🔥 PROJECT 1: "TokenTax" - The Multilingual Token Cost Analyzer The Problem You're Solving: You know from your notes that non-English text uses 3x more tokens due to BPE bias, creating an "API tax" on non-English speakers. Your Solution (End-to-End):
- Build a web tool that analyzes text in 20+ languages
- Shows the "token inequality index" (English vs Tamil vs Arabic)
- Live demo: User inputs text → see token count + cost comparison across GPT-4, Claude, Llama
- The twist: Add a "BPE Fairness Score" that shows which tokenizer is most equitable
- Deploy as a Chrome extension that warns users before they paste into ChatGPT Why interviewers will love it:
- Shows you understand the economic implications of technical decisions
- Combines web dev + NLP + real-world impact
- GitHub: Include a research section citing the "multilingual tax" papers from 2025
- Bonus: Add a "glitch token detector" (from your notes about "SolidGoldMagikarp") Tech Stack: FastAPI, tiktoken, React, Docker Demo hook: "I built this after reading Karpathy's note that LLMs can't spell 'strawberry' because of tokenization"