About
Building a serious identity around learning, research depth, and technical consistency
This is where the path from CSE student to AI/ML/LLM/MLOps professional becomes visible, documented, and evidence-driven.
identity
Who I am
A student-builder-researcher identity, still early but serious.
I am building a technical identity rooted in evidence, curiosity, and consistency. I want the public work to show real movement, not perform expertise.
Visual identity
Tusher's Blog
Base
CSE and software systems
Direction
AI, ML, LLMs, and MLOps
Mode
Research-minded builder
Timeline diagram
Current learning roadmap
Depth first, then leverage.
I'm building from CS fundamentals toward practical AI systems. That means strengthening statistics, ML workflows, LLM application design, and the deployment discipline needed to move from notebooks to reliable products.
Computer science grounding
Programming discipline, algorithms, systems thinking, and the technical habits that support durable engineering work.
Applied ML practice
Statistics, experimentation, model evaluation, and the transition from theory-only study into repeatable implementation.
LLM engineering focus
Prompt design, retrieval patterns, orchestration, and understanding how LLM systems succeed or fail in practical use.
Production and MLOps depth
Deployment pipelines, reproducibility, observability, and the operational discipline required for dependable AI products.
principles
Values
Curiosity, depth, experimentation, and consistency.
I prefer serious iteration over surface-level speed. I want research habits, implementation discipline, and public learning to reinforce one another.