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@sh_reya
Incoming assistant professor @CSDatCMU @CMUDB. Putting LLMs in databases and BI tools. Created http://docetl.org and http://evals.info.
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Vik Paruchuri argues that scoring flaws can penalize correct OCR results and hide important differences between models.
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Shreya says readers can order it on Amazon or get a copy at a book signing.
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One post argues that human insight, good metrics and basic coding agents can help academics build ambitious systems.
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A post says it has 40-plus submissions and saturates only if a person heavily tunes agents' prompts to overfit tasks.
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A selected team says it aims to improve large-scale AI-powered data processing and deploy its work with industry partners.
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The preprint's authors cite verbs formed from nouns and inanimate subjects as examples of the style.
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A quote post calls Hamel an exceptional fit for the work and expresses excitement about learning from his experience.
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A user predicts AI coding agents could let companies build their own data systems and significantly reduce compute bills.
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A user calls something “the future of coding agents” but does not identify what they mean.
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A user says candidates work through a 200–500-line codebase, with Gemini available for use.
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Quail's team says an example tagging 1,772 long agent traces used 3x fewer tokens and ran 2.6x faster.
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Polars says the release adds initial spill-to-disk support, a Map data type and performance improvements.
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A viewer discussing a Rippling case study says its data team still manually inspects agent queries and builds rollup tables when patterns emerge.
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A post says the API supports visual inputs and is tuned to make decisions in less than a few hundred milliseconds end to end.
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A post shares a wry delayed-response apology that blames “middling generative AI” for changing the war for attention.
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A user reports that a query took 6.84 hours with hand-tuned vLLM, versus a 15-minute “speed-of-light” estimate.
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A post describes AI-powered operators in SQL as “having their day in the sun.”
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A user argues that mathematics’ biggest impact will come from proofs for software, not the millennium problems, and sees automated research loops as a way to tackle verification at a billion-line scale.
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One post links their popularity to users wanting to avoid the work of training their own classifiers.
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One post argues that specialized inference engines can reuse vLLM or SGLang components, and suggests applications such as data processing may need specialized KV cache management.
