Article URL: https://tomverbeure.github.io/2026/08/23/Tektronix-TDS7104-Refurbishing.html Comments URL: https://news.ycombinator.com/item?id=49527232 Points: 4 # Comments: 0
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Apache Iceberg's Puffin is a lightweight container file format designed to store statistics that do not fit inside Iceberg's standard manifest files. Manifests efficiently hold per-file scalar metrics like row counts and value bounds, but cannot accommodate larger structures such as distinct-value sketches or row-level delete bitmaps. Without distinct-value estimates, query optimizers are forced to guess join strategies, potentially causing unnecessary shuffling of terabytes of data. Puffin addresses this by housing two blob types: Theta sketches for estimating distinct column values, and deletion vectors for tracking row-level deletes introduced in Iceberg format version 3. The format uses a simple structure — a magic number, opaque data blobs, and a JSON footer — allowing query engines to access costly statistics without bloating the manifests read on every query.
Article URL: https://9to5mac.com/2026/08/31/apple-openai-forensic-macbook-evidence/ Comments URL: https://news.ycombinator.com/item?id=49527573 Points: 30 # Comments: 9
OpenAI has introduced Prism, a free cloud-based writing workspace built on its GPT-5.2 model, designed to help scientists draft, revise, and prepare research papers for publication. The platform combines a LaTeX editor with document-aware AI assistance, keeping equations, citations, figures, and references within a single working environment. Prism evolved from Crixet, a cloud-based LaTeX platform that OpenAI previously acquired and redeveloped. The workspace is currently available at no cost to personal ChatGPT account holders across Free, Go, Plus, and Pro tiers, with no stated seat limits for personal use. OpenAI has indicated that paid features and expanded access for Business, Enterprise, and Education users are planned, though no pricing details or firm rollout dates have been announced.
A software engineering advocate argues that measuring pull request metrics—such as duration and throughput—is counterproductive because PRs inherently slow down code integration and delivery. Citing a DX study of over 500 organizations, the author notes that the longer PRs remain open, the less code developers ship, particularly in high-performing teams. Rather than optimizing a practice that introduces delays, the author urges engineering leaders to adopt DORA metrics—deployment frequency, lead time for changes, change failure rate, and time to restore service—as more meaningful indicators of delivery performance. The piece contends that continuous integration with trunk-based development is a superior alternative, supported by over a decade of DORA research showing that speed and stability improve together. Using a marathon analogy, the author warns that measuring and rewarding PR efficiency is akin to timing tea service during a race—optimizing a stop that should not exist in the first place.
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