Front-page articles summarized hourly.
LinkedIn secured a court order with ProAPIs and Netswift to halt mass scraping of user data, ban selling or transferring scraped data, and delete data already scraped. The deal also bars access via fake accounts. ProAPIs has agreed to cease scraping LinkedIn. LinkedIn had accused the firms of running millions of fake accounts to harvest profiles, posts, reactions and other member data, despite rapid blocking of counterfeit accounts.
RLCD reframes Jev as calibrated, multiway decision-making rather than a language-model generator. It combines multiway preference modeling with probability calibration via Plackett–Luce: scalar rewards become pairwise preferences, then a full ranking yielding a calibrated decision distribution. Jev turns reward modeling into the product: the evaluator is the runtime interface; a decision head computes utilities from state/question/candidate representations; packing and tree-attention enable parallel scoring of many candidates. RLCD is not a new reward source but a contract: typed outputs (Noul, Choice, Score) with calibrated probabilities. The post proposes testable predictions (calibration, consistency, symmetry).
WaveDigger is a Next.js/TypeScript app with deck.gl that lets you locate Wi‑Fi access points by BSSID and LTE/5G towers by MCC/MNC/TAC/Cell ID. It provides a searchable map interface (deck.gl-based), input validation for multiple BSSID formats, search history, and a responsive UI. Built atop research from the apple-corelocation-experiments team and Apple's WLOC API, it uses protobuf to talk to gs-loc.apple.com, converts coordinates, and supports China region endpoints. Prereqs: Node.js 24+, npm; optional Mapbox token. License: AGPL-3.0. Hosted demo at wavedigger.networksurvey.app. Roadmap: export results and TAC-cluster view.
Matt Huggins built a tiny redirect host to turn a single printed QR into the right destination. The short URL go.pokernexus.com/app redirects via a small Hono service to the iOS App Store, Google Play, or the website, chosen by User-Agent. Bots go to the website; iPhone goes to App Store; Android to Play. The /app path uses storeFor(request) to pick destinations; others stay on the site. It uses 302 (not 301), Vary: User-Agent, and careful query handling for campaigns. The project is small, test‑covered, and the generator is browser‑based and free.
An autobiographical confession: the author built this site largely with AI, not by hand. A former scientist turned software developer describes how AI coding evolved from unreliable “AI slop” to agent-based, testable workflows, enabling him to auto-create features like a tag engine and a rich Timeline. He sought a hands-off approach, still guiding and correcting, but misses the joy of traditional software craft—the journey versus destination. He worries about attribution, the ethics of trained code, and the gig-economy cost to developers, and asks readers to forgive him.
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On Aug 31, a software author reported an imitation of his data-wrangling tool on Github that used his product name and logo without permission. A colleague scanned the Mac DMG and found malware, with the DMG’s background image altered to urge ignoring warnings. The author provided this extra info on Sept 10. By Sept 23, Github had offered only the original automated reply with no follow-up after 23 days. He considers a DMCA takedown and warns users to download software only from the vendor.
F-Droid 2.0 is a complete redesign of the Android free/open-source app store. Built in Kotlin with Jetpack Compose and Material Design, it streamlines discovery and management around Discover, Search, and My Apps, with expanded categories and a redesigned Discover that highlights new, updated, and popular apps. Search now covers descriptions and categories and supports CJK languages; filters are powerful. The install flow uses a unified, pre-approval installer and updates run automatically by default. Privacy/safety features stay, some changes to Tor and masking; app-wiping was removed. Community-driven, with audits and funding.
Japanese used-bookstores are surging, with sales up fivefold in some cases, driven by bulk online orders. Alarmingly, buyers are sending huge consignments— including a reported 50-ton shipment— to the US for “destructive scanning” and shredding to train AI, with several shipments funneled to a logistics center in Okayama Prefecture. Critics warn this could erase culturally important works and raise copyright concerns, as genres like philosophy and history show strong demand. While owners enjoy a boom now, the long-term impact on availability and pricing could hurt independent shops.
An enthusiast ponders fluently applying physical laws like Coulomb’s law at home. He questions how to test F between point charges and whether it obeys an inverse-square law. He derives a general form F = K (q1 q2)^a f(r), noting symmetry suggests a same exponent for both charges and wonders why a equals 1. He discusses historic hints that inverse-square isn’t exact. For home testing, he considers a torsion-balance setup with two equal charges, and attempts to build a simple electroscope to observe charge, but faces charge decay and qualitative limitations. He seeks a repeatable, robust experiment.
Dymocks Tutoring and Talent 100, a Sydney chain with five centres, will close at week’s end after telling customers that AI has rendered its service obsolete and urging parents to use Gemini or ChatGPT for tutoring and study feedback.
Patrizio Raffa investigates whether grog from The Secret of Monkey Island can dissolve a metal mug as quickly as the game implies. He builds a diffusion-limited corrosion model with a 2 mm tin wall and a 35‑second perforation, treating grog as an aqueous sulfuric‑acid solution. The required proton flux implies a bulk proton concentration around 383 M, far above plausible acidities, so ordinary acid corrosion cannot explain the rapid dissolution under these assumptions. The study notes possible oxidants in SCUMM or enhanced mass transport could matter, and cautions that the puzzle is intentionally hard. Don’t drink that swill.
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An error at the Texas Department of Public Safety left an unknown number of online voter registration applications unprocessed, delaying transmission to county registrars. Counties will receive tens of thousands of older DPS records before the Oct. 5 registration deadline, potentially aggravating backlogs. Voters whose registrations were affected can still have ballots counted if they registered by the deadline, via provisional ballots if necessary. The DPS issue involves older transactions in the TEAM system and affects counties using independent registration software differently; the state is sending records in batches and coordinating with vendors to avoid overwriting newer data.
Proposes a non-destructive method to suppress refusal behavior in open-weight LLMs by dynamically steering at multiple layers (12,14,16,18,20) via Engram-based memory with forward hooks. Replaces permanent weight abliteration with dynamic residual injection, using a Dynamic Sigmoid Context Gate, constant-time N-gram triggers, and learned per-layer projections. Describes implementation (MultiLayerEngramModule), hooking scheme, and training on PKU-SafeRLHF to calibrate steering heads while freezing the base model. Demonstrates that multi-layer, conditionally activated steering yields non-refusal outputs without altering base weights, at the cost of added complexity and data requirements.
Bloomberg shows a not-a-robot verification message, asks users to enable JavaScript and cookies and complete the check, points to Terms and Cookie Policy, provides a support reference ID, and ends with a subscription pitch.
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AIIndex presents a value frontier dashboard that ranks LLMs by blended cost per 1M tokens (3:1 input/output) against an intelligence score. Frontier models are non-dominated: nothing cheaper matches or beats them; other models lie below or to the right. The page can show only the best variant per model, or all variants if desired, and you can adjust a minimum score to exclude weak models. A budget lookup helps you pick the highest-scoring model you can afford and the runner-up. Data updates daily; code on GitHub.
Brad Montague reflects on Warren Zevon’s late-show line about “enjoying every sandwich,” recalling Zevon’s loss and the value of each minute. He links the idea to a current layover and a soggy, overpriced sandwich, noting that not every sandwich will be great. The point, he suggests, is to notice you’re eating one and to savor the moment with loved ones, even amid uncertainty and disappointment. Life is strange; choose presence over perfection.
Gen Alpha’s growing insult “That’s AI!” isn’t really about artificial intelligence; it’s a broad label for things that seem superficially convincing yet cheap or fake. The slang equates AI with BS, applying to knock‑offs, overblown claims, or excuses, and is almost always used as an insult. While some see AI as potentially beneficial with guardrails, Gen Alpha has already decided AI is “so AI.” The column riffs on Trump’s “super intelligence” rename and ubiquitous AI references in everyday life.
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