|
|
| Layer | Core Tools |
|---|---|
| Frontend | React, Next.js, TypeScript, Tailwind CSS |
| Backend | FastAPI, Django, Node.js, REST + async workers |
| Data | PostgreSQL, Redis, MongoDB, Supabase |
| Platform | Docker, Kubernetes, Terraform, Linux |
| AI | PyTorch, model integrations, inference pipelines |
[build_queue]
- Product-grade AI features with measurable user value.
- Distributed backend patterns for resilience and speed.
- Tooling workflows that reduce friction for shipping.
- Data-heavy applications that stay responsive at scale.
[engineering_dogmas]
- Keep core flows simple and brutally reliable.
- Prefer architecture that teammates can reason about quickly.
- Measure performance early, not after users complain.
- Automate the boring path so focus stays on product quality.
[2026_targets]
- Push deeper into AI-native product architecture.
- Publish more high-signal open source projects.
- Build systems that remain elegant under real traffic.
- Collaborate with teams who optimize for execution and taste.
