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  1. 332

    How I Wrote a Forth (Without Knowing How)

    The author details building MoonForth, a Forth interpreter written in C that leverages Lua's C API for stack manipulation and word storage. The process was surprisingly straightforward, reinforcing the idea that Forth "writes itself" by iteratively filling missing functionality.

    Lobstersvtrlx.ca9 minHN2lobste.rs9
  2. 334

    Squalk: an old-school forum engine built on Nostr (NIP-29 groups, NIP-7D threads)

    Squalk is a SvelteKit‑based forum built on the Nostr protocol, implementing NIP‑29 groups and NIP‑7D threads. It can run in a single‑forum “simple” mode or a multi‑forum “full” mode, with chat sidebars, markdown resources, and optional server‑side rendering for SEO. Configuration is done entirely via `PUBLIC_` environment variables, and deployment scripts support both static hosting and Node SSR,…

    Lobstersgithub.com5 minlobste.rs16
  3. 338

    This Code Is CRAP (2011)

    A user praises Crap4J, an old Java code quality tool, for effectively identifying "dangerous" code without false positives, unlike other coverage tools. They note its age and incompatibility with Java 7, urging for updates.

    Hacker News front pagegoogleblog.com1 minHN7657
  4. 340

    Do you use :visited on links?

    The author switched the site’s link colors to differentiate visited links using a simple CSS rule, arguing it’s useful for reading many RSS‑fed articles. The post is a brief personal opinion with a code snippet and no deeper technical analysis.

    Lobsterskevquirk.com1 minlobste.rs26
  5. 342

    OmniVBench: A Benchmark and Large-Scale Dataset for Omni Reference-to-Video Generation

    OmniVBench is a new benchmark and the Omni‑R2V Dataset, offering 7 task families, 18 fine‑grained reference‑to‑video generation tasks and a factor‑grounded evaluation checklist of over 12 k items. The dataset provides 340 k industrial‑grade video samples and pipelines for constructing reference‑target pairs, exposing large performance gaps in current R2V models.

    Hugging Face Daily Papersarxiv.org2 minpaper
  6. 343

    1% of Tokens Can Be Enough: On Gradient Estimation in On-Policy Distillation

    The paper studies gradient‑estimation noise in sparse on‑policy distillation (OPD) and introduces the Information‑Efficiency Ratio (IER), a signal‑to‑noise based metric for selecting which tokens to supervise. IER is derived from an information‑geometry analysis with an optimal scalar baseline, and a candidate‑set approximation lets it be combined with existing usefulness scores while keeping the…

    Hugging Face Daily Papersarxiv.org1 minpaper
  7. 347

    CARE: Experience-Guided Atomic Corrective Execution for Vision-Language-Action Policies

    CARE learns corrective actions from real failure rollouts of Vision‑Language‑Action policies, models stage‑conditioned deviation distributions, and synthesizes representative failure states plus corrective demos. At inference it uses stage‑wise planning plus 3‑D monitoring to trigger atomic adjustments, improving task‑success rates by ~15 pts in simulation and real‑world dual‑arm tasks. Introduce…

    Hugging Face Daily Papersarxiv.org1 minpaper
  8. 351

    Relation algebra is not relational algebra

    The post clarifies the long‑standing confusion between *relation algebra* (a mathematical logic framework equivalent to FOL³) and *relational algebra* (Codd’s query language foundation). It outlines their histories, theoretical differences, and recent CS applications such as Alloy and the Prela query language, urging the community to adopt the term “Tarski’s Algebra of Relations” (TAR) to avoid m…

    Lobstersremy.wang2 minlobste.rs24
  9. 354

    Jev is the fastest-adopted model in AI Gateway history

    Vercel reports that the Jev decision model was adopted by 13% of paid teams within its first day, outpacing prior model launches. Jev claims to be up to 194× faster and 445× cheaper than general‑purpose LLMs while returning structured, probabilistic decisions.

    Vercelvercel.com1 minHN21
  10. 356

    AI Changed How Spotify Builds. What We Learned (and Fixed) About Quality at Higher Velocity

    Spotify’s AI‑assisted development doubled change volume, exposing gaps in alerting, capacity planning, fleet‑update safety, and mobile quality signals. The team added end‑to‑end monitoring, priority‑based tiering, stronger rollback/observability, and expanded edge capacity. Data shows AI‑generated code isn’t a direct incident cause, but verification pipelines must scale with velocity.

    Spotifyatspotify.com7 minpostmortemHN52
  11. 359

    The Shadows Lurking in the Equations – Underwater Islands

    The article introduces FuzzyGraph, a web tool that visualizes equations as error heatmaps (fuzzy/non‑binary mode) instead of the classic binary “exact‑equals” plots. By showing regions of high error (black‑hole shadows) and low error (underwater islands), the author argues that fuzzy graphs reveal mathematical features invisible to conventional graphing tools.

    Hacker News front pagegods.art4 minHN7013