Alan Hoa👨‍💻

Life on the Moon looking pretty appealing right now.

Before learning React, make sure you understand JavaScript well enough to explain: • map() and filter() • Destructuring • Spread syntax • Modules • Arrow functions • Closures • Promises • Async/await • DOM events • Object references React becomes much easier when JavaScript isn't the confusing part. A framework should build on your fundamentals, not replace them.

The vibe coding stack in 2026: Claude: $20/mo Supabase: $0 Vercel: $0 GitHub: $0 Cloudflare: $0 Domain: $12/yr Stripe: 2.9%/transaction You don’t need a CS degree. You don’t need funding. You don’t need a team. Ship it.

If you have to choose one. Claude code or cursor?

If you can write Python but still struggle to build projects, practice these 7 things: 1. Break large problems into small steps 2. Read error messages carefully 3. Write reusable functions 4. Work with files and JSON 5. Call external APIs 6. Use a debugger 7. Write basic tests Then build something without following a tutorial line by line. That's when you discover what you really understand.

How many times do you post in a day?

Two developers start their careers on the same day. Developer A: - Learns only what's needed for assigned tasks. - Never updates their portfolio. - Avoids difficult projects. - Waits for someone to offer a promotion. - Stays comfortable with the same technologies. Developer B: - Builds projects outside their comfort zone. - Learns system design and debugging. - Documents their achievements. - Contributes to open source. - Negotiates based on measurable impact. - Explores better opportunities. Five years later, their career opportunities could look very different. Experience isn't just the number of years you've worked. It's what you've learned, built, solved, and improved during those years. Don't just collect years of experience. Make those years count.

Reddit is full of sadists LinkedIn is full of manipulated resumes Instagram is full of fake performative influencers X is full of .....

Want to build your first AI application? You only need a small starting stack: • Python — Application logic • FastAPI — Backend endpoints • An LLM API — AI responses • Streamlit — Simple interface • SQLite — Store data • GitHub — Version control Start with an AI study assistant. Make it explain concepts, generate quizzes and save progress. Then add testing, authentication and deployment. One complete project beats ten unfinished AI demos.

As a Developer, do you like CSS?

Want to become an AI Engineer in 2027 and actually get hired? Start building things companies are willing to pay for. Here's the roadmap I'd follow from ZERO to JOB-READY. 👇 STAGE 1: Become a Strong Python Developer Learn: - Python fundamentals - Object-Oriented Programming - APIs and HTTP requests - Git & GitHub - SQL and databases - Virtual environments and testing 🎯 Build: A Python application that connects to an external API, processes data, and stores results in PostgreSQL. STAGE 2: Understand How AI Works Learn: - Machine Learning fundamentals - Supervised vs. unsupervised learning - Neural networks - Transformers and embeddings - Tokenization and context windows - Model evaluation 🎯 Build: A text classification application using Hugging Face models. STAGE 3: Learn to Build with LLMs Learn: - OpenAI and Anthropic APIs - Prompt engineering - Structured outputs - Function calling - Streaming responses - Token usage and cost optimization 🎯 Build: An AI assistant that processes documents, extracts information, and returns structured JSON. STAGE 4: Master RAG Systems Learn: - Retrieval-Augmented Generation - Vector databases - Embeddings and semantic search - Chunking strategies - Hybrid search and reranking - Retrieval evaluation 🎯 Build: A chatbot that answers questions using company documentation and provides verifiable source citations. STAGE 5: Build AI Agents Learn: - Tool calling - Agent workflows - LangGraph - Model Context Protocol (MCP) - State and memory management - Human approval workflows - Agent security 🎯 Build: An AI agent that reads support tickets, categorizes them, searches documentation, and drafts responses for human approval. STAGE 6: Learn Production AI Engineering This is where you start standing out. Learn: - FastAPI - Docker - Cloud deployment - CI/CD pipelines - Redis and background jobs - Authentication and authorization - Logging and monitoring STAGE 7: AI Evaluation & Reliability Learn: - LLM evaluations - Hallucination detection - Prompt injection defenses - Latency optimization - Cost tracking - Regression testing - Observability 🎯 Build: Dashboard that tracks response quality, latency, failures, and cost across different models. STAGE 8: Create a Portfolio That Proves Your Skills Build 3 projects: 1. AI Customer Support Agent: RAG, tool calling, ticket management, human approval. 2. AI Document Platform: PDF processing, structured extraction, search, evaluations. 3. AI Assistant GitHub integration, codebase search, issue analysis, automated test suggestions. Every project should include: - Public GitHub repository - Live demo - Architecture diagram - Clear README - Automated tests - Deployment instructions - Performance and cost measurements STAGE 9: Prepare for AI Engineering Interviews Practice explaining: - RAG vs. fine-tuning - How embeddings work - How to reduce hallucinations - How to secure AI agents - How to evaluate LLM outputs - How to reduce inference costs - How to scale an AI application - How to debug a failing AI workflow STAGE 10: Start Applying Search for roles such as: - Junior AI Engineer - LLM Application Engineer - AI Software Engineer - Applied AI Engineer - Python Developer (AI Products) - AI Automation Engineer For every application: → Match your portfolio to the job description. → Show working projects, not just certificates. → Explain the technical decisions you made. → Demonstrate how you tested and deployed your work. → Reach out to engineers and hiring managers with relevant projects. Your GitHub should show what you can build. Your resume should explain the problems you solved. Your interviews should prove you understand both. Which stage are you currently working on?

Are you learning to solve real-world problems or are you just learning what is trending on X?

Developers should never stop learning how to code.

Want to become a backend developer? Learn in this order: 1. One backend language 2. Git + GitHub 3. HTTP + REST APIs 4. SQL 5. Databases 6. Authentication 7. Backend frameworks 8. Error handling 9. Testing 10. Docker 11. Caching 12. Cloud + deployment 13. Build real APIs and products What backend project would you build first?

As a developer, what’s something you wish you had learned much earlier?

Here are a few trends I predict will take off alongside the advancement of AI over the next 5–10 years: Homeschooling: At-home education powered by personalized AI tutors, allowing every child to learn at their own pace and skill level. One-Person Company (OPC): Solopreneurs operating alongside a team of AI Agents instead of hiring a large workforce. Vibe Coding: Anyone can build software, websites, or apps using natural language without needing deep programming skills. Agent-First Software: Building software designed for AI Agents to use rather than humans. Interfaces are no longer the primary focus; instead, APIs, structured data, and seamless Agent autonomy take center stage. Agent-to-Agent Economy: An economy where AI Agents communicate, negotiate, purchase services, and execute transactions on behalf of humans within authorized boundaries. Personal AI Agents: Individuals owning their own team of AI assistants capable of remembering context, planning ahead, and proactively executing tasks. Second Brain & Company Brain: Constructing digital brains to store personal and enterprise knowledge, serving as the foundational context for AI Agents to query and act upon. Micro-SaaS: A single individual building, running, and monetizing multiple niche software products—leveraging AI for everything from coding to customer support. AI-Native Business: Companies designed from day one with AI as their primary operational workforce, leaving humans to focus on strategy, creativity, and decision-making—rather than just "patching" AI into legacy processes. Personalized Learning: Every learner has a dedicated AI Tutor that designs custom learning pathways and continuously adapts based on real-time progress. Autonomous Workflows: Moving from automating single tasks to assigning high-level goals—allowing AI to independently plan, coordinate tools, and complete end-to-end workflows. Creator Economy 2.0: A single creator operating an entire media ecosystem, supported by an AI team handling research, production, editing, and distribution. AI-Powered Personal Software: Instead of shopping for an off-the-shelf app, individuals can build micro-apps tailored precisely to their personal everyday needs. Localism 2.0: A return to local values, neighborhood communities, and real-life experiences as most digital tasks become handled by AI. There is a thought-provoking paradox here: As AI advances, humans gain greater economic independence, yet may crave genuine social connection more than ever. Because of this, I believe the two biggest macro trends of the coming decade will be: 1. The rise of AI-augmented, hyper-capable individuals. 2. The resurgence of tight-knit local communities, real-world experiences, and human-to-human relationships. These are trend predictions, meaning not all of them will become mainstream or completely replace traditional models. The common thread across all these shifts is moving from "humans directly using tools" to "humans setting goals, and AI using tools to execute them." Freeing up our time for real-world connections! Have a great start to the week!

20 years ago, none of these existed: 𝕏 iPhone Facebook YouTube Instagram TikTok Snapchat Reddit Gmail WhatsApp Bitcoin Uber Airbnb Instacart DoorDash Netflix streaming Amazon Prime Google Map Tesla Roadster iPad Fitbit Chrome Zoom FaceTime SpaceX Falcon 1 Which of these do you think will still be around in 20 years?

Can you realistically learn HTML, CSS, JavaScript, Python, and Java at the same time, or are you setting yourself up for confusion?

Don't rush to become an expert in everything. Become really good at understanding and solving problems.