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The 8BitDo Arcade Controller Pro is a subtle update for hardcore fighting game fans.

The Samsara Tracking Label hides a BLE system that can offer real-time location in a disposable package.

Zoox, the autonomous vehicle company owned by Amazon, unveiled a new look for its boxy, bidirectional robotaxi, calling it the "next evolution" of the vehicle intended for mass production. The company is currently operating a free robotaxi service in San Francisco, Las Vegas, Austin, and Miami while it waits for the federal government to approve […]

8BitDo announced a new version of its all-button Arcade Controller that debuted a little over a year ago. The new Arcade Controller Pro looks similar to 8BitDo's original with a leverless (or Hitbox) design that replaces joysticks with buttons for improved precision and responsiveness. The new Pro version comes with upgrades that include an even […]

Branches received exceptions to Hegseth's policy that made the shot optional.

Named Jalapeño, the new processor was designed specifically for the unique needs of OpenAI's inference systems.

These Prime Day specials on a Samsung SSD and Lexar SD card represent the best digital storage deals you're likely to find this year.

Philips Hue has announced a connectivity upgrade for its Matter-over-Thread-enabled smart bulbs that will allow them to run both Thread and Zigbee at the same time. The change means you can connect compatible Hue bulbs and fixtures directly to a Matter ecosystem - Apple Home, Amazon Alexa, or Google Home - while keeping them linked […]

You have just 3 days left to save up to $190 on your pass to TechCrunch Founder Summit 2026 before Early Bird rates end on June 26 at 11:59 p.m. PT. Register here.

New details have emerged about Valor's latest fund, which last year announced it was raising an unspecified amount of capital.

It's way more convenient, but way less efficient.

Quando comecei a trabalhar com tecnologia, há mais de duas décadas, a Inteligência Artificial era algo restrito a laboratórios de pesquisa e ficção científica. Hoje, ela está embutida no aplicativo que recomenda sua próxima série, no e-mail que filtra spam automaticamente e até no GPS que recalcula sua rota em tempo real. A IA deixou de ser promessa para se tornar infraestrutura invisível do cotidiano. Neste artigo, quero ir além do hype e mostrar, com exemplos concretos, como essa tecnologia já transforma a forma como vivemos e trabalhamos. Produtividade pessoal e profissional turbinada O caso de uso mais palpável da IA hoje está na produtividade. Assistentes baseados em modelos de linguagem (LLMs) como ChatGPT, Claude e Gemini reduziram drasticamente o tempo gasto em tarefas que antes consumiam horas: redação de e-mails, geração de relatórios, resumos de reuniões e até depuração de código. Na minha rotina como gestor de TI, integrei essas ferramentas a fluxos de trabalho reais. Por exemplo, utilizo modelos de IA para revisar contratos de smart contracts escritos em Rust para a rede Stellar, identificando padrões de vulnerabilidade antes mesmo da auditoria formal. Não substitui a perícia humana, mas funciona como uma primeira camada de triagem que economiza tempo precioso da equipe. Algumas aplicações práticas que recomendo testar: Transcrição e resumo automático de reuniões com ferramentas como Otter.ai ou Fireflies Geração de documentação técnica a partir de comentários de código Automação de respostas em suporte de primeiro nível via chatbots treinados com a base de conhecimento da empresa O segredo está em tratar a IA como copiloto, nunca como piloto automático. A revisão humana continua indispensável, especialmente em contextos críticos. Saúde, finanças e decisões do dia a dia A IA também opera nos bastidores de decisões que afetam diretamente nossa qualidade de vida. No setor de saúde, algoritmos de visão computacional já auxiliam radiologistas na detecção pr

I just completed my bachelor’s last month and I have realised basic programming knowledge about different languages won’t get me any job. So I have decided to pursue Mern stack + Ai integration path. However I don’t have any money to buy courses. Lucky I have an internet connection and a laptop. I’m already familiar with basic javascript and I want to dive into the intermediate level at the least ASAP! Are there any free courses online for Mern ?? I’m already looking into freecodecamp. However if there are other unpopular options available, help would be appreciated. Thanks!! PS :- I attached google link cuz didn’t know what else to do. submitted by /u/Mental-Parsnip-8026 [link] [留言]

Google's new speaker is a modest update, but the question is whether Gemini is a good smart home assistant.

Google’s first new smart speaker in six years is here and once again leads its competitors—now with paywalled features.

Synchronous billing is easy, and that's the problem — it makes you think all billing is easy. When a request does its work inline, the billable number is in the response by the time you send it. The gateway meters from there — the meter write, retries and all, is its problem, not yours. From your side, synchronous billing is one number in the response. Asynchronous work breaks that. The request submits a job; the work happens later, in a worker; the result comes back through a poll or a callback. And the thing you bill for — characters processed, pages converted — isn't known when the request arrives. It's known when the job finishes . So you can't meter at the edge. The meter has to fire from the completion path. And the real difficulty is firing it exactly once per unit of completed work — because requests, polls, and retries all conspire to make that zero times or many times. This is platform-agnostic. Every submit-process-poll API has it. I'll use the system I run as the example, but the shape is the same anywhere. Three ways metering goes wrong On arrival. Carry the synchronous habit over and you meter when the job is submitted. But you don't know the size yet, so you're forced into a crude flat fee — or you bill for work that hasn't happened and might fail. Wrong unit, wrong time. On retrieval. The subtle one. You wire the meter to fire when the client fetches the result. Now a client who submits a job, lets it run — costing you real money downstream — and never bothers to poll is never billed. You did the work for free. "Completion" is not "the client picked up the result." It's the worker finishing. Without a fixed quantity. Input characters or output characters? Pages before OCR or after? If you haven't decided exactly what you measure and where, invoices drift and customers argue. Decide once; measure there. All three point the same way: meter on measured work-completion, with a fixed definition of the unit. Not on arrival. Not on retrieval. The mechanism:

Three AI stories landed this week that all poke at the same nerve: the images, video, and films we actually look at are getting an AI layer — and the line between "real" and "AI-made" keeps thinning. Quick rundown in the short, then my take below: 1. ChatGPT will start showing real, licensed photos — not AI fakes. OpenAI signed a multi-year display deal with Getty Images, so licensed photography shows up inside ChatGPT's search and discovery. It's display-only — the photos aren't used to train models. The twist I can't get over: AI image generation had nearly wiped Getty out (stock down ~55% on the year), and this one deal sent the shares up ~145%. The thing AI almost broke got rescued by AI. 2. ByteDance — yes, TikTok's parent — teased Seedance 2.5: a full 30-second video generated in a single shot, no stitching, up to 50 reference inputs, 4K. Most tools still cap out around 5–10 seconds, so "30s native, one pass" is a real jump in how usable the output is. Public launch is early July. 3. Google DeepMind is partnering with A24 on AI filmmaking — a ~$75M, non-exclusive deal to co-build Veo-powered tools. Notably Google gets no access to A24's film library or data. A prestige studio building with AI in the open makes the whole "AI in Hollywood" debate a lot less hypothetical. As someone building a daily AI-news pipeline on the side, the Getty one is the story I keep chewing on. So much of the "AI vs creators" fight has been framed as scrape-or-die. A display-licensing deal is a third option — pay to show the real thing, instead of generating a confident fake or quietly training on someone's work. I don't know if it scales, but it's the first move in a while that didn't feel zero-sum. The Seedance + A24 pair points the other way though: generation is getting longer, more controllable, and is walking straight into real production. So we get both at once — more verified real media and more convincing synthetic media, in the same week. Curious where other builders land:

The Switch 2’s price is set to increase by $50 on September 1st, starting at $499.99, so if you’re purchasing one before that (or you already own one), now’s a good time to also save on accessories while you can. Amazon’s Prime Day has delivered many great game deals, but some of the best sales […]

AI has made it easier to produce working software. That part is real. It can write code, draft documents, research a topic, scaffold a prototype, and debug a problem faster than most teams can finish writing a decent ticket. But faster building doesn't automatically mean better product decisions. That's the part I keep coming back to. For decades, software teams optimized around delivery. Requirements, design, development, QA, release. Waterfall softened into Agile. Agile grew into DevOps. The practices changed, but the assumption underneath stayed pretty stable: building software is expensive, so plan carefully before you start. That made sense because, for a long time, it was true. Now that assumption is breaking. AI is doing to software what calculators did to accounting. It isn't eliminating the job. It's moving the job up a level. The syntax, boilerplate, first draft, and some of the debugging are getting offloaded. The work doesn't disappear. The bottleneck moves. Learning is still expensive Here's what didn't get cheaper: understanding what people actually need getting stakeholders aligned deciding what evidence would change your mind putting something real in front of users reading the signal without fooling yourself The old question was: Can we build it fast enough? The new question is: Do we understand the problem well enough? That sounds like a small shift, but it changes the work. It changes what strong engineers spend time on. It changes what product people need from engineering. It changes how teams should define "done." If the code ships but nobody learns anything, did the team actually move forward? Sometimes yes. Often no. Users don't know until they can touch it People are not great at specifying requirements up front. Not because they're difficult. Because they're human. Most of us don't know how we feel about something until we can react to a version of it. A mockup. A prototype. A rough slice. A real workflow with sharp edges. So the fastest pat

Long-running workflows look simple when you first build them. Something happens. A few systems exchange data. Everything completes. Done. At least that's the expectation. Reality is very different. The biggest thing I underestimated was time. Not execution time. Elapsed time. Because once workflows start running for hours, days, or continuously, strange things start happening. APIs become temporarily unavailable Data changes halfway through the process Retries arrive much later than expected Someone manually updates a record Another system processes things in a different order Nothing is broken. But everything is slightly different from when the workflow started. Early on, I assumed workflows were transactions. Start. Execute. Finish. Now I think of them as conversations between systems. And conversations can get interrupted. Another thing I underestimated: State changes. You might start processing an order that is "pending". Ten minutes later, another system marks it as "cancelled". An hour later, a retry comes in from an earlier step. If your workflow only thinks about data, weird things happen. Because the world has changed while the process was still running. Long-running workflows also expose assumptions you didn't know you made. Like: this API will always respond quickly data will arrive in order users won't modify records manually retries will happen immediately Those assumptions survive in testing. Production removes them quickly. One thing that changed how I build these systems: I stopped asking: "Will this workflow finish?" And started asking: "What state will the world be in when it finishes?" Because those are two very different questions. Most problems in long-running systems aren't caused by one big failure. They're caused by lots of small changes happening while the workflow is still alive. And if you don't account for that, eventually the workflow finishes successfully and still produces the wrong outcome. This is something we think about constantly