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APC‑2 is a professional‑grade record‑cutting device engineered to produce original playback discs with superior sound quality in real time. The machine is offered exclusively through collaborative partners and analog‑media specialists SUPERSENSE, reflecting a shared goal of making physical record production accessible to anyone who wishes to release music or sound on vinyl. Production has been limited to a small number of units, and availability is controlled via direct inquiry. Interested parties are instructed to contact the provider via email to learn how to obtain an APC‑2 unit.

Troy Hunt marks the 1,000th breach added to Have I Been Pwned and uses the milestone to highlight a growing “disclosure lag” – the delay between a breach’s discovery and notifying affected individuals. He cites recent incidents (Carnival, ZenBusiness, DentaQuest, Charter) where companies waited 40‑45 days or longer after learning of data exfiltration before issuing public notices, despite the data being widely posted on dark‑web sites and forums. Hunt attributes the lag partly to litigation‑driven postures: firms prioritize limiting shareholder exposure and class‑action risk over customer protection, often invoking legal language (“as legally required”) to defer notification. He notes that privacy regimes such as GDPR, CCPA, and Australia’s Notifiable Data Breaches scheme contain carve‑outs that permit delayed or absent disclosure unless the breach involves “high‑risk” or “sensitive” personal data—categories that many breaches (e.g., ShinyHunters‑derived leaks) do not meet. Consequently, victims frequently remain unaware of exposure, underscoring the persistent need for services like HIBP despite evolving regulations.

A perceptron is the simplest artificial neuron: it takes a single numeric input x, multiplies it by a weight w, adds a bias b, and outputs True if w·x + b > 0, otherwise False. Learning occurs by iterating over training examples (epochs) and adjusting w and b when the prediction differs from the target: w ← w + η·error·x, b ← b + η·error, where η is the learning rate and error = target − prediction. The decision boundary is defined by w·x + b = 0, i.e., x = −b/w; bias allows the boundary to shift away from zero, which is essential when inputs are not centered (e.g., passing score ≥ 50). Normalizing inputs (scaling to a common range) reduces the magnitude of updates and stabilizes training, especially when feature scales differ. A minimal Python implementation demonstrates these concepts with random initialization, 100 epochs, η = 0.1, and optional bias and normalization toggles. Stacking perceptrons forms multilayer networks capable of more complex decision boundaries.

- Began using amphetamines at 14, sold prescription drugs, and was arrested on 17 counts, serving two years in a maximum‑security juvenile facility where he earned a GED. - After release he briefly attended community college, worked low‑wage jobs, and relapsed into drug dealing, leading to a second arrest and a felony conviction at 18‑19. - While in county jail he saw a newspaper ad for a tech internship for at‑risk youth, secured the position, and learned full‑stack development on the job, gaining experience with Ruby on Rails, MongoDB, ES6, and early React. - A subsequent relapse caused job loss; he and his wife endured periods of homelessness, low‑pay labor, and repeated job rejections due to “no felons” policies. - Eventually landed a junior role at a Miami startup, where he rewrote a legacy Rails app and discovered Hasura, an automated GraphQL layer for PostgreSQL. - Became active in Hasura’s community, contributed PRs, and was hired by the company (now PromptQL) in 2020, receiving a salary double his previous pay; the employer accepted his low‑grade felony after disclosure. - He now works as a senior developer, remains sober, married, and advocates for giving opportunities to people with criminal backgrounds.

DeepSeek V4 Pro is reported to surpass GPT‑5.5 Pro in precision, according to a RuntimeWire article. The comparison highlights superior accuracy metrics for DeepSeek’s latest model, positioning it ahead of OpenAI’s GPT‑5.5 Pro in performance evaluations.

"Dopamine fracking" is defined as the intensive allocation of resources—money, data analysis, optimization, and crowd input—to extract a maximal, concentrated dopamine response from an activity, disregarding long‑term sustainability. The term, coined on Discord, parallels hydraulic fracking: short‑term yields are high, but systemic damage is severe. The concept is framed as a form of cultural commodification where entertainment, hobbies, and relationships are engineered for peak dopamine hits, leading to homogenization and loss of nuance. An illustrative case involves extracting the dominant strawberry flavor compound for mass use, which simplifies taste, eliminates texture and variability, and can displace genuine fruit cultivation. The author argues this process erodes complexity in media, music, and social interaction, creating a feedback loop of synthetic experiences that replace authentic ones. Personal mitigation strategies mentioned include unfollowing trigger‑laden feeds, uninstalling apps, and setting consumption boundaries. Awareness of dopamine fracking is presented as the initial step toward reducing its pervasive influence.

The study analyzes 3.4 million applicants (4 million applications) screened by a single hiring‑algorithm vendor across 156 U.S. employers in 11 sectors, exposing the effects of an algorithmic monoculture. Key results: - Over 60 % of Fortune 100 firms use the same vendor (e.g., HireVue), creating shared decision pipelines. - Racial disparity: 30 % of Black applicants encounter at least one position with adverse impact; Asian applicants experience the largest aggregate shortfall—≈29 000 additional recommendations would be needed to match the most‑selected group’s rate. - Systemic rejection: among candidates submitting four applications, 10 % are rejected by every employer, a rate significantly above the independent‑decision baseline (χ² = 18 481, p < 0.001). - Counterfactual simulation shows that, under realistic application behavior, 25 submissions are required for a 99.9 % chance of at least one recommendation, versus 10 submissions under independent decisions. Policy suggestions include position‑level adverse‑impact measurement, enhanced market surveillance of homogeneous outcomes, monitoring of vendor concentration, and expanded data access for independent research.

The One World Flag is intended as a universal symbol emphasizing global unity amid geographical, social, and political differences. Unlike typical flags that represent specific nations, regions, or groups, this flag conveys that humanity shares a common future on a single planet. Its design features a central blue sphere representing Earth, set against a transparent background that adapts to any surrounding context. The flag is not meant to replace existing national or regional flags but to complement them, highlighting the overarching connection among peoples. The visual concept underscores that, despite distinct identities, more commonalities bind humanity than divisions.

The repository provides Rust Embassy example applications for the Raspberry Pi Pico 2 (RP2350) board. The RP2350 features dual‑core Arm Cortex‑M33 and RISC‑V cores and exposes I²C0 (GPIO4 SDA, GPIO5 SCL), I²C1 (GPIO2 SDA, GPIO3 SCL), UART0 (GPIO0 TX, GPIO1 RX) and UART1 (GPIO8 TX, GPIO9 RX). Examples include: - **hs3003_i2c**: reads temperature (‑40 °C to +125 °C, ±0.2 °C) and humidity (0‑100 % RH, ±1.5 %) from a Renesas HS3003 via I²C0 (GPIO4/5). - **adxl345_i2c**: reads 13‑bit 3‑axis acceleration (±16 g) from an ADXL345 on I²C0. - **zermatt** / **zermatt_snow**: drives an Adafruit 2.2″ 320×240 TFT via SPI (GPIO18 SCK, GPIO19 MOSI, GPIO16 MISO, GPIO20 DC, GPIO21 RST, GPIO17 TCS), rendering static or animated graphics using an async framebuffer and DMA. - **ds18b20**: obtains 9‑12‑bit temperature (‑55 °C to +125 °C, ±0.5 °C) from a DS18B20 1‑Wire sensor using a cycle‑accurate PreciseDelay on Cortex‑M33 (GPIO16). - **dht11**: reads temperature (0‑50 °C, ±2 °C) and humidity (20‑90 % RH, ±5 %) from a DHT11 via async dht‑sensor crate; requires release mode and a pull‑up resistor. - **matter_wifi_light**: implements a Matter‑compatible Wi‑Fi light bulb using rs‑matter, BLE commissioning, and Wi‑Fi connectivity; an external LED on GPIO15 (220‑330 Ω series) mirrors the light state. - **blinky**: simple LED blink on GPIO15, sharing wiring with the Matter example. All examples are built and run with `cargo run --example `.