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2026-07-14 · 2 min read

The AI Stack I Use in 2026

AI StackTools

Principles First

Before listing tools, the principles that guide selection:

  1. Sustainability — Will this tool exist in 2 years? Is it backed by a real company or community?
  2. Autonomy — Can I self-host, export, or migrate? Am I locked in?
  3. Cost — Does the pricing model make sense at scale?
  4. Reliability — Has it survived production use, or is it a demo?

The Stack

LLMs

  • Primary reasoning: Claude (Anthropic) — best for complex analysis and code generation
  • Fast tasks: GPT-4o-mini for cheap, fast summarization and classification
  • Open-source fallback: Llama variants via cloud inference for cost-sensitive workloads

Orchestration

  • Agent framework: Custom — I've tried most of the popular ones and found that a lightweight custom framework gives more control and fewer surprises
  • Workflow automation: n8n for visual pipelines, custom code for everything else

Data Layer

  • Vector storage: SQLite with vector extensions — simple, portable, no external dependency
  • Document storage: File system + Git — Markdown files are the most future-proof format
  • Structured data: PostgreSQL for anything that needs real queries

Frontend

  • Web: Next.js + Tailwind — battle-tested, great DX, excellent SEO
  • Content: Markdown + remark for blogs, structured JSON for everything else

Deployment

  • Hosting: Netlify for static, Vercel for dynamic — both have excellent free tiers
  • DNS: Cloudflare — free, fast, reliable
  • Monitoring: Uptime checks + log-based alerting

Communication

  • Delivery: Telegram for real-time, email for formal
  • Voice: TTS for briefings when hands-free is needed

What I Dropped

  • LangChain — too many abstractions, too much magic, too many breaking changes
  • Pinecone — expensive, vendor lock-in, SQLite works fine for my scale
  • Mid-tier CMS platforms — Markdown in Git is simpler and more portable

What I'm Watching

  • Local inference getting cheaper — may shift more workloads on-device
  • Agent-to-agent protocols — standardized communication between agents
  • Structured output improvements — making LLMs more reliable for data tasks

The stack isn't exciting. That's the point. It works, it's sustainable, and it doesn't change every week.