Arjun Ganesh

problem> GPU/AI-infra changelogs are noisy and scattered — teams miss the one release that breaks their pipeline.

approach> An agentic pipeline that turns noisy changelogs into cited, bounded, inspectable release briefings for GPU and AI-infrastructure teams — grounded and source-linked, never hand-wavy.

problem> Nobody reads a transaction CSV for fun — but everyone will watch a 60-second recap of their own year.

approach> Spotify Wrapped, but for your bank account — an agentic pipeline that turns a transaction CSV into a personalized 60-second narrated recap video.

problem> An agent killed mid-incident normally loses all context — and starts the investigation over from zero.

approach> Agentic incident-response memory that survives the agent being killed mid-incident — execution state lives in CockroachDB, not process memory, so a cold-started agent resumes from the exact interrupted step.

problem> How fast can a 100-year-old series actually run on today's silicon — classical and quantum?

approach> How fast can a GPU compute π — classically, and as a quantum computer? An end-to-end study from consumer RTX to datacenter H100 (CUDA C++ and CUDA-Q/cuQuantum), with an AI layer that writes up what the numbers actually show.