Building in the digital realm ⚙️
Code + technical learnings | Debugging life 🐛
Prev. Intern @clcoding@thita_ai
🔗 GitHub: https://nitter.gq/t.co/cWEJokZvrH
One thing I learned about MCP that I found genuinely interesting: its beautiful separation of concerns.
MCP's beautiful separation of concerns:
• Tools → What AI can DO.
• Resources → What AI can KNOW.
• Prompts → What USERS want.
I built a game where you look at a painting and write what you think is happening.
No title. No clues. Just your eyes, your imagination, and your interpretation.
Here’s a glimpse of what I’ve been working on.
This page breaks down your interpretation across five dimensions: visual observation, evidence-based reasoning, historical alignment, originality, and uncertainty awareness
I built a game where you look at a painting and write what you think is happening.
No title. No clues. Just your eyes, your imagination, and your interpretation.
Here’s a glimpse of what I’ve been working on.
In machine learning, knowing something isn't enough. Being able to explain it clearly is a different skill.
Cross-validation and K-Fold are a good example of how a simple visual can make technical concepts easier to communicate.