标签：#AR

Everyone is sick of spam calls and creeper sites that show weirdos where you live—but can any service solve it?

Whether you need a tent, sleeping pad, rain jacket, or new pack, REI’s Independence Day sale has something for everyone.

How AI Changes What 'Learning' Means Hook: Amre learned Python using AI. No, not just using AI as a supplementary tool—he learned from AI, as if it were his personal tutor. If AI can teach a complex skill like programming, what does that mean for the future of education? Background: The traditional education system, with its structured curriculums and standardized testing, has long been criticized for its rigidity. Enter AI, and suddenly, the landscape of learning is shifting. AI tutors, adaptive learning platforms, and intelligent coding assistants like GitHub Copilot are becoming ubiquitous. These tools are not just helping students with homework; they are fundamentally altering the way we acquire new skills and knowledge. Consider Amre's experience. Frustrated with the slow pace of a traditional Python course, he turned to an AI-powered learning platform. The AI assessed his current knowledge, identified his learning style, and tailored a curriculum specifically for him. It provided instant feedback, suggested additional resources, and even simulated real-world coding challenges. Within weeks, Amre was writing functional code and solving complex problems—something he hadn't thought possible in such a short time. This isn't an isolated incident. Across the globe, learners are turning to AI for personalized education experiences. From language learning apps that adapt to your pace and style, to AI tutors that can explain complex mathematical concepts in multiple ways until you understand, the traditional classroom is being redefined. Analysis: The most significant change AI brings to learning is personalization. Unlike traditional education systems that follow a one-size-fits-all approach, AI can adapt to the unique needs of each learner. It can identify gaps in knowledge, adjust the difficulty level of tasks, and provide customized feedback. This level of personalization was previously only available to those who could afford private tutors. Moreover, AI democrati

TL;DR If the Vercel CLI keeps trying to open a dev link against your Vercel project during local next dev runs, set VERCEL_EXPERIMENTAL_DEV_SKIP_LINK=1 in the shell that launches the dev server, or add it to .env.local at the project root, and restart the process. The flag is opt-in, all-uppercase, and only affects local CLI behaviour. It never reaches your deployed build, and the production runtime on Vercel does not read it. If the CLI still tries to link after a restart, scroll to Debugging when the skip link isn't working for the version-compatibility and process-tree checks that catch the cases the basic setup misses. I have shipped this flag in three production monorepos and the same four mistakes account for almost every "I set it and it did nothing" report I see. What VERCEL_EXPERIMENTAL_DEV_SKIP_LINK actually does VERCEL_EXPERIMENTAL_DEV_SKIP_LINK is an opt-in environment variable the Vercel CLI honours when it runs alongside a local Next.js dev server. Its job is narrow: tell the CLI to skip the step where it would normally reach out to Vercel and create or refresh a dev link against your Vercel project. A "dev link", in the Vercel sense, is a local connection record that lets vercel dev and some Vercel-only local emulators (KV, Postgres, Edge Config) pull real values from a Vercel project. It is useful when you want production-shaped data during development, and a real annoyance when you do not — for example in CI sandboxes, offline laptops, monorepo workspaces that share a single project, or any time you want next dev to behave like a plain Node process without the CLI wrapping it. The variable is shipped under the VERCEL_EXPERIMENTAL_ namespace, which Vercel uses to mark features that can change between CLI versions. That has two practical consequences: the name must be uppercase with underscores, and you should not build production logic on top of it. I treat it like a local-dev knob, set per shell session, and never check it into CI as a hard dependen

Most AI applications begin with a direct model integration. Install an SDK, add an API key and send a prompt. This works well until the application needs a second provider. A coding task may work better with one model, while another may be more suitable for vision, reasoning, long context or low-cost processing. At that point, model access becomes an architecture problem. The dependency problem When provider-specific logic lives inside product code, the application becomes responsible for: authentication request formats model names rate limits retries usage tracking error handling provider switching Every new provider increases this complexity. The solution is to introduce a model layer between the application and the providers. Define workloads, not providers Your product should describe what it needs instead of deciding how a specific provider should deliver it. type Workload = | "reasoning" | "coding" | "vision" | "fast-response"; interface AIRequest { workload: Workload; input: string; } interface AIResult { content: string; model: string; provider: string; usage: number; } The routing policy can remain outside the application: const modelPolicy = { reasoning: "reasoning-model", coding: "coding-model", vision: "vision-model", "fast-response": "low-latency-model" }; async function runAI(request: AIRequest): Promise { const model = modelPolicy[request.workload]; return modelLayer.generate({ model, input: request.input }); } Now the product depends on workloads and capabilities rather than one provider’s SDK. Compatibility is only the beginning A compatible request format reduces integration work, but production systems also need: centralized API keys usage and cost records retry policies provider health checks billing rules fallback models operational logs This is why multi-model infrastructure is becoming its own application layer. VectorNode is being built around this category: multi-model access and operations for AI applications. The long-term advantage is not

Originally published on the Keylight blog . A short founder note on why Keylight exists. Every product starts as somebody's unsolved problem; this is mine, and if you are shipping a paid app you have probably run into the same one. The problem I kept hitting I wanted to sell a desktop app directly. Not through the App Store — directly, to customers I could actually talk to. The payment side was easy: Stripe is excellent and the decision took an afternoon. Then I got to licensing, and everything slowed down. Stripe takes the money. It does not give you a license key. It does not sign anything your app can verify. It does not know what a device activation is. The moment a customer has paid, you are on your own: you need to mint a key, sign it so it cannot be forged, deliver it, let the app check it, track devices, and revoke it on a refund. None of that is payment processing, so none of it is in Stripe. So I looked at the platforms that do bundle licensing. Why the merchant-of-record platforms did not fit Paddle, Gumroad, and Lemon Squeezy all advertise license keys. I looked hard at each, and the same three problems came up. The fee. As merchants of record they charge around 5%, against Stripe's ~2.9%. On every sale, forever. Reasonable if it solved my problem well — but it did not. Offline validation. This was the dealbreaker. Their licensing is built around an online validation API: to check a key, the app calls the platform's server. My app is a desktop app, and desktop apps run on planes, behind firewalls, and offline. An online-only check leaves no good option. Fail closed — refuse to run without a server response — and a paying customer who is simply offline cannot use what they bought. Fail open — keep running when the server is unreachable — and the check is trivially bypassed: block the app's network access and it can never re-check the license or learn it was revoked. The app never actually verifies anything itself; it only knows what the server last told i

Factors like the short interval between the two powerful quakes and different types of soil led to some structures collapsing while others stayed standing.

Hey all, a bit of background - I'm an ex Amateur MMA fighter and BJJ brown belt and am also in the AI/ML space ... weird combo but wanted to know if anyone else was at the intersection of ML/AI and MMA/BJJ. In short, I'm building AI models that "watch" fights and are able to detect positions and moments throughout the fights - things like standing vs clinching vs ground (with intention of becoming more granular in time) along with detecting knockdowns, takedowns, etc. There's a timeline at the bottom of each fight with markers for different moments so you can jump straight to them. Anyway this is where my worlds collide and was curious for thoughts for anyone who wants to check it out. If you do, it's at https://cagesight.ai . All feedback welcome. Thanks all. submitted by /u/UnholyCathedral [link] [留言]

Hey ! I’m starting a series to document my work on GPU infrastructure, LLMs, and CV. Stop #1 is up: A brief look at why GPUs are the center of the industry, the CPU/GPU divide, and why nvidia-smi is the first place you check when things break. We’ll move past the basics quickly to focus on: Empirical architecture differences (Ampere vs. Hopper vs. Blackwell). Handling register pressure in custom kernels. Asynchronous memory paradigms (TMA/wgmma). #CUDA #GPU #KernelOptimization #SystemsProgramming submitted by /u/Positive_Canary1723 [link] [留言]

A Go interview is rarely about syntax alone. Yes, you should know how slices work, how goroutines...

Hello Devs, for months, I felt confident about system design interviews. I'd watched endless YouTube videos. I'd studied architecture diagrams. I could explain how Netflix builds recommendation systems. I understood Kafka, Redis, load balancers, and microservices. I'd memorized the designs of Twitter, Uber, YouTube, and TinyURL. Then I sat down for my first real system design interview and froze. The interviewer asked: "How would you design a notification system?" I had memorized notification systems. I knew about push notifications, email queues, delivery workers, and retry logic. I could recite architectural patterns. But suddenly, none of that helped. I didn't know which questions to ask first. I started designing before understanding the actual requirements. I built architecture for problems that didn't exist. I missed obvious bottlenecks. I couldn't articulate why I made specific trade-offs. When the interviewer pushed back, I had no framework to adjust. I failed that interview. But that failure taught me something crucial: System design interviews aren't about knowing technologies. They're about knowing how to think. After that, I went back and systematically practiced 20 system design problems. Not passively watching solutions. Actually designing. Making mistakes and refining my approach. And somewhere around problem 12, a pattern emerged. The best candidates didn't know more technologies than anyone else. They had a framework . They asked the same questions in the same order. They structured their thinking consistently. They could handle curveballs because their framework was flexible. They reasoned through trade-offs explicitly. Here's the framework that finally made it click for me. The Problem with Memorization Before I share the framework, let me explain why memorizing designs fails. When you memorize " How to Design Twitter," you learn: Use relational databases for users and tweets Use NoSQL for timelines Cache with Redis Use message queues for fanout S

It is like pytest but for statistical tests: it ensures no regression of your metrics at a statistical level. It manages tedious things such that seeds, past benchmark results, ... Simple CLI working like pytest but with benchmarks/ directory instead of tests/: pybench # 1st time: samples seeds, saves a baseline, marks NEW pybench # later: reruns on the same seeds, marks PASS / FAIL pybench update # re-baseline after an intended change pybench show # print current baseline stats (--history for per commit) Please give me your feedback, Github: https://github.com/AnthonyBeeblebrox/pybench Docs: https://pybench.readthedocs.io/en/latest/ submitted by /u/SpecificPark2594 [link] [留言]

This post is my submission for DEV Education Track: Build Apps with Google AI Studio . What I Built For this project, I built DevBrand AI, an AI-powered web application that helps developers create a complete personal branding kit in just a few clicks. Instead of manually writing bios, portfolio headlines, README introductions, or designing graphics, users simply provide their GitHub username, role, tech stack, experience, and preferred design theme. The application then generates everything automatically. Prompt Used I used Google AI Studio's Build apps with Gemini feature with a prompt similar to this: Build a modern React + TypeScript application called DevBrand AI that generates a complete developer branding kit. Use Gemini to generate professional bios, portfolio headlines, GitHub README introductions, project ideas, mission statements, social media introductions, CTAs, and branding recommendations. Use Imagen to generate a modern 3D developer mascot, hero illustration, and portfolio banner. Create a responsive UI using Tailwind CSS with reusable React components, loading animations, copy buttons, and download functionality. Features 🤖 AI-generated developer bio 🎯 Personal tagline 💻 Portfolio headline 📄 GitHub README introduction 💡 Project ideas 🌈 Suggested branding colors 📢 Social media introduction 🚀 Portfolio call-to-action 🎨 AI-generated developer mascot 🖼️ Hero illustration 🌐 Portfolio banner 📋 Copy buttons 📥 Download generated content 📱 Responsive modern interface Demo Screenshots Live Demo App: https://devbrand-ai-706459620449.asia-southeast1.run.app My Experience This project was my first time using the new Build apps with Gemini experience in Google AI Studio, and it was surprisingly fast to go from an idea to a working application. What impressed me most was how the AI generated a well-structured React + TypeScript project instead of just producing a single file. The generated components, services, and overall architecture made the project easy to und

I submitted one of my NeurIPS review ~6 hrs later than the official deadline. Will this still affect my own submission? Asking because I’m a first time reviewer. I pinged the AC a day before that I might be a few hours late, but didn’t hear back. So wondering if I might have triggered something that’ll now affect my own submission. submitted by /u/confirm-jannati [link] [留言]

I Built 9 AI Agents to Run a Gym. Here's the Architecture. The thesis that changed everything Most people think AI in business means: a chatbot → a dashboard → a few automated emails. I think it means: an entire organization runs on specialized AI agents, coordinated by a constitution, accountable to an independent auditor — with one human founder providing direction and warmth. Not a demo. Not a simulation. A real fitness studio in Dongguan Wanjiang, China. Real members. Real revenue. Running since April 2026. Here's the architecture. One Brain, Two Faces, Four Layers Let me start with the big picture, because the architecture is the strategy. ZWISERFIT = AI Operating System for Physical Businesses │ ├── 【Kernel】 9-Agent Enterprise OS (24×7 · full-stack autonomous) │ ├── 【Application Layer】 Saros & Melody │ Saros = Momo(Brain) + SaaS Stack → Digital Store Manager (B2B) │ Melody = Momo(Brain) × 3-Layer Metabolism → Personal Coach (B2C) │ ├── 【Data Layer】 KinTwin │ Hardware sensors + Nova behavioral streams + Ethan ZK proofs │ └── 【Protocol Layer】 Zeus Protocol Cross-domain agent communication + automated data transactions Fitness is the first vertical. Once the protocol runs, insurance, corporate health, and cross-industry data markets come online sequentially. The same architecture, different verticals. The 9 Agents: A Department Store for the AI-Native Company Each agent has domain expertise, a constitution (SOUL.md), identity (IDENTITY.md), memory (MEMORY.md), and cross-validation rules. They don't run on prompts. They run on governance. 🎯 Shuyu — Commander-in-Chief Orchestrates all 9 agents on the founder's behalf. Reads every agent report, coordinates across departments, makes daily strategic calls. The founder sets direction; Shuyu ensures execution 24×7. Role: COO + Chief of Staff, AI-native Output: Daily operational reports, cross-agent coordination logs Constitutional scope: Has authority over all agent scheduling but cannot modify the constitution 💰 Zeus —

Most developers treat SEO like that one dependency you know you need but keep putting off. You build a fast, clean site with solid architecture, then hand it off to a "marketing person" who asks you to add keyword-stuffed meta descriptions. Here's what changed in 2026: search engines place heavy emphasis on Core Web Vitals, which measure loading performance, interactivity, and visual stability of web pages. The technical foundation you're already building? That's 80% of modern SEO. Let me break down what actually matters when evaluating SEO services as a developer. The Technical Reality Check Technical SEO is the foundation that everything else sits on. On-page optimization and link building amplify a technically sound site. Applied to a technically broken site, they produce unpredictable, often disappointing results. If an SEO service can't speak your language about INP metrics, structured data, or mobile-first indexing, run. What Dev-Focused SEO Services Should Cover Core Web Vitals (Not Just PageSpeed Scores) Core Web Vitals (LCP, CLS, INP) are confirmed ranking factors — INP replaced FID in March 2024. Any SEO service still talking about First Input Delay is using outdated information. What to look for: Field data analysis from real users (not just lab tests) Specific fixes for Interaction to Next Paint Understanding of when to optimize vs. when to rebuild Crawlability and Rendering Google now clarifies that pages returning non-200 status codes (like 4xx or 5xx) may be excluded from the rendering queue entirely. If you're running a JavaScript-heavy framework, this matters. Red flag: SEO services that don't understand Server-Side Rendering (SSR) or Static Site Generation (SSG). Structured Data Implementation Structured data helps search engines understand what your content is about, not just what it says. In 2026, this matters for traditional search and AI search alike. Schema markup isn't just about rich snippets anymore. It's how AI systems like ChatGPT and Per

Why 40V Input and 500mA Output Matter in Noise-Sensitive Designs You’ve probably fought a power rail that looked clean on a multimeter but still trashed your 24‑bit ADC readings. The culprit is rarely the DC level—it’s the broadband noise, switching artifacts, and line‑frequency ripple that ride on top. In precision analog, RF, and sensor signal chains, even 50 µV of supply noise can bury a 1 mV sensor signal or degrade an RF PLL’s phase noise by 10 dB. The MAX20151R addresses this head‑on with a combination that’s hard to find in a single LDO: a 40 V input range, 500 mA output drive, and just 6.5 µV RMS output noise (10 Hz–100 kHz). That wide input headroom lets you power sensitive circuitry directly from a 12 V or 24 V industrial rail, an automotive battery, or a noisy intermediate bus without a pre‑regulator. You eliminate an entire buck converter stage, saving board space and avoiding the switching noise that would otherwise require heavy filtering. The 500 mA output current is equally important. Many ultra‑low‑noise LDOs top out at 200 mA or 300 mA, forcing you to split rails or add a discrete pass transistor. With 500 mA, the MAX20151R can comfortably supply a mixed‑signal chain—an MCU, a precision ADC, a low‑jitter clock, and a handful of op‑amps—from a single quiet rail. And because the device maintains its noise performance across the full load range, you don’t have to derate your noise budget as current increases. Field experience shows that transient events on 24 V vehicle buses can easily exceed 40 V during load dump. The MAX20151R’s 40 V absolute maximum input rating, combined with integrated reverse‑voltage protection down to –40 V, gives you a robust front end that survives those spikes without external clamping. This is a practical necessity for any design that must pass ISO 7637‑2 or similar automotive transients, and it’s a key reason engineers are migrating from lower‑voltage LDOs to the MAX20151R in harsh electrical environments. Key Takeaway: If

The problem: I kept missing big moves on Polymarket because I had no way to see what the biggest traders were betting on in real time. So I built WhaleTrack — a free, no-signup tool that shows you exactly what top Polymarket whales are buying and selling. What it does Live whale activity feed — see the last 40 trades from top wallets, updated on refresh Whale leaderboard — P&L, win rate, trade count for the biggest accounts No login, no ads, no fluff — just the data How it works The whole thing is vanilla HTML/CSS/JS deployed on Vercel with two serverless functions: /api/whales.js — hits the Polymarket leaderboard API, fetches position stats for each whale, calculates win rates from closed positions /api/activity.js — pulls recent trades for each whale wallet in parallel, filters out internal combo transactions (no title / zero price), and returns the 40 most recent trades The serverless layer solves CORS — Polymarket's data API doesn't allow browser requests, so everything goes server-side. Tech stack Frontend: Vanilla HTML/CSS/JS (zero dependencies) Backend: Vercel serverless functions Data: Polymarket public data API Deploy: Vercel (free tier) Biggest lesson Filtering bad data is half the work. The raw API returns combo trades and internal transactions that show up as "Unknown Market @ 0¢" — useless noise. Had to figure out which fields to check (title, price > 0) to strip them. Also: win rate calculation is tricky when most whales have unrealized profits. Showing "—" instead of 0% is more honest. Try it WhaleTrack → Also launched on Product Hunt today if you want to show some love: Product Hunt Built this in a weekend. Happy to answer questions about the Polymarket API or Vercel serverless setup.

Mesh came out of stealth in February with a $50 million Series A.