Do You Actually Know If It Works?
The scarce skill now is not writing code but knowing your own system: what you solved, how it works, and whether a new bug is even yours.
I build the layer between a language model and software that already exists.
Five-plus years on the same stubborn question: how do you take something that works in a notebook and make it survive real users? Mostly that has meant backend systems, production machine learning, and the harness layer in between. Lately a fourth thread has crept in, machine learning for biology, where the datasets are small, the labels are noisy, and being honest about your numbers matters more than beating a leaderboard.
Now
Since Aug 2026
Senior Software Engineer, Mevrik
An AI customer-experience platform used by telecom operators and several hundred smaller businesses. I work on the AI side: agents, retrieval, and the serving infrastructure underneath.
Since 2024
Founder, AlgolyzerLab
A small studio for applied AI that does not fit a product roadmap: sports science for elite cricket, clinical software for doctors, agriculture AI, and research engineering.
What I think about
01
Agent harnesses
A model is only as useful as the surface you hand it. Most teams give an LLM raw tables and generated specs instead of the intent-named operations their own code already has.
02
Machine learning for biology
Anti-inflammatory peptide prediction, and structure-aware virtual screening of natural products against understudied viral proteases. Both taught the same lesson: the dataset you choose decides the result long before the model does.
03
Recent writing
How Computers Guess by Asking the Neighbours
A gentle, playful look at k-nearest neighbours, the lazy way a computer labels something new by asking the dots closest to it.
Why a Light Weight Can Lift a Heavy One
A friendly beginner guide to why a see-saw balances, how torque works, and why a light weight far out can lift a heavy one close in.
How Four Little Numbers Can Bend a Whole Grid
Change four numbers and watch a square spin, stretch, and lean, and see how the same idea powers every layer of a neural network.
The Bendy Lines That Help Computers Learn
A gentle beginner guide to activation functions, the little curves that let neural networks learn curvy and complicated patterns.
Bounce Box and the Great Ball Swap
A friendly look at Bounce Box, the tiny game where balls bounce off walls and trade their push in a neat elastic collision.