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AI Benchmarking

288 summarised stories about AI Benchmarking, each linking back to the original source. Browse all topics →

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Sunday, 27 September 2026

Performance engineering from kernel analysis to AI: Adrian Cockcroft’s take

The New Stack 3 weeks ago 21

Adrian Cockcroft discussed how performance engineers should move beyond relying on system metrics and percentiles by digging into kernel-level causes and then using new analysis techniques for response-time distributions. He said percentiles like P99 do not work for modern web service latency because a single number cannot show whether there are one or multiple latency peaks. As a result, he used an LLM to automate a custom R tool that identifies and tracks multiple peaks over time instead of collapsing data into averages or a single percentile value.

A Coding Guide to Google Research’s MSEB: Writing Sound Encoders to the Benchmark Contract and Scoring Them Across Classification, Clustering, Retrieval and Segmentation

MarkTechPost 3 weeks ago 12

The tutorial demonstrates how Google Research’s MSEB Massive Sound Embedding Benchmark works by implementing two different SoundEmbedding encoders and running them through the benchmark’s classification, clustering, retrieval, and segmentation evaluators. It builds the example audio and data pipeline at a 16,000 Hz sample rate, then directly inspects what each evaluator’s metric rewards. As a result, the two encoders trade places depending on the evaluator being used, illustrating that leaderboard scores reflect the evaluator “surface” a submission is tested on.

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