Charles Green

Most AI agent frameworks demo well and scale badly. Seconds-long cold starts. Gigabyte containers. Vendor lock-in baked in. Got tired of it. Built the one I wanted. aixgo.dev — open-source Go framework for production AI agents. → Sub-20MB binaries → Sub-100ms cold starts → Multi-provider, no lock-in → Ships clean to Cloud Run, Lambda, edge Foundation for a few things I'm shipping next. More soon.

Python wins research. Go wins production. That split is solidifying fast — Benthos, GoMLX, Argo, Kubeflow, every serious inference layer I see in 2026 is Go. But nobody’s built a real production-grade agent framework for it. The Python frameworks don’t port cleanly — concurrency, single-binary deploy, predictable latency are exactly where Python struggles and Go shines. That’s why I’m building aixgo.dev. Open source. Multi-provider. Built for the layer that actually serves users, not the layer that trains models. The agent serving problem is a Go problem. Most teams haven’t realized it yet.

Quiet on here for a few months. Tried being an employee again. Great company. Good people. Wrong shape of work for me. I'm at my best seeing an org end-to-end, finding the leverage point, building the system that fixes it. Then moving on. Going independent again next month. AI agents + aixgo.dev. Follow along.

19 years in Tokyo across fintech, banking, and Big 4. The last few of those building production AI agents. Both as a CISO making sure they don't blow things up, and as an engineer shipping them. I'm taking on 2 advisory engagements from May to put it to work. Who this is for: * Teams deploying AI agents in production * English or Japanese-operating, anywhere * AI ambitions wanting support to deploy at scale Happy to talk through what you're building. If that's you, send me a DM. Systems, not guesswork.

@TalAdam Nice to see Go staying strong at no. 3.

Python and TypeScript are the only languages that stayed consistently at the top. Everything else moved around. Ruby, PHP, and Java declined. Rust worked its way in.

I went through every Hacker News "Who is Hiring?" thread since 2019. 83 months. 109,462 listings. A few things stood out 🧵

Switch from OpenAI to Anthropic to Gemini. No code changes. Just update the model name. Aixgo.dev auto-detects the provider.

Start on Cloud Run. Scale to Kubernetes. Same binary. Same code. Aixgo.dev doesn't force architecture decisions before you have revenue.

Python AI frameworks fight the GIL to run agents in parallel. Go was literally built for this. Goroutines > async/await gymnastics. aixgo.dev

Your API gateway boots in milliseconds. Your database proxy boots in milliseconds. Why do your AI agents need 30 seconds? They don't.

One binary. Zero dependencies. 8+ LLM providers. go get github.com/aixgo-dev/aixgo That's the whole install.

Python: "I'll catch that error in testing" Go: "I caught that error at compile time" Your AI agents deserve the same.

Your AI agent doesn't need a 1GB container. Aixgo.dev ships as an 8MB binary. Same agents. 125x lighter.

Aixgo is MIT licensed. Fully open source. Get started in 60 seconds: go get github.com/aixgo-dev/aixgo 📦 GitHub: github.com/aixgo-dev/aixgo 📖 Docs: aixgo.dev/guides/quick-s… #golang #AI #LLM #OpenSource

13 orchestration patterns out of the box: → Supervisor → Parallel (3-4x speedup) → RAG → Reflection → MapReduce → Router (25-50% cost savings) → And 7 more... Production patterns. Not toy examples.

Introducing Aixgo v0.3.3 Beta The AI Agent Framework That Ships in 8MB. Build, deploy, and scale AI agents in Go. No containers. No cold starts. No Python. Your AI infra just got 125x lighter. Thread on why this matters for production AI...