LLM & Agents

Reads a new GitHub issue and tells you which component it belongs to, which already-solved issues look like it, and roughly how long it will take to close.

An AI shopping assistant for Indian weddings that searches eight store catalogues at once and will not invent a price or a size to make the sale.

Answers whether rewriting an AI agent's tool descriptions actually made it better at its job, with an A/B harness for MCP servers. The research behind my tool-description paper.

Turns a pile of customer reviews in English and Hinglish into what people actually keep saying.

Describe a trip in a sentence and get two genuinely different itineraries, put together by a team of agents, instead of a page of near-identical flights.

Scores a Claude Code session on how efficient it was, and shows you where the tokens went to waste.

Fails your build when an agent starts costing more per run, and tells you, every run, the smallest rise it could actually have caught.

Type what you spent the way you would say it out loud. It files the expense, categorizes it, and predicts where your spending is heading.

A held-out benchmark for a class of silent-verdict-degradation bugs, plus 3 detection baselines (AST, local LLM consensus, frontier judge) that all failed a pre-registered viability gate.