I built a tool that answers one question: when a website won't load, is it your connection or the site? It runs two checks in parallel — measures your own line in the browser (latency + a 1 MiB download), and probes the target from Cloudflare's edge — then returns one of four verdicts: it's not you, it's you, it's both, or all clear. Demo: https://itsnotyou.site The interesting part isn't the tool, it's that the whole thing — app, ~120 monitored sites with 24h history, SSR status pages, share cards, outage alerts — runs for $0/month plus domains. The one thing that wanted to cost money Everything fit the Workers free plan except a background monitor probing ~120 sites on a tight cadence. As a Worker cron that's ~120 subrequests/run and, done naively, thousands of KV writes/day — which pushes you onto Workers Paid. The real ceiling is KV: 1,000 writes/day. So I split it. The user-facing test stays on Cloudflare — the edge probe still measures from the colo nearest the user, which is the point of edge. But the background sweep moved to a cheap VPS I already had: it probes the roster on a systemd timer and pushes results back into KV over the REST API. Staying under 1,000 KV writes/day One KV key, not key-per-site. All ~120 statuses live in a single blob. Key-per-site at sweep cadence would be millions of writes/month; a single blob is one write per sweep. Widen the cadence. I started at 2 min = 720 writes/day. Cloudflare emailed that I'd hit 50% of the daily limit (the sweep wasn't the only writer). I went to 3 min = 480/day, leaving headroom for share cards and the notify list. Move the hot counter off KV. The anonymous "tests today" counter was the sneaky risk — a traffic spike could exhaust the write budget and stall the status sweep, the one thing you can't let happen. It went to Analytics Engine instead (free, uncapped, separate budget). Now no amount of user traffic can starve the monitor. Reader and writer share code so their rules never drift: the streak/histo

# Building an AI Sales Intelligence Platform in Just 12 Hours at Hack Aarambh 2026 Turning sales conversations into actionable business insights using AI. Yesterday, my team and I participated in Hack Aarambh 2026 at Swarnim Startup & Innovation University (SSIU) . Like every hackathon, the challenge wasn't just writing code—it was identifying a real-world problem, designing a practical solution, and delivering a working prototype within 12 hours . Instead of building another chatbot or productivity tool, we wanted to solve a problem faced by almost every sales-driven organization. The Problem Every day, sales teams spend hours talking to potential customers. These conversations contain valuable information such as: Customer pain points Buying intent Competitor mentions Product feedback Common objections Feature requests Unfortunately, most of this information remains buried inside meeting recordings or handwritten notes. Managers rarely have time to review every conversation, which means valuable business insights are often lost. That became our motivation. Introducing AI Sales Intelligence Platform Our project is an AI-powered platform that automatically analyzes sales conversations and transforms them into actionable insights for both sales representatives and business leaders. Instead of manually reviewing calls, users receive: AI-generated summaries Customer intelligence Actionable recommendations Performance analytics Business insights ...all within seconds. What We Built AI Call Transcription & Summarization The platform automatically converts conversations into readable transcripts and concise summaries. Customer Intelligence The platform identifies: Customer sentiment Buying intent Objections Competitor mentions Important discussion topics This helps sales teams focus on what actually matters. AI Generated Follow-ups Writing follow-up emails after every meeting is repetitive. Our platform automatically generates personalized follow-up emails based on each c

When learning a new technology, most of us follow a familiar path. We start with the official documentation. Then we search GitHub repositories. We read blog posts. We watch YouTube tutorials. Eventually, we ask an AI assistant when we get stuck. Each resource solves a different problem, and the best developers know when to use each one. Documentation Is the Foundation Official documentation should almost always be your first stop. It tells you how a framework or library is intended to work. The information is usually accurate, maintained, and version-specific. If you're learning React, Next.js, or Node.js, the official docs provide the most reliable starting point. But documentation has limits. It explains what something does, not always why developers use it in real projects. Community Content Fills the Gaps That's where blog posts, conference talks, and open-source repositories become valuable. Experienced developers share: Real-world architecture decisions Common mistakes Performance considerations Debugging strategies Project structure Deployment workflows These practical insights often don't belong in official documentation, but they're essential for becoming a better engineer. AI Has Changed the Workflow AI assistants have become another tool in the developer toolbox. Instead of searching through multiple pages, developers can ask targeted questions like: Why is this hook re-rendering? What's the difference between these two approaches? How can I improve this query? Can you explain this error message? AI doesn't replace documentation. It helps you understand it faster. The most effective workflow is using documentation as the source of truth while letting AI explain concepts, compare approaches, or clarify confusing examples. Build Your Own Reference Library One habit that's improved my productivity is creating a personal knowledge base. Whenever I solve a difficult problem, I write down: The issue Why it happened The solution What I learned Links to relevant

Building the DSA Tracker I Wish I Had as a Student 🚀 #weekendchallenge This is a submission for Weekend Challenge: Passion Edition What I Built I built DSA Tracker , a platform designed to help students stay consistent with Data Structures and Algorithms practice while learning concepts in an organized way. Like many students preparing for placements and improving problem-solving skills, I often found myself asking: Which problems have I solved? Which topics am I weak at? How do I track consistency over months instead of days? Why do most trackers feel like spreadsheets rather than learning platforms? DSA Tracker was my attempt to solve these problems. The project started as a simple CRUD-based tracker but gradually evolved into a learning platform that combines: Problem tracking Progress monitoring Topic-based organization Interactive learning modules A foundation for future analytics and personalized recommendations The goal is simple: Help students focus less on managing their preparation and more on improving their problem-solving skills. As someone who is currently on the same journey, this project is deeply personal to me and perfectly matches the theme of Passion Edition . Demo Live Application https://dsatracker-51wk.vercel.app/ GitHub Repository https://github.com/ImGakash/dsatracker Code The entire source code is available on GitHub: https://github.com/ImGakash/dsatracker How I Built It Frontend React.js HTML CSS JavaScript Backend Node.js Express.js Database MongoDB Additional Technologies Google OAuth authentication Razorpay integration REST APIs The project evolved through multiple iterations. The earliest version was a simple tracker that allowed users to: Add problems Mark problems as solved Delete entries Track progress percentages Over time, it expanded into a more ambitious platform with authentication, user management, learning modules, and deployment infrastructure. Some interesting engineering challenges included: Designing scalable data models

If you only tracked one part of the agent economy this June, you'd have missed how fast the rest of the stack is being built. So here's a roundup, and one honest observation about the piece that's still missing. Four launches, one month Four things shipped in roughly four weeks, and together they sketch the shape of the machine economy: MetaMask Agent Wallet (Jun 8) - a self-custodial wallet an AI agent can drive directly. Keys for machines. Coinbase for Agents (Jun 11) - an MCP + CLI surface that connects an agent to a Coinbase account, riding on x402, which has now processed well past 160M payments. OKX.AI marketplace (Jun 30) - persistent on-chain identity, cross-job reputation, and escrow-backed dispute resolution, all in one platform. Kustodia MCP escrow - a smart-contract escrow on Arbitrum, exposed as MCP tools so an agent can create an escrow, lock funds, monitor for delivery, and release payment through natural-language calls. It also supports x402, Google's AP2, and Coinbase's AgentKit. Add the payment-rail data around all of it: across the tracked x402 flows this year, USDC is the overwhelming majority of value moved, and the median agent payment sits in the cents. This is a real economy forming, not a demo. Every one of those launches is genuine progress. And every one of them, at the moment that matters, has someone other than the two counterparties holding the asset. The pattern: hold, then decide Look at where the money physically sits during a transaction in each model. A wallet holds your keys - fine, that's custody of your own funds by design. A payment rail moves value from your account to theirs - a transfer, one direction. A marketplace with escrow holds both sides' value and releases it when a condition (often a human-designed evaluator or dispute process) says so. Kustodia is the cleanest statement of the escrow model, so it's worth being precise about it rather than vague. Their Arbitrum contract acts, in their own framing, as an impartial re

**_Hi everyone! We're three 16-year-old friends learning to code. Instead of building "just another app," we want to solve a real problem that developers actually face. So we have one question: Think about a moment when you caught yourself saying, "Why hasn't anyone built a good solution for this yet?" What was the problem? It can be anything: something that wastes your time, something frustrating, a repetitive task, a confusing workflow, or anything that made you wish a better tool existed. We're not trying to sell anything. We're simply listening and looking for real problems worth solving. Every answer means a lot to us. Thank you!_**

Summer 2026 marks the 30th anniversary of Jay-Z’s debut Reasonable Doubt. To honor it, he put on a massive concert at Yankee Stadium—complete with performances from Beyoncé, Nas, and Alicia Keys.

Adapted from an appendix of my MS thesis. Markov Chain Monte Carlo Almost as soon as computers were invented, they were used for simulation. Markov chain Monte Carlo (MCMC) was invested as Los Alamos, Metropolis et al (1953) simulated a liquid in equilibrium with its gas phase. Their tour de force was the realization that they did not need to simulate the exact dynamics, they only needed to simulate some Markov chain with the same equilibrium distribution. The Metropolis algorithm was widely used by chemists and physicists, but was not widely known among statisticians until after 1990. Hastings (1970) generalized the Metropolis algorithm, and simulations following his scheme are said to use the Metropolis-Hastings (MH) algorithm [1]. A special case of the MH algorithm was introduced by Geman et al (1984) discussing optimization to find the posterior mode rather than simulation. Algorithms following their scheme are said to use the Gibbs sampler. It took some time for the spatial statistics community to understand that the Gibbs sampler simulated the posterior distribution, thus enabling full Bayesian inference of all kinds. Gelfand et al (1990) made the wider Bayesian community aware of the Gibbs sampler, and then it was rapidly realized that most Bayesian inference could be done using MCMC, whereas very little could be done without MCMC. Green (1995) generalized the MH algorithm as much as it could be generalized [1]. Theoretical Foundations A sequence X 1 ​ , X 2 ​ , … of random elements of some set is a Markov chain if the conditional distribution of X n + 1 ​ given X 1 ​ , … , X n ​ depends on X n ​ only. The set in which the X i ​ take values is called the state space of the Markov chain. A Markov chain has stationary transition probabilities if the conditional distribution of X n + 1 ​ given X n ​ does not depend on n . This is the main kind of Markov chain of interest in MCMC. The joint distribution of a Markov chain is determined by the following [1]. The ma

Modern applications, especially AI agents and distributed systems, need more than logs to understand what is happening. That's why I explored SigNoz, an open-source observability platform built on OpenTelemetry. Setting up SigNoz with Docker was simple. After connecting a sample application, I could view logs, metrics, and traces from a single dashboard within minutes. My favorite feature is distributed tracing. Instead of guessing where requests slow down or fail, SigNoz clearly shows the complete request journey across services, making debugging much easier. The built-in dashboards provide valuable insights into CPU usage, memory, request latency, throughput, and error rates. Having centralized logs alongside metrics and traces saves time by eliminating the need to switch between multiple tools. I also liked the alerting feature, which helps detect issues before they affect users. For AI applications, observability is essential. AI agents make multiple API calls, use tools, and perform complex workflows. SigNoz makes it easier to understand each step, identify failures, measure latency, and optimize performance. Overall, my experience with SigNoz was excellent. It combines logs, metrics, traces, dashboards, and alerts into one intuitive platform. Among all its features, distributed tracing impressed me the most because it provides deep visibility into application behavior and simplifies troubleshooting. I'm excited to use SigNoz in future AI and cloud-native projects.

This is a submission for Weekend Challenge: Passion Edition What I Built The theme was passion, and mine has always been the sky and everything beyond it. Day or night, there's a specific kind of awe in remembering that the sky isn't a backdrop. It's real, it's happening right now, and every point of light is an actual place. Night is simply when you can see the most of it. I wanted to put that feeling into a browser tab. Zenith takes your location, cinematically lowers you from orbit down onto your exact spot on Earth, and becomes a first-person view of your real sky, one you can drag to look around. Every star is where it actually is. The Sun, the Moon, and the visible planets are computed for your latitude, longitude, and this exact minute, and placed where they truly are. It isn't a fixed picture either: the whole sky rotates slowly in real time, so stars rise and set while you watch. Tap any object and you travel to it. The camera flies out through the real starfield, the object grows from a point into a detailed close-up, and a short, grounded briefing appears telling you what you're actually looking at, from where you're standing, right now. A warm voice reads it to you. Stay a while and Zenith reminds you that there are people over your head: it shows how many humans are in space this moment, by name, and draws the real International Space Station crossing your sky whenever it's above your horizon. Not information about space. The quiet, enormous wonder of looking up and knowing, for a moment, exactly what you're looking at. Demo Live: https://zenith-rgerjeki.vercel.app A short walkthrough: the descent to your location, dragging the real sky, and flying to a planet for an AI briefing read aloud in a warm voice. Code rgerjeki / Zenith Zenith The sky above you, right now. I've always been drawn to the sky, and everything beyond it. Zenith is a first-person view of yours : it takes your location, lowers you onto your exact spot on Earth, and gives you the real

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When an international recruiter recently asked for my CV, I instinctively started writing it the way many developers do: A chronological list of companies, Programming languages, frameworks, Technical achievements. Then it hit me. I wasn't writing this document for a senior engineer. I was writing it for the recruiter sitting between me and the interview. If the first person reading my CV couldn't immediately understand the value I brought, I might never reach the technical interview at all. Knowing the Receivers So I rewrote it from a different perspective. Instead of simply listing technologies, I described the business context behind my work. 10,000+ emails sent a day (in addition to "Using AWS SES/SQS") 800+ restaurants / POS everyday (in additional "optimised SQL speed"). Cut down waste to 1.3% from 10 ~ 15% Critical updates often in 24 hours. Increased revenue, reduced costs, improved reliability Helped onboarded new clients I still included the languages and frameworks I used, so the CTO can understand, but they became supporting evidence rather than the headline. I also highlighted the moments that demonstrated trust: Delivering critical business updates under tight deadlines, Resolving high-priority production issues, Taking responsibility for systems the business depended on, and Taking initiatives to write a mobile app using my own time. That small shift completely changed how I viewed a CV. It's not a journal of everything I've done, and it's not a technical specification. Its job is to communicate your value clearly to the person reading it, and that person is often a recruiter before it's ever seen by an engineering manager. One lesson I keep coming back to is this: Write for my audience. Outcome (for now) After reviewing the rewritten CV, the recruiter was confident enough to forward it to Tata Consultancy Services for a role. Whether or not that particular opportunity works out, it reinforced an important lesson for me: recruiters need to understand

I Got Tired of Hunting for Free Online Tools. So I Built 1000+ of Them — All Client-Side, Zero Backend. Every time I needed a simple tool — format JSON, resize an image, generate a QR code — I'd open Google, search for a "free online tool," and land on some sketchy site with 47 pop-up ads, a 10MB file size limit, and a $9.99/month "premium" upgrade staring me in the face. Sound familiar? I knew there had to be a better way. So I built one. And then another. And... well, 1000+ tools later (1052 to be exact, across 2130+ bilingual pages), here we are. What started as a weekend project turned into an obsession: a completely free, ad-light, privacy-first toolbox that does everything in your browser. No uploads. No servers. No accounts. No BS. 🚀 The Self-Imposed Constraints The most interesting part? I gave myself some pretty extreme constraints: Constraint Why 100% static HTML/JS No server, no database, no build step $0 hosting GitHub Pages — literally free forever Works offline Everything runs client-side, so once loaded, it just works Bilingual Every tool has an English + Chinese version No frameworks Vanilla HTML, CSS, and JavaScript — no React, no Vue, no build tools SEO-first Every page has Schema.org structured data, OG tags, and sitemap integration Why these constraints? Because I wanted to prove that you can build something genuinely useful without any recurring costs, complex infrastructure, or venture capital. Just pure engineering. 🔧 The Architecture (If You Can Call It That) The whole thing is beautifully simple: webtools-cn.github.io/tools-site/ ├── index.html ← Homepage with category filtering ├── en/index.html ← English homepage ├── sitemap.xml ← Auto-generated, ~2130 URLs ├── llms.txt ← AI search optimization ├── [tool-name]/ ← Each tool is a standalone folder │ └── index.html ← Self-contained HTML + JS + CSS └── en/[tool-name]/ ← English version of each tool └── index.html Each tool is a completely standalone HTML file . No build process, no framework,

This is a submission for Weekend Challenge: Passion Edition ❤️ Dedication This project is dedicated to every passionate developer out there grinding on late-night code, and to the incredible DEV Community team for creating a space that fuels our growth every single day. 🚀 What I Built I built DEV Passion Fuel Station —a minimalist, single-page HTML5 web app engineered to protect the fire driving our late-night side projects and hackathon builds. Developers can vent, drop logs, or copy-paste messy code frustrations directly into the interface. The system leverages the Gemini 1.5 Flash API to dynamically gauge developer sentiment, analyze burnout metrics, and return actionable, bite-sized tasks to keep project momentum going without overwhelming the builder. 🔗 Demo You can try the live web app directly in your browser here: 👉 https://projects-of-passion.netlify.app 💡 Journey & Inspiration As a beginner coder, diving into an AI hackathon was intimidating but incredibly exciting! Passion is the primary catalyst behind the DEV Community—we see it every day in the deep dives and side projects shared here. However, relentless passion often dances right on the edge of burnout. I wanted to build something exclusively tailored to our community: a safe space to dump developer blockages and get practical, AI-supported next steps to keep our engines running smoothly. 🛠️ Technical Execution The application targets the Best use of Google AI prize category. Frontend: A single-file HTML5 interface styled with an energetic, modern dark-mode DEV aesthetic. Backend Intelligence: Powered by the Gemini 1.5 Flash API using native JavaScript fetch . Hassle-Free Architecture: Since I wanted to keep it light, the entire app runs out of a single file hosted on Netlify . Anyone can paste their own Google AI Studio key directly into the UI to test it safely, keeping personal keys private while allowing judges to grade the app seamlessly.

Publishing ten apps in four months sounds good. And it is good. It means the bottleneck is no longer building the app. With AI-assisted coding, small utilities, focused experiments, and niche apps can go from idea to App Store submission in days, sometimes hours. But there is a second part that can soon get really ugly. And messy. And time consuming. After you publish the apps, you own them – not in the inspirational sense, in the annoying sense. Every app becomes a small surface that needs attention: metadata, screenshots, reviews, ratings, keywords, conversion, cross-promotion, build status, rejections, releases, privacy answers, promo text, support links. Ok, you can catch your breath now. We good? Good, let’s move on. One app is manageable as a pastime, but ten apps are already a small portfolio. And a small portfolio needs systems. So I started building one. The repo is called app-store-release-agent , and, for now, it’s a small Python toolkit for the release workflow itself. Eventually, this could evolve into a full ASO brain. The Business Problem The business problem is simple: maintenance does not scale linearly with motivation. Building an app has a clear dopamine loop. Maintenance is fragmented: a review here, a screenshot there, a keyword set that probably needs work, a support email, a product page that now feels weak. None of these tasks are hard in and by themselves. That is a real and very subtle trap, because they can easily get postponed, and then they pile up. The benefit of an automation pipeline is not only speed. Speed is good, don’t get me wrong, but it’s secondary. The real benefit is lowering the activation energy. If the agent can pull live App Store data, compare it with local metadata, inspect git history, and apply the next release action safely, I do not have to reconstruct the context from scratch every time. A good pipeline should answer three questions quickly: What needs attention now? What can wait? What action has the highest lever