Zephan Hwang
@zephanylh
Learning AI from scratch and sharing my journey from zero to AI mastery.
Day 60 of the AI learning journey (going through Anthropic's AI fluency course) ☑️ Learned the 3As of AI use ☑️ Deepened my understanding of the 4Ds of AI fluency ☑️ Learnt 6 effective prompting strategies
People who watch only know hype. People who actually start learn the hard truths. People who choose to push through the hard truths will win.
Don’t fear the complexity of your dream. Relish the opportunity to build it.
When you feed AI models bad data, they give bad outputs. Your mind is the same. - If you feed it brainrot and pessimism, that's what you output. - If you feed it knowledge and positivity, that's what you output. Fix the inputs, watch the outputs change.
Do you think AI should be regulated more strictly or should innovation be allowed to move faster?
Day 59 of the AI learning journey. ☑️ Learned the Goldilocks rule for thinking about AI ☑️ Studied how bias forms and why it matters ☑️ Explored adversarial attacks and harmful uses of AI
Learn a little every day. Build a little every day. Show up when everyone else slows down. Momentum is your superpower.
Here is a simple breakdown of AI roles: - SWE: builds the software around the model - ML Engineer: builds the model - Applied ML Scientist: adapts research to real problems - ML Researcher: pushes the AI frontier - Data Scientist: extracts insights from data - Data Engineer:…
If you had to start your AI journey inside one role what would you pick and why? (e.g. Software Engineer, ML Engineer, AI PM)
Day 58 of the AI learning journey. ☑️ Learned the roles inside a real AI team (ML engineer, applied ML scientist, etc) ☑️ Studied how companies actually transform into AI-driven organizations ☑️ Understood the AI transformation playbook from pilot projects to strategy
Here’s how to think like an AI team: - Split your data into training and test sets - Train on one - Measure performance on the other - Accept that accuracy is always statistical - Improve through iteration, not perfection
Take action before you feel ready. You don’t need permission. You need momentum.
A simple framework for AI projects: 1. Is AI suited to the task? 2. Does it create business value? 3. Is the project technically feasible? i.e. Find the intersection between - what AI can do - what your business needs.
Day 57 of the AI learning journey. ☑️ Learned a full framework for choosing strong AI projects ☑️ Understood technical vs business diligence ☑️ Explored how AI teams think, measure accuracy, and manage data
Here is the entire ML project workflow in one simple path: 1. Collect data 2. Train model 3. Deploy it 4. Get user data 5. Fine-tune
The world is moving fast. But if you show up every day, you move faster. Consistency always wins.
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