Sachin Gupta · San Jose
15+ years shipping large-scale billing and financial systems. Currently leading platform reliability and AI-enablement at eBay.
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Reviewer for REALM 2026, the 2nd Workshop for Research on Agent Language Models at EMNLP 2026 (Budapest, Hungary), reviewing one submitted paper on language agents.
Area Chair for GroundLM 2026, the workshop on grounding and retrieval-augmented language models at EMNLP 2026 (Budapest, Hungary), overseeing five submissions and writing meta-review recommendations.
A demo answers one question once. Production answers forever, over a corpus that never stops changing.
Part seven of the RAG series, and the first on running RAG in production. A demo retrieves from a fixed pile of documents and answers one question. Production retrieves from a corpus that changes every hour, so the hard part is not the answer, it is keeping the index true to the source: continuous ingestion and incremental re-indexing for freshness, deletion that removes a document's chunks so the model cannot cite what no longer exists, a migration plan for the day you change the embedding model and every old vector becomes meaningless, and a trace on every answer so you can see why it said what it said. Grounded in the RAGOps paper. Everyday analogies, a hand-drawn diagram, and interactive visualizers.
Reviewer for the EMNLP 2026 Industry Track (Budapest, Hungary), reviewing one submitted paper on deploying natural-language-processing systems in real-world settings.
Peer reviewer for the 2nd Workshop on Efficient Reasoning at COLM 2026, reviewing three submitted papers.
Judged the 6th Global AI Hackathon, Hack-Nation's largest to date (5,500+ applications, some 2,500 builders across 12 hubs), run with the MIT Club of Northern California and the MIT Club of Germany. Scored seven projects on the jury.
Reviewer for GroundLM 2026, the workshop on grounding and retrieval-augmented language models at EMNLP 2026 (Budapest, Hungary).
Artifact evaluator for ASE 2026 (IEEE/ACM International Conference on Automated Software Engineering).
The last five parts were the recipe. This one is the kitchen at dinner rush: the two engineering tricks that get DSpark running inside a real serving system, and the speedups it delivers there.
Part six of the speculative decoding series, the finale. Parts one to five were the idea. This one is the engineering: two tricks that get DSpark running inside a live serving system. First, do not ship the whole dictionary of scores between machines while training the guesser, ship the compact summary and do the last step locally. Second, a scheduler that cannot wait, because real GPU speed rises in jagged steps and the pipeline cannot pause, so it predicts the batch size ahead of time. Plus the real production speedups. Plain words, an analogy, a diagram, and two auto-playing visualizers.
DSpark's last machine, on its own: how it decides exactly how much of the draft to check, across everyone it is serving at once, like loading a delivery van to its sweet spot.
Part five of the speculative decoding series, and the last of three that open up DSpark one machine at a time. This one is the expediter, the paper's greedy scheduler. It decides how much of the draft the big model should check, across everyone it is serving at once, and the rule is neat: pool every guessed word, sort the most likely to pass first, and keep loading the batch while the words-per-second keeps climbing, then stop at the peak. Explained with a delivery-van analogy, a diagram, and an auto-playing visualizer. Short, one idea, and it wraps the three machines.
