket to your tab — your browser becomes an MCP server.<p>When you&#x27;re done, Export gives you a real Python MCP server: server.py, agentic.py, tools&#x2F;*.py, Dockerfile, requirements.txt, .env.example. The exported agentic.py is a faithful Python port of the same orchestration logic running in the browser, so the deployable artifact behaves identically to the prototype.<p>Also shipped: Project Packs. Export the whole project as a single .agentpack.json. Auto-detects required external services (OpenAI, GitHub, Stripe, Anthropic, Slack, Notion, Linear, etc.) by scanning tool source for os.environ.get(...) and cross-referencing against the network allowlist. Recipients get an import wizard that prompts for credentials. Manifests are reviewable, sharable, and never carry secrets.<p>Some things I&#x27;m honestly uncertain about:<p>- 10 strategies might be too many. My guess is most users only need Supervisor, Mixture of Experts, and Debate. Open to data on which ones actually pull weight. - Browser cold-starts (Pyodide warm-up on first load) are a real UX hit despite aggressive caching. - bridge.js is the only non-browser piece. A hosted variant is the obvious next step.<p>Built with Pyodide, DuckDB-WASM, Transformers.js, OpenAI Chat Completions (or a local Qwen 1.5 0.5B running in-browser via Transformers for fully offline mode). ~5K lines of HTML&#x2F;CSS&#x2F;JS in one file.<p><a href="https:&#x2F;&#x2F;www.agentmcp.studio" rel="nofollow">https:&#x2F;&#x2F;www.agentmcp.studio</a><p>Genuinely curious whether running this much LLM-generated code in a browser tab feels reasonable to you, or quietly terrifying.