标签：#AI

Global music streaming service Deezer is taking a contrarian approach to AI, even as it adds a feature that lets fans remix songs.

If League of Legends and Valorant players have the right hardware and elect to opt into "pre-boot security mechanisms and Windows' own native protection features," then, starting today, they can switch the Vanguard anti-cheat software from always-on to one that's "on demand." With "Vanguard Pre-Check," the kernel-level driver won't launch when your system does, according […]

Agility Robotics, the humanoid robotics startup that spun out of Oregon State University in 2015, expects to generate $620 million in proceeds.

Figma's update adds a new code layer, support for motion and shaders, and the ability to create custom plug-ins for various tasks using AI.

Figma has revealed some new design and coding product updates at its annual Config conference that aim to help creatives "push their ideas further" and automate tedious tasks with AI. Part of this is a reimagined canvas that's now optimized for full-stack development, according to Figma, bringing teams, AI agents, tools, and materials "together in […]

As web developers, engineering workflows are heavily dependent on the NPM registry. However, the traditional process of searching, auditing, and integrating packages remains highly fragmented. Developers are routinely forced to hop between npmjs.com, GitHub source repositories, and external documentation tabs simply to verify bundle sizes, check dependency trees, or generate setup boilerplate. Following a strong reception on LinkedIn, X, and Facebook, the Motion Mind Team has introduced LibPilot to the dev.to community. LibPilot is not a traditional registry interface; it is an AI-powered search engine and discovery hub engineered to index, track, and analyze over 4,000,000 NPM packages in real time. Here is an architectural breakdown of how LibPilot restructures package exploration for modern developers and autonomous AI code agents. Intent-Based Discovery and Global Search Architecture Traditional search engines require users to input the exact name or strict keyword of a library. LibPilot introduces a dual-input architecture on its home page to eliminate this constraint: Direct Registry Querying: Users can input full or partial package names into the global search bar to instantly surface clean, structured, and typed suggestions directly from the live NPM ecosystem database. Contextual AI Recommendations: For scenarios where the ideal package is unknown, developers can type out a complete description of their project architecture or system constraints (for example: "a lightweight, typed state management engine that handles server-side rendering natively"). LibPilot's internal AI agent processes the functional requirements and suggests production-ready libraries suited for that stack. Continuous Context AI and Interactive Onboarding A core goal of the platform is reducing developer friction and maintaining deployment momentum. LibPilot transitions static package documentation into an interactive environment: Unlimited AI Chat Architecture: Once a library is select

When we started Reach , the plan was simple. We had a bunch of small businesses we were already in touch with - real SMBs, the kind that live on WhatsApp and don't have a "tech team." We'd give each of them an AI agent that talks to their customers, hand them a clean starter template for the instructions, and let them tweak it from there. That was the whole bet: give people a good starting prompt and a template, and they'll play along. We were so wrong it's almost funny now. The part nobody warns you about Here's the thing about prompt engineering that you only learn by watching non-technical people try to do it: writing the instructions is not the hard part. The hard part is thinking about the task in the abstract . We'd hand a business owner an agent that mostly worked, and say "just adjust the instructions when it gets something wrong." Sounds easy. It is not. Sitting down and imagining all the ways a conversation could go, then writing rules for a machine to follow - that's a skill. It's basically a job. And it's a completely different job from running a flower shop or a real estate office. So they got stuck. They'd open the instructions editor, stare at it, and close it. The agent stayed mediocre because the iteration loop we designed required them to be part-time prompt engineers. A few of them just quietly left. That stung, but it taught us the actual problem. What "iteration" actually looked like We started doing the iterations for them, manually. And once we did that a few dozen times, a pattern jumped out. The feedback never led to an abstract change. It was never something that made us "rethink the agent's persona" or "restructure the system prompt." It was tiny, concrete, and tied to a real conversation: "It suggests all of our services, but honestly most customers only care about these three - push those." "It pulled our opening hours from the website and they're just wrong. We changed them months ago." These were one-line corrections. The owner knew ex

Technology companies are extending AI beyond code generation into earlier stages of the software lifecycle, including PRD validation, design inputs, and code review. Initiatives from Uber, DoorDash, and Cloudflare highlight a shift toward AI-driven governance layers that evaluate engineering artifacts before implementation while preserving human oversight across the development pipeline. By Leela Kumili

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.

OpenAI has just revealed a new "intelligence processor" chip for AI servers made in partnership with Broadcom. The chip, called Jalapeño, is designed to power current and future large language models, according to an announcement on Wednesday. Jalapeño is an ASIC (Application-Specific Integrated Circuit), meaning it's designed for a specific purpose: AI inference. With AI […]

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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

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:

Right out of the box, the new Google Home Speaker passed a couple of important tests. Even with the volume at 100 percent and music blaring out of the speaker, it quickly ducked the audio and listened every time I said "Hey, Google." In fact, in two days of testing, the speaker's three microphones haven't […]

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

TL;DR: An API gateway manages HTTP traffic between services — auth, routing, rate limiting, load balancing for REST and gRPC. An AI gateway manages LLM workloads — token-based rate limiting, model routing, cost attribution, semantic caching, guardrails. Use an API gateway for your microservices. Use an AI gateway for your LLM traffic. Most production teams eventually need both, sitting at different layers. This post walks through exactly where each one fits. When we started adding LLM features to our platform, we already had Kong running for our microservices. The instinct was natural: route the LLM traffic through Kong too. Same auth, same rate limiting, same observability stack. One gateway to rule them all. It worked — for about six months, and only in the sense that requests got through. What it didn't give us was anything useful for actually managing AI workloads. We had no idea what each team was spending on tokens. We had no way to set a budget cap that would fire before the bill arrived. Our rate limits were based on requests per minute, which meant a single request with a 50k token prompt counted the same as one with a 200 token prompt. And when OpenAI had a partial outage, Kong had no concept of "try Anthropic instead" — we just served errors. None of that is a criticism of Kong. It's doing exactly what it was designed to do. The problem was us expecting an API gateway to handle a fundamentally different category of infrastructure problem. Here's the precise distinction, and why it matters architecturally. What an API gateway actually does An API gateway is a reverse proxy that sits between client applications and backend services. It handles the cross-cutting concerns of service-to-service HTTP communication: authentication, authorization, rate limiting, load balancing, SSL termination, request transformation, and routing based on URL paths or headers. A typical request flow through an API gateway: Client sends a request to the gateway endpoint Gateway ve

The US government is reportedly asking Meta to share its AI models for review, in the midst of growing security and safety concerns over the technology.

The biggest Switch 2 exclusives so far have largely been about scale. Mario Kart World introduced a wide open continent to race across, Donkey Kong Bananza let you smash basically everything around you, and Pokopia brought an expansive Minecraft-style creative experience to the Pokémon universe. Star Fox is different. A remake of a Nintendo 64 […]

Naomi Saphra discusses 5 rules governing language model behavior, breaking down why LLMs act like populations rather than individuals. She explains how tokenization creates strange semantic blind spots and highlights the mechanics of sycophancy, showing how models leverage subtle data associations to match user biases and demographics - even guessing political views based on favorite sports teams. By Naomi Saphra