I build systems, then try to break my own claims about them.
Third-year Computer Engineering student in Mumbai. I work on information retrieval, long-term memory for language models, and evaluation — how you measure whether a system actually does what you claim, which turns out to be the harder half.
Most of what I build starts as a question I couldn't find a satisfying answer to. Lately those have been: what does a model actually need to remember, and how would you know if it remembered the right thing? Before that: can a constraint solver produce a timetable nobody has to fix by hand?
I write the criticism of my own work before someone else does. My largest project ships with a fidelity audit listing which of its components were genuinely defective, which the benchmark never exercised, and which actually carried the result — because ablation studies routinely conflate those three, and a component that never ran isn't a component that failed.
I'd rather state a result precisely than round it up. When my system matched a baseline instead of beating it, that's what I reported — along with the 32% context reduction that made the match interesting.
I document more than most people think is reasonable. Architecture notes, threat models, cost models. It's how I find out whether I actually understand something.
| Project | What it is |
|---|---|
| ice | A local-first memory layer between any OpenAI-compatible client and a locally served model. Six retrieval legs fused with weighted Reciprocal Rank Fusion, a knowledge graph where superseded facts stay queryable as history, and a classifier gating whether retrieval fires at all. Evaluated with LSREP, a longitudinal protocol I designed for it. Paper submitted to ACM TIST · Apache-2.0 |
| timetable-generator | Constraint-driven scheduling. One engine covering seven timetable types, OR-Tools CP-SAT alongside a greedy solver, behind a hard-constraint registry that fails closed. |
| prompt-routing-classifier | Multi-label topic and intent classification for routing prompts to specialised models. Built the pipeline end to end, from dataset construction to CPU inference. |
| DS-Practice | Algorithms, data structures, OS and networking from coursework — written from the algorithm rather than adapted from a library. |
| micrograd-from-scratch | Scalar autodiff and manual backpropagation, no ML libraries. |
Off GitHub: I've taken a civic-reporting platform through two national and state competitions with a six-person team, where I wrote the security threat model and the infrastructure cost model, and led the pitch.
Python · Java · PyTorch · FastAPI · PostgreSQL and pgvector · OR-Tools · Docker. I run Arch on a machine I partitioned the hard way.
Open to research and startup internships in retrieval, memory, or applied ML systems. If you're working on any of that, I'd like to hear from you.