标签：#ia

Cómo solucionar el error "Text content does not match server-rendered HTML" en Next.js Este error ocurre cuando el HTML generado en el servidor (SSR) no coincide con el árbol de React que se construye durante la hidratación inicial en el navegador. Es un problema crítico que rompe la experiencia de usuario y puede causar comportamientos impredecibles. Causa raíz En tu caso, el error está relacionado con contenido dinámico que varía entre renderizado del servidor y renderizado del cliente , probablemente por: Uso de Date() o new Date() en el renderizado (ej. fechas de eventos como JUN 9 , JUN 11 , etc.) Uso de typeof window !== 'undefined' o APIs del navegador directamente en el render Metaetiquetas o scripts que modifican el DOM antes de la hidratación (como iOS detectando fechas como enlaces) Configuración incorrecta de librerías CSS-in-JS o Edge/CDN que modifiquen el HTML Solución definitiva (pasos) ✅ Paso 1: Aisla el contenido dinámico con suppressHydrationWarning Si el contenido que varía es intencional (como fechas de eventos), envuelve solo el elemento problemático con suppressHydrationWarning={true} : // app/page.tsx o app/events/page.tsx export default function EventsPage () { const events = [ { name : ' NEXT.JS NIGHTS ' , date : new Date ( ' 2024-06-09 ' ) }, { name : ' AMS ' , date : new Date ( ' 2024-06-11 ' ) }, { name : ' LDN ' , date : new Date ( ' 2024-06-18 ' ) }, ]; return ( < div > < h2 > VIEW EVENTS </ h2 > < ul > { events . map (( event , i ) => ( < li key = { i } > < strong > { event . name } </ strong > { /* ✅ Solo este elemento usa suppressHydrationWarning */ } < time dateTime = { event . date . toISOString () } suppressHydrationWarning > { event . date . toLocaleDateString ( ' en-US ' , { month : ' short ' , day : ' numeric ' }) } </ time > </ li > )) } </ ul > </ div > ); } ⚠️ Importante : suppressHydrationWarning solo funciona en el elemento inmediato, no en hijos. Usa span , time , div , etc., no en contenedores grandes. ✅ Paso 2: Evita Da

A new AI compliance service sits between AI models and end users to flag and replace any messages that might present a compliance problem.

Float32 was the true enemy – not backpropagation, not the architecture. BIN16 replaces every floating-point operation with a single boolean operation: popcount16(XNOR16(a,b)). The result: 82 % MNIST at H=512 with zero floats, zero gradients, zero AdamW and zero learning rate tuning. The training converges immediately in epoch 1 – without warm-up, without decay, without hyperparameter search. Both layers use identical XNOR+popcount operations – training and inference run directly in off-the-shelf DRAM with only 5 transistors per cell. This is the only neural architecture where the same hardware performs both training and inference without modification. The remaining 18 % to 100 % is the bit-mass limit – no training deficit. The groundbreaking insight came when we stopped fighting against float and embraced pure boolean computation. Every complexity – AdamW, backprop, LR schedules, BLAS – dissolved as soon as we removed floating-point numbers from the architecture. Three groundbreaking insights changed everything. Float was the true enemy: backpropagation, AdamW or momentum were never the problem. Float32 introduced numerical noise and instability. Bitwise centroids converge instantly: a running bitwise majority vote per class reaches final accuracy in a single epoch. Random projection is entirely sufficient: W0 does not need to be trained – a random boolean projection provides adequate separation. The entire training consists of only four steps and 220 lines of C – without learning rate, without GPU, without any conventional optimization. This architecture opens the door to a future in which neural networks compute directly in memory. No more expensive GPUs, no endless hyperparameter tuning marathons. Instead, pure, efficient logic that is ready for use immediately and everywhere. Imagine: AI systems that train and infer in off-the-shelf DRAM – energy-efficient, lightning-fast and accessible to everyone. BIN16 is the first step into this new era. Identical operations

We ran into something that didn't seem like a problem until it was. Each agent had access to the tools it needed and everything worked fine in isolation. The issues started once agents were running in parallel. Two parts of the system would try to use the same tool or hit the same resource at the same time. Results became inconsistent and it wasn't obvious why. Limiting access helped in some cases but slowed things down elsewhere. Too much access caused race conditions. Too little caused steps to stall waiting for something to free up. Most of the coordination logic ended up sitting outside the agents themselves. Every new agent added more decisions around what it should be allowed to access and when. There isn't a shared way to manage tool access across a multi agent system. How are you handling this when multiple agents are running at the same time? submitted by /u/Logical-Bite-4221 [link] [留言]

submitted by /u/NISMO1968 [link] [留言]

The global health care sector is under increasing strain. Decades of chronic underinvestment and constraints in recruitment have coincided with a surge in demand for services for aging populations. Gaps in provision are already taking a toll, with fragmented access to care and high rates of stress and burnout among staff. And it’s getting worse.…

The restrictions affect posts related to body image and mental health.

Anthropic filed a confidential S-1 with the SEC this week, moving toward a public listing that will put disclosure obligations and investor return expectations directly in tension with its safety-first positioning. The IPO filing lands as GitHub Copilot ends flat-rate billing and switches to metered consumption, meaning teams with heavy usage face immediate cost spikes with no grace period to audit seat activity. OpenAI's frontier models and Codex are now available directly on AWS , which changes vendor-lock assumptions for inference pipelines and removes the proxy layers some teams were routing around. These two moves together suggest the "get developers hooked, then price for real" phase is now active across the stack. The security picture is worse. A researcher documented a Meta AI social-engineering exploit that handed attackers access to high-profile Instagram accounts by manipulating the agent through its account-management tool calls. No sophisticated jailbreak required. Any agent with write permissions to external accounts is now a confirmed social-engineering surface, and the Meta incident is the clearest public proof of that so far. Separately, malicious npm packages reached Red Hat Cloud Services repositories and were downloaded at scale, which means JS dependency audits for cloud-native stacks need an immediate re-run against known-bad versions, not a scheduled one. On the hardware side, Intel's Crescent Island GPU ships with up to 480GB VRAM , which revises local inference capacity planning for large MoE models in ways that weren't on most teams' roadmaps six months ago. Alphabet announced an $80 billion equity raise for AI infrastructure , which will tighten GPU allocation queues and data center procurement timelines across all cloud providers regardless of whether you're an Alphabet customer. The pattern across all of this: monetization is accelerating faster than the trust infrastructure required to support the attack surface already in production. A

If you work a white collar job, you’re probably scared of AI replacing you. AI started at the desk — data entry, customer service, software. Now its stepping onto the factory floor: Amazon robots moving inventory, Figure bots handling BMW parts, Tesla building Optimus for repetitive labor, and warehouses being automated. But at the end of the day, AI is a technology. We cannot stop it any more than we could stop electricity or the assembly line. The problem is not that machines are becoming powerful. The problem is the economic machine around it. Let’s face it: Capitalism doesn’t have the ability to support this kind of technology. Capitalism was built for a world of scarcity, where human labor was necessary and wages gave people access to goods. But as AI advances exponentially, it can produce more with fewer workers, while capitalism still distributes wealth through jobs it is actively eliminating. The result is abundance trapped behind an archaic wage system. I believe that we NEED to get governments and major tech companies to start seriously planning for a universal basic income funded by AI-driven productivity. As automation replaces more human labor over the coming decades, UBI will become essential to prevent mass instability and ensure that the wealth created by AI supports society as a whole, not just the companies that own it. We already know the wealth gap is too wide. If we don’t start addressing AI-driven inequality now, that divide will grow exponentially as more labor is automated and more wealth concentrates at the top. Without a plan to distribute the gains from AI, we risk mass instability and eventual economic collapse. Capitalism built the machine that could end scarcity, but not the system that could distribute its output. It’s time that we, as a global society, start thinking about phasing out that old machine. submitted by /u/SuddenEducation442 [link] [留言]

Original post: GitHub Copilot for Engineers: Getting Better Results GitHub Copilot moved to usage-based billing in June 2026, dropping the flat subscription model that made monthly costs predictable. For teams using it heavily across multiple projects, that shift puts a premium on being deliberate: reaching for the right model, keeping prompts focused, and building a configuration that produces good results without a lot of back-and-forth iteration. Many of us install the extension, start with the defaults, and only tune settings later. The defaults are a reasonable starting point, but they are not a full configuration. A small investment in setup changes how much you get out of every request on an ordinary working day, and that matters more now that each request has a cost attached. This guide covers the full path: getting the tooling in place, choosing models with cost in mind, layering global and project-level rules, and building out instructions, agents, and skills that make Copilot predictable across different kinds of work. Architecture overview Diagram fallback for Dev.to. View the canonical article for the full version: https://sourcier.uk/blog/github-copilot-for-engineers Before you start Subscription and VS Code extension You need an active GitHub Copilot subscription. Plans are available at individual, business, and enterprise tiers at github.com/features/copilot . Once active, all tools use your GitHub account credentials. The GitHub Copilot extension for VS Code is the primary day-to-day interface. Install it from the Extensions panel or via the CLI: code --install-extension GitHub.copilot The extension provides inline completions as you type, Copilot Chat in the sidebar, inline chat on any selection via Cmd+I / Ctrl+I , agent mode for multi-step tasks, and multi-file edits with a single review step. Defaults keep improving, so avoid cargo-culting old setting lists. Focus on non-default tweaks that improve signal quality and control usage: Setting Value

Donald Trump killed an executive order to regulate AI. Now, administration officials and AI executives are trying to figure out if there’s anything left to piece back together.

submitted by /u/lilubba [link] [留言]

Hello this might be weird but I am doing a study on society's view on AI as a school project. Therefore I am asking all kinds of communities and trying to get a very wide audience. This is clearly an AI sentric sub so hopefully his is relavent? I would be very happy if any of you would like to be a part of it! submitted by /u/Timely_Special_5011 [link] [留言]

Ameer Al-Khatahtbeh was just trying to find an outlet for Muslim news. Now he has more than 12 million followers.

This article is from Making AI Work, MIT Technology Review’s limited-run newsletter examining how to apply LLMs across industries. To receive it in your inbox,sign up here. From accounting to design to market research and product development, there’s a staggering breadth of skills needed to run a business. A large company can hire experts to…

This is what production AI costs when nobody's watching. A 4-person team posted their Anthropic invoice. Agentic systems don't make just one API call per task. They read context, plan steps, call tools, hit errors, retry. Each step is a separate call to Opus at $25 per million output tokens. One user instruction can trigger 20+ calls before it's done. A lot of engineers have no idea what a single task costs end-to-end. - They don't know which prompts trigger the longest loops - They don't know how many silent retries are happening in the background - They can't tell which tasks could run on a smaller model without losing quality Frontier models are genuinely impressive. But agentic systems don't make one call.. they make dozens. Every single day. And most teams aren't watching the meter. If you're running agentic workloads in production, start tracking what individual tasks actually cost before your next invoice does it for you. submitted by /u/aipriyank [link] [留言]

Most AI tutorials start with a setup checklist. Pick a model provider. Create an account. Wire up a...

So basically Claude at 20 buck sub is not much better than free. Chatgpt. It is pretty much shit. Gemini. It seems to have some reductions to its abilities in the last couple of months as well. The 20 buck price range used to have lots of good ais. Now they are all limited, downgraded. What would be the king in this price range? I have found myself using gemini ai pro with other ais as free on the top of that. submitted by /u/aluode [link] [留言]

Hi, I'm prepare for a phd in the US in medical AI, so I want to improve listening and speaking skills. I warry about admission interview, and I like discuss research. I find male friends because I have a girlfriend. I don't want to make her worry. I find the friends online firstly. Please feel free to contact me. submitted by /u/CrazyIndependent7436 [link] [留言]

submitted by /u/CatCognition [link] [留言]