My Open Source AI Tools Stack for 2026

Every year I revisit my developer toolchain and ask: "What's actually earning its place?"

2026 is the year AI tooling matured. The hype has settled. What's left are genuinely useful tools that make building AI products faster, more reliable, and more fun. Here's my current stack — including tools I've built myself.

LLM Evaluation: Clawbench

If you're shipping anything powered by large language models, you need systematic evaluation. Not vibes — data.

Clawbench is the benchmarking tool I built for exactly this. It lets you define custom evaluation suites, compare models side by side, and track performance over time.

Why it matters: Model providers update constantly. What worked last month might not work today. Without automated benchmarking, you're flying blind.

Market Intelligence: Demand Signal

Before building a feature or launching a product, I check Demand Signal to see if there's real market demand. It aggregates signals from search trends, social mentions, and job postings into an AI-scored dashboard.

Why it matters: The best code in the world doesn't help if nobody wants what you're building.

The Foundation Stack

Beyond my own tools, here's what powers my daily work:

Languages

• TypeScript — for everything frontend and full-stack (Next.js apps, API routes)
• Python — for AI/ML pipelines, data processing, and evaluation scripts
• Rust — for performance-critical tooling (when Python is too slow)

AI/ML

• PyTorch — the backbone for custom model work
• Llama.cpp — local LLM inference for development and testing
• LangChain / LlamaIndex — for RAG pipelines (with heavy customization)

Frontend

• Next.js — my default for any web project. SSG for performance, server components for flexibility
• Tailwind CSS — design speed without compromising on quality
• Framer Motion — smooth animations that make products feel premium
• D3.js — for complex data visualizations in Demand Signal and analytics dashboards

Infrastructure

• Vercel — deploy and forget. Perfect for Next.js projects
• Railway / Fly.io — for backend services and Python APIs
• Supabase — PostgreSQL with real-time features built in

What I Look For in Developer Tools

After years of building AI products for major brands and shipping my own tools, I've developed a simple filter:

1. Does it save me time? A tool that adds 30 minutes of configuration for 10 minutes of saved work is a net negative.
2. Is the community active? Solo-maintained tools are risky dependencies.
3. Can I customize it? Black-box tools work until they don't. I need escape hatches.
4. Does it play nice with my stack? Integration friction kills adoption.

What's Next

I'm actively building and iterating on all of these tools. If you want to follow along:

• Check out Clawbench for LLM evaluation
• Try Demand Signal for market intelligence
• Track my daily progress on Building in Public
• See all my projects
• Follow me on X and LinkedIn

The best time to optimize your AI stack is before you need it. The second best time is now.