Hard Problems.

Not just what I built — but what I had to figure out first.

1. 01 Machine Learning

   Overcoming The Precision Pitfall

   The Problem

   Describe the hard problem here — what made it difficult, what constraints existed, and why existing solutions didn't cut it.

   How I Solved It

   Walk through your approach — the reasoning, the experiments, the dead ends, and the insight that ultimately cracked it.

   Outcome

   What changed as a result? Quantify it if possible — accuracy improved, time reduced, system unblocked.

   • Python
   • PyTorch
   • Data Pipeline
2. 02 Systems

   Problem Title Goes Here

   The Problem

   Describe the hard problem here — what made it difficult, what constraints existed, and why existing solutions didn't cut it.

   How I Solved It

   Walk through your approach — the reasoning, the experiments, the dead ends, and the insight that ultimately cracked it.

   Outcome

   What changed as a result? Quantify it if possible — accuracy improved, time reduced, system unblocked.

   • Distributed Systems
   • Redis
   • Go
3. 03 Algorithm Design

   Problem Title Goes Here

   The Problem

   Describe the hard problem here — what made it difficult, what constraints existed, and why existing solutions didn't cut it.

   How I Solved It

   Walk through your approach — the reasoning, the experiments, the dead ends, and the insight that ultimately cracked it.

   Outcome

   What changed as a result? Quantify it if possible — accuracy improved, time reduced, system unblocked.

   • Optimization
   • Graph Theory
   • C++