标签：#AR

Beijing’s endlessly delightful Robot Games featured tons of impressive stunts. But the most mind-blowing tricks challenged the humanoid’s brain, not its brawn.

China’s hacking campaign targeted NASA, the Federal Reserve, the US Senate, the Justice Department, and more, according to the DOJ.

The "everyday" GT3 delivers, despite looming regulations and an ever-inflating price tag.

More than 15 politicians from across the country have signed on to the AI Pact, vowing to regulate data centers and AI. “We’ve got to get this right,” says Senate candidate Dan Osborn of Nebraska.

Author here. Ibteda Digital Library is a private community archive in Pakistan — for ten years we digitized rare Urdu books (lithographs, dictionaries, periodicals) on a DIY camera rig, finishing every page by hand in Photoshop. When we wound down daily operations, I realized those 575,729 finished pages across 1,765 books recorded a decade of crop decisions, so I registered them back to their raw photos (SIFT + MAGSAC with conservative acceptance gates) and used the recovered geometry as supervision. The negative results are probably the most interesting part for this sub. Scaling from 378 to 572 training books didn't move unseen-book pass@80 . Neither did ResNet-50 (better training fit, flat held-out, worse after calibration), 1024px inputs, or a spatial head. Per-book error analysis showed why: the failures were near-constant offsets per volume — our operator's preferred margin inset, which simply isn't present in the pixels of a new book. Ten operator-corrected crops per book (element-wise median residual) took pass@80 from 0.71 to 0.83 on held-out volumes. Ten labels beat every scaling lever we tried. For retouching (stain/stamp removal), we kept the neural net to detection only — a U-Net proposes removal support, classical OpenCV reconstructs the paper, and everything outside the mask is byte-identical to the original. Labels used REMOVE/KEEP/IGNORE states, and any erased Urdu diacritic vetoed deployment regardless of IoU. The stricter label cut both improved mark IoU (0.56 → 0.60) and got diacritic false positives to zero. Two things I'd genuinely like input on: (1) has anyone modeled document boundaries that depend on an invisible human preference rather than visible structure — is there prior work on per-instance residual calibration like this? Our own next step is conditioning the model on the calibration examples directly (few-shot inset inference) instead of a post-hoc median. (2) Is there any constrained diffusion/inpainting setup you'd trust to guarant

The event is expected to be a notable one, as Apple is rumored to unveil its long-awaited foldable iPhone. It'll also be the first Apple event with John Ternus as CEO, who is scheduled to take the reins of the tech giant from Tim Cook on September 1.

Vibe-coding darling Lovable just raised $400 million at a $13.3 billion valuation, roughly doubling its worth in eight months. But Lovable isn’t the only Stockholm startup putting up huge numbers lately — legal AI company Legora and health tech startup Neko Health are right there with it. On this episode of TechCrunch’s Equity podcast, Dominic-Madori Davis is joined by Sophia Bendz, a partner at Cherry Ventures and longtime fixture of Europe’s startup […]

Volvo is updating three of its electric vehicles with new hazard alerts to warn drivers when there are animals or vulnerable road users ahead. The new connected safety features are based on Volvo's cars talking to one another, as opposed to crowdsourced alert systems used by popular navigation tools like Google Maps and Waze. The […]

System Design: Payment Processing System A capstone system design walkthrough — designing a payment processing system end to end — covering the core domain model, the ledger as the system's source of truth, idempotency and exactly-once-effect guarantees, integrating with external payment gateways and card networks, handling asynchronous webhooks, reconciliation, fraud and risk checks, and the specific correctness and compliance demands that make payments a uniquely unforgiving system design problem. Table of Contents Introduction Why Payment Systems Are a Different Kind of Hard The Core Domain Model The Ledger: Double-Entry Bookkeeping as the Source of Truth Idempotency: The Single Most Important Property Integrating with Payment Gateways and Card Networks The Payment State Machine Webhooks: Handling Asynchronous Gateway Callbacks The Saga: Coordinating Payment Across Multiple Services Reconciliation Fraud and Risk Checks Data Security and Compliance Consistency, Availability, and the CAP Trade-off for Money Scaling the System Observability for a Payment System Common Pitfalls Quick Reference Table Conclusion Introduction A payment processing system takes the general system design vocabulary covered in this series' System Design guide — databases, caching, queues, load balancing — and applies it to a domain where the ordinary consequences of a bug are dramatically higher: a double-charged customer, a lost payment, or a corrupted ledger isn't a degraded user experience, it's real money moved incorrectly, sometimes irreversibly. This guide walks through designing such a system end to end, drawing directly on this series' DDD, Event-Driven Architecture, Database Migrations, and Secret Management guides, each of which turns out to be load-bearing infrastructure for getting payments right rather than optional architectural polish. Client → Payment API → [validate, risk-check] → Payment Gateway (Stripe/Adyen/etc.) → Card Network → Bank ↓ ↓ (async webhook) Ledger (source o

Perceptron offers an AI model that it says can help machines navigate the world while also providing in-depth visual intelligence.

Ring says users can still opt for end-to-end encryption.

AWS describes a specification-driven approach for composing flexible data workflows by separating intent from processing logic. Architecture uses declarative specifications, reusable processing capabilities, and validation before execution. AWS reports that the approach can reduce dataset onboarding from weeks to days while supporting traceability, versioning, data classification, and governance. By Leela Kumili

"We're living in this world of scarcity with the Moon."

Olá, comunidade dev.to! Meu nome é Neto, tenho 17 anos e sou estudante de Ciência da Computação no UNIPÊ, em João Pessoa. Atualmente, estou cursando o segundo semestre da graduação e também estudando design profissional, área que considero importante para a criação de soluções digitais mais úteis, intuitivas e visualmente agradáveis. Minha trajetória na tecnologia ainda está no começo, mas já tem sido marcada por descobertas, aprendizados e desafios. Escolhi Ciência da Computação porque sempre tive curiosidade sobre como aplicativos, sites e sistemas funcionam. Quero aprender não apenas a programar, mas também a compreender todo o processo de desenvolvimento de um produto, desde a identificação de um problema até a construção de uma solução. Durante o curso, tive a oportunidade de desenvolver, com alguns colegas, um projeto relacionado à criação de um aplicativo. Essa experiência foi importante porque me mostrou que desenvolver um produto vai muito além de escrever código. Foi necessário discutir ideias, organizar tarefas, pensar nas necessidades dos usuários e encontrar soluções para os problemas que surgiram durante o processo. Mesmo enfrentando desafios simples, percebi como cada obstáculo pode contribuir para o nosso crescimento. Em alguns momentos, precisamos revisar decisões, corrigir erros e adaptar o projeto. Também aprendemos que uma equipe precisa manter uma boa comunicação, pois cada integrante possui habilidades, responsabilidades e pontos de vista diferentes. O estudo de design profissional complementa minha formação em computação. Estou aprendendo que uma aplicação não deve apenas funcionar corretamente: ela também precisa oferecer uma boa experiência ao usuário. Elementos como cores, tipografia, organização das informações, acessibilidade e facilidade de navegação influenciam a maneira como as pessoas utilizam um produto. Ainda tenho muito a aprender sobre programação, design e desenvolvimento de projetos. Porém, entendo que a evolução acontece aos po

A program can produce the right answer and still contain work that does not help it reach that answer. Tests pass, the output looks correct, and unnecessary computations survive because they appear harmless. This becomes easier to miss in AI-generated code. A model can produce a plausible implementation in seconds, but plausible code often includes variables, conversions, or branches that the requirement never asked for. An intent alignment review adds one question to the usual correctness check: Does every instruction help achieve or explain the stated goal? This does not require a formal proof or an exhaustive line-by-line exercise. The useful result can be concise. Correctness and intent Correctness asks whether the observable behavior matches the specification. Intent alignment looks for code that contributes neither behavior nor useful clarity. The goal is not to produce the fewest possible lines. A named constant or helper function can be worthwhile even when the program could run without it. The concern is accidental complexity: code that suggests requirements or design decisions that do not actually exist. AI can help by reading the requirement and implementation together. It can confirm the working behavior, identify unnecessary instructions, and explain whether those instructions are harmful or simply unhelpful. A small Fibonacci example Consider this specification: The function should print to stdout the first hundred elements of the Fibonacci sequence. The phrase "first hundred" does not specify whether the sequence begins with 0, 1 or 1, 1 . For this review, we assume the intended convention begins with 0, 1 and prints one value per line. def print_fibonacci_100 (): a , b = 0 , 1 sequence_limit = 100 display_width = len ( str ( sequence_limit )) for index in range ( sequence_limit ): current_value = int ( a ) print ( current_value ) a , b = b , a + b checkpoint = ( index + 1 ) % 10 == 0 final_pair = ( a , b ) print_fibonacci_100 () Review The implementa

The New York Times has a long article ( alt link ) on surveillance systems aimed at babies. They are increasingly using AI. Nanit and its rivals want to own 24/7 health tracking for the sub-four-foot set. And their already astonishing levels of baby data collection are just the beginning. Nanit recently raised $50 million from investors to expand its use of A.I. and use its camera to track speech and language development, motor skills and more, while extending its presence in children’s bedrooms into early adolescence.

Quick Start makes things a lot easier.

The glasses, called Legato Frames, integrate the company’s patented hearing-assistance technology into the arms of eyewear frames.

The focus is on agentic capability and predictable enterprise deployment.

A steel frame cargo bike with a unique twist: You can let it choose your gear.