AI Summarized Hacker News

Front-page articles summarized hourly.

Show HN: A website that tracks US food prices every day

US Food Price Monitor compiles daily USDA farm-to-retailer price data. The latest report (18 Sep 2026) lists wholesale price changes: onions Idaho Falls +100% YoY; potatoes +68%; tomatoes +48%; strawberries +27%; avocados +4.1%; eggs -64%. It highlights biggest weekly movers (Longan +96%; Limes +48%; Cucumbers +40%; Beans KY Wonder -33%; Tomatoes -15%; Pineapples -16%). Retail ads show current vs. year-ago prices for staples (avocados, eggs, onions, potatoes, strawberries, Roma tomatoes). Market notes cover onion market steadiness in several regions and strawberry updates. Updated daily.

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RoboHarm: Do Frontier Robot Policies Refuse Unsafe Instructions?

RoboHarm evaluates whether frontier robot policies refuse unsafe instructions in five hazard scenes using bimanual YAM arms. The study tests Claude Fable 5.1, GPT-6 Astra, and MolmoAct2 across 300 trials (60 per instruction, 20 per policy), with human raters classifying outcomes as safety/refusal, non-safety refusal, no meaningful attempt, attempted, or completed. Safety refusals: Fable 20/100, Astra 2/100, MolmoAct2 0/100 (no built-in refusal). Completion rates vary by task; MolmoAct2 often completes despite no safety refusal. Limitations include single phrasings and lack of long-horizon harm assessment.

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Divide by Depth for Instant 3D

Depth division x/z is a simplified view of the perspective projection. Cameras in 3D are a few simple transforms: world space to view space to clip space, then perspective divide to normalized device coordinates, then to pixels. The perspective projection matrix encodes focal length f = 1/tan(FOV/2) and aspect a, with near/far planes n and F shaping depth via A = (F+n)/(F-n) and B = -2Fn/(F-n). This pipeline enables culling and correct perspective; sometimes you only need the depth-division trick, but the full pipeline provides more control.

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Transformers Explained Visually

Transformer Explainer uses a live GPT-2 (small) in the browser to show how text-generative Transformers work. It covers key ideas: tokenization and embeddings, positional encoding, and stacked Transformer blocks with multi-head self-attention and an MLP, producing vocabulary logits that are turned into next-token probabilities. It explains Q, K, V, masking, heads capturing different relations, and residuals/LN/dropout. It also shows sampling controls—temperature, top-k, top-p—for deterministic or creative outputs. Built in-browser (ONNX Runtime) with JavaScript (Svelte, D3); developed by Georgia Tech researchers.

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The Advisory Group on Mathematics and Artificial Intelligence

Announces the Advisory Group on Mathematics and Artificial Intelligence (AGMAI) at IAS and agmai.org. Its purpose: advise AI companies on interactions with mathematical research and the community; publish recommendations; act as a channel between mathematicians and the AI industry; operate independently of any company. Members: François Charles; Camillo De Lellis; Timothy Gowers; Martin Hairer; Nikhil Srivastava; Ulrike Tillmann; Ravi Vakil; Edward Witten; Melanie Matchett Wood. Formed after OpenAI approached members; current task: advise OpenAI on coordinating the release of math results from its internal model; input via a form; not public without consent.

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AI coding has made CI a bottleneck, so we reworked ours to keep up

Linear reworked CI to match faster code shipping. They cut PR wait from >6 minutes to just over 5 and halved runner time per test by four-fold changes: upgraded infra (faster third-party runners; tsgo; Oxlint), gate-work optimization (smaller fetches, sparse checkout, limited history), resilient checkout (retry with backoff and cache), and reduced setup (prebuilt images, install-only-needed deps, avoid slow caching). They also batched short checks, balanced test shards, and shared module state with strict isolation. Result: ~87,000 runner-minutes/month saved (~11.8% of CI) and setup per shard down ~44%. CI continues to scale with ~2,000 tests/week.

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Why Does an NPM Math Library Need an Encrypted Loader?

SafeDep analyzed an npm package mathmain that copies mathjs and ships with an encrypted loader. The loader stays dormant until a trigger is provided: the password is the JSON form of the LU lower factor L of the 3x3 Pascal matrix A = [[1,1,1],[1,2,3],[1,3,6]]. When correct, it decrypts and writes graph.js and two payloads, then loads them to run a remote-access implant (Slack bot, Telegram) with network endpoints. The same loader appears in mathsbase and math-universe; the GitHub sources do not contain it, so it was added at publish time. Indicators include the encrypted blobs and file hashes.

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Help 404 Media Find Out How Your Local Police Are Surveiling You

404 Media investigates how Axon ALPR cameras are used after cities swap from Flock, seeking to reveal data access and sharing practices. They have filed public-records requests nationwide for Axon ALPR activity reports (internal and network), data-sharing, and hotlist audits, and publish sample FOIA language to empower others to do the same. The goal is to document when officers query Axon’s systems and who can see or receive the data. They’ve already obtained reports from several departments and urge locals to contribute.

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