# Daniel Tran

> AI Platform Engineer | SaaS Engineering Partner | Platform Architect. Senior Platform Engineer with 9+ years building production SaaS, fintech, Web3, and AI systems used by real users. Specializes in scalable platforms, AI-native applications, and modern cloud infrastructure. Based in 1129 København K, Denmark.

## About Me

- [About](https://0xdanieltran.vercel.app/about.md): Bio, personal details, tech stack, and social links.
- [Full Profile](https://0xdanieltran.vercel.app/llms-full.txt): Complete portfolio, experience, projects, and all insights in one document.

## Experience & Projects

- [Experience](https://0xdanieltran.vercel.app/experience.md): Career roles across blockchain, fintech, gaming, healthcare, and SaaS engineering.
- [Projects](https://0xdanieltran.vercel.app/projects.md): 32+ projects including Skypost AI, o1 Exchange, Predictefy, DFS Chain, and DIFINES AI.

## Insights (Blog)

Engineering articles on AI platform development, Web3 infrastructure, backend architecture, and production SaaS.

- [Supabase in Production: Lessons from Scaling Real Apps](https://0xdanieltran.vercel.app/insights/6-supabase-usage): What actually works (and what breaks) when using Supabase in real-world production systems.
- [Vibe Coding in Production: How to Build Real Products Using Lovable and v0](https://0xdanieltran.vercel.app/insights/5-vibe-coding): Practical lessons from building real SaaS and MVP products using AI builders like Lovable and v0, and how prompt engineering impacts product quality.
- [Designing a Queue System for Limited Resource Pools](https://0xdanieltran.vercel.app/insights/10-queue-system-architecture): How to architect an asynchronous FIFO queue broker when upstream capacity is capped — row locking, resource pool rotation, and event-driven state transitions.
- [AI Automation in Production: What Actually Works](https://0xdanieltran.vercel.app/insights/8-ai-automation): Practical lessons from building real AI automation workflows — agents, triggers, and reliable systems beyond one-off scripts.
- [Building a RAG Chat System: Lessons from DIFINES AI](https://0xdanieltran.vercel.app/insights/9-rag-chat-system): What I learned building a production RAG chatbot with markdown knowledge, pgvector search, global search fallback, and Groq — including the pros, cons, and trade-offs.
- [What Most Developers Get Wrong About AI Products](https://0xdanieltran.vercel.app/insights/7-wrong-aiproduct): Building successful AI products requires much more than prompts and model integrations. Real AI systems depend on UX, reliability, latency optimization, and scalable architecture.
- [Building a Production-Ready Web3 Platform: Lessons from Real Blockchain Infrastructure](https://0xdanieltran.vercel.app/insights/1-web3-blockchain): Key architectural lessons learned while building scalable Web3 platforms including wallets, explorers, and DeFi infrastructure.
- [Why Next.js Became My Default Framework for Production SaaS](https://0xdanieltran.vercel.app/insights/2-nextjs-default): How Next.js enables faster product development through full-stack architecture and modern performance features.
- [Building Real AI Products vs AI Demos: What Actually Matters](https://0xdanieltran.vercel.app/insights/3-ai-engeineering): Lessons learned integrating LLMs into production systems instead of building simple AI demos.
- [Why Python Remains Essential for Modern Backend and AI Systems](https://0xdanieltran.vercel.app/insights/4-python-engineering): How Python continues to play a critical role in backend services, automation, and AI engineering.

## Connect

- [Portfolio](https://0xdanieltran.vercel.app/): Homepage with overview, projects, and contact.
- [Contact](https://0xdanieltran.vercel.app/contact): Get in touch for consulting and engineering partnerships.
- [GitHub](https://github.com/0xdanieltran): Open-source work and code repositories.
- [X](https://x.com/0xdanieltran106): Updates and engineering insights.
- [Schedule a Call](https://calendly.com/0xdanieltran/30min): Book a 30-minute intro call.
- [Source Code](https://github.com/0xdanieltran/0xdanieltran-portfolio): Portfolio source on GitHub.