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Amazon bucked its usual tradition of having Prime Day in July. Prime Day 2026 is happening in June, kicking off in just a few weeks. Prime members will get access to many deals starting June 23rd at 3:01AM ET through June 27th at 3:01AM ET. As with Amazon’s previous events, you don’t need to be […]

According to every product demo from the last four years, planning a trip is a killer use case for AI. Just tell it where you're going, they all promise, and your chatbot / agent / other buzzword will exhaustively search travel options, read up on all the fun things to do, check all the local […]

Review AI edits like stacked PRs at generation time. Discussion | Link

Blockchain performance is determined by consensus rules. There is no acceleration layer within the protocol. One of the most common misconceptions about blockchain technology is the belief that transaction speed can be dramatically increased through special tools, hidden settings, or external services. While applications can improve user experience and optimize how information is presented, they cannot change the fundamental rules that govern how blockchain networks process transactions. At the core of every blockchain is a consensus mechanism. Consensus is responsible for ensuring that independent participants agree on the validity and order of transactions before they become part of the permanent ledger. Whether a network uses Proof of Work, Proof of Stake, or another consensus model, transaction processing remains tied to the protocol rules that all participants follow. Every transaction moves through a structured lifecycle: submit → validate → confirm Submission introduces the transaction to the network. Validation ensures that the transaction complies with protocol requirements and contains legitimate data. Confirmation establishes agreement across the network and records the transaction as part of the blockchain. These stages are not optional. They are essential to maintaining consistency and trust within decentralized systems. Because blockchain performance is governed by consensus, there is no protocol-level acceleration layer that can bypass validation or force immediate finality. No application can override consensus. No service can remove verification requirements. No external process can alter the execution sequence established by the protocol. What users often interpret as slow performance is usually the result of network conditions such as congestion, validator workload, transaction prioritization, or fee market activity. These factors can influence confirmation times, but they do not change the underlying rules of the system. Blockchain networks are d

Keyboard accessibility is one of the most important — and most neglected — aspects of web accessibility. An estimated 2.5 million Americans have motor disabilities that prevent mouse use. If your site can't be operated entirely by keyboard, you're excluding them completely. The Four Core Principles WCAG 2.2 Principle 2 (Operable) contains the keyboard requirements: 2.1.1 Keyboard (AA): All functionality must be operable via keyboard 2.1.2 No Keyboard Trap (AA): If focus moves into a component, it must be possible to move it out 2.4.3 Focus Order (AA): If page can be navigated sequentially, order must be logical and predictable 2.4.7 Focus Visible (AA): Any keyboard-operable UI must have a visible focus indicator 2.4.11 Focus Appearance (AA, new in 2.2): Focus indicator must meet size and contrast requirements Testing Without Automated Tools Start with the basic keyboard test: Unplug (or ignore) your mouse Press Tab to move forward through interactive elements Press Shift+Tab to move backward Use Enter/Space to activate buttons, links, checkboxes Use arrow keys for radio groups, menus, sliders Use Escape to close dialogs and menus Any element you can't reach or activate? That's a WCAG 2.1.1 failure. The Most Common Keyboard Failures Custom dropdowns and menus // ❌ Keyboard inaccessible function Dropdown ({ items }) { return ( < div onClick = { toggle } className = "dropdown" > { items . map ( item => ( < div onClick = { () => select ( item ) } > { item . label } </ div > )) } </ div > ); } // ✅ Fully keyboard accessible function Dropdown ({ items }) { return ( < div role = "combobox" aria-haspopup = "listbox" aria-expanded = { isOpen } tabIndex = { 0 } onKeyDown = { handleKeyDown } // handles Enter, Space, Arrows, Escape className = "dropdown" > < ul role = "listbox" > { items . map (( item , i ) => ( < li key = { item . id } role = "option" tabIndex = { - 1 } aria-selected = { i === activeIndex } onKeyDown = { e => e . key === ' Enter ' && select ( item ) } > { item

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

Hi, I'm Heet Sanghani, a Python Developer and AI/ML Engineer from Ahmedabad, Gujarat, India. I currently work at BrainerHub Solutions building backend systems and AI-powered applications. In this post, I want to share how I built BidXpert — a real-time auction platform using FastAPI. What is BidXpert? BidXpert is a real-time bidding platform where users can create auctions, place bids, and get instant updates using WebSockets. Tech Stack Backend: FastAPI + Python Database: PostgreSQL Real-time: WebSockets Auth: JWT Authentication Deployment: Docker What I Learned Building BidXpert taught me how to handle concurrent WebSocket connections efficiently in FastAPI and manage real-time state. About Me I'm Heet Sanghani — Python Developer & AI/ML Engineer based in Ahmedabad. Check out my portfolio and other projects at: 👉 https://heet-sanghani-portfolio.vercel.app/ python #fastapi #webdev #ai

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

Every inventory tool says real-time. Every single one. Open the settings. Find the sync frequency configuration. It says 15 minutes. Or 10. Or 30 on the cheaper plan. That's not real-time. That's a cron job. There's a meaningful architectural difference and the industry has collectively decided to pretend there isn't. I want to make the technical case for why this matters — and ask why so few tools have actually fixed it. What "real-time" actually means technically Real-time in distributed systems has a specific meaning. It means the system responds to events within a bounded, predictable latency — not on a schedule. javascript// This is NOT real-time — this is scheduled // Latency: up to 15 minutes (the full interval) setInterval(async () => { const stock = await getSourceOfTruth(); await syncToAllChannels(stock); }, 15 * 60 * 1000); // This IS real-time — event-driven // Latency: network round-trip (~milliseconds) orderEventBus.on('order.confirmed', async (event) => { const updated = await decrementStock(event.sku, event.qty); await propagateToAllChannels(updated); }); The first example responds to state changes on a schedule. The second responds to events as they happen. These are fundamentally different architectures with fundamentally different latency guarantees. Calling the first one "real-time" is technically incorrect. It's scheduled sync. The schedule is just short enough that most users don't notice — until they do. When users notice The failure mode is predictable and well documented: javascript// Flash sale scenario — 10x normal velocity const normalOrdersPerWindow = 500 / ((24 * 60) / 15); // ~5.2 const flashSaleOrdersPerWindow = normalOrdersPerWindow * 10; // ~52 // 52 orders processed against potentially stale stock // per 15-minute window // across multiple channels simultaneously // none of which know what the others have sold 52 orders per window. At 2% oversell rate — just over 1 oversell per window. Across 96 windows per day — nearly 100 oversel

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

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. How small businesses can leverage AI From accounting to design to market research and product development, there’s a staggering breadth of skills needed to run a business. Large companies can hire…

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]

Learn strategies for building interrupt-resilient AI workloads on Google Kubernetes Engine (GKE).

"The market's going to continue to find exciting new things."

A unicorn Beanie Baby. A 15-pound green bowling ball. A pair of dentures. These are just some of the items left behind in robotaxis in the past year, according to Uber's annual Lost and Found Index. For the first time, the company is expanding its annual of accounting of things forgotten in Uber vehicles to […]

OpenTelemetry has introduced a new "Blueprints" initiative aimed at reducing the growing complexity of deploying and operating observability systems at scale. By Craig Risi

submitted by /u/NISMO1968 [link] [留言]

I’ve rounded up the best smart speakers that let you talk to Alexa, from the popular Echo Dot to the newest Echo Studio.

Session reports for Claude Code & Codex to improve your code Discussion | Link

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.…