Evals & Research

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.

A word game you play once a day, where your only clue is how close each guess comes in meaning to the secret word.

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.

Forecasts retail demand a week out across all 30,490 Walmart product and store lines, so a buyer knows what to order.

Turns a prompt or a photo into a Japanese woodblock print, on a LoRA I fine-tuned to hold that style.

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.

A PWA that tracks India's 22K gold rate and ships an honest naive flat-hold range, not a forecast, because the ML model I trained never beat that baseline.

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.

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.