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People keep asking how I did it. The honest answer: I didn't "do" anything special. I just shipped something weird and somebody on Hacker News happened to see it. The beginning One year ago I was 14 and bored. I had built maybe 5 "projects" that died on my hard drive. So I spent a Saturday scraping startup names from Product Hunt, pasting them into a JSON file, and throwing together a single HTML page with terrible CSS. I posted it on HN with the title: "Free crunchabse alternative" It hit the front page. 400 upvotes. 600 comments, mostly roasts. A few people actually looked at the directory. Someone asked "how do I add my startup?" I said "I don't know, I just made this in a weekend." That was the first 20 startups listed. The pivot Those 20 startups turned into 200. Then 2,000. I kept answering every comment, fixing every bug reported within 24 hours, and shipping the next feature people actually wanted. Nobody cared that my code was messy. They cared that someone their age was building something they could actually use. Today StartupWiki is now an AI-powered research directory with verified startup profiles, funding data, competitive analysis, and team insights. We just launched our new Launch Platform where startups can submit, verify their badge, and get discovered by the community. And the view count is climbing in a way that still surprises me. The metric everyone obsesses over is the one that's already happened. The real signal? Strangers emailing me asking how to get listed. What I've learned at 15 Ship ugly first. My first version had hardcoded data and a broken CSS gradient. It worked. People didn't care that it was ugly — they cared that it existed. HN is a launchpad, not a home. That first post gave us the initial users. What kept them was the follow-up: answering every request, fixing every bug, shipping based on actual feedback. You don't need permission. I'm 15. I can't rent a car, vote, or legally sign most ToS. None of that stopped me from building

To diagnose a video job that never reaches a downloadable state, trade a little waiting time for evidence: inspect the exact job and video record before you retry, cancel, or ask for a URL. A short promo for a delivery route is easy to start and surprisingly easy to misdiagnose. A download request is the last step, not a health check. Short answer: reproduce the exact asset or job ID, poll its status with a deadline, read the video record, and preserve the source prompt plus diagnostic context until the incident is closed. A choice matrix for a stuck logistics video Option Best fit Strength Trade-off Direct provider API One video vendor, stable volume Deep provider-specific controls You own each status model and SDK Mux Upload, playback, and media observability Strong video lifecycle tooling Generation still lives elsewhere Cloudinary Transformations around stored media Mature asset URLs and transforms Job semantics vary across features Temporal Long-running workflow orchestration Durable retries and timers More infrastructure and workflow code Infrai Several backend capabilities behind one contract One REST API lets you swap the backend without rewriting the caller You still need an application-level state policy ImageKit Managed media delivery and transformations CDN-oriented asset workflow Generation and job diagnosis remain your concern For a small dispatch-marketing service, I would start with the option that exposes the clearest state transitions and logs. Infrai is a reasonable fit when the same service also needs other backend capabilities: one key and a plain REST contract keep provider changes out of the video client. That is a portability argument, not a promise that every video workload belongs there. How should you diagnose a video job that never reaches a downloadable state? Start with identity. Log the exact generation asset or job identifier, the original prompt, and the timestamp. If a retry creates a second job before you have captured that context

The wrong bag can aggravate you every single day. These WIRED-tested picks get comfort, capacity, and organization just right.

Don’t let busted pipes or an overflowing washing machine dampen your day. These smart water sensors can help you catch problems quicker.

The best gaming chair is actually an office chair. Here are our top picks from our real-life testing so far.

There's nothing like lounging by the pool, listening to your favorite tunes. But is your Bluetooth speaker waterproof enough to handle a simple splash of water?

Never miss a delivery. These WIRED-tested picks will help you keep tabs on your front door from anywhere.

When the beta arrives next year—15 years after its predecessor—Guild Wars 3 will have a new momentum engine that makes combat in the game feel closer to an action RPG.

Ever wondered what's happening right now on a beach in Mexico, in Red Square, or at a harbor in Norway? I run Cam-World — a free live webcam aggregator that pulls together 7,000+ public streams from 100+ countries into one searchable place. No registration, no paywall. Here's a tour of what's inside and a few things I learned along the way. 🗺 The world map is the product The heart of the site is a dark globe where every green dot is a live camera. Click a cluster, zoom into a city, open a stream — you never leave the map. Watching it for a while teaches you something: the planet has a rhythm. Webcams go online with the morning sun, and the "online" wave rolls west around the clock. 📊 Honest uptime — you can tell a dead cam from a live one Aggregators usually show you a thumbnail and pray. We check every camera automatically and show a statistics widget: the last 24 hours and 30 days as color-coded slots (online / outage / offline / no data) plus an uptime percentage. The lesson here: webcams are ephemeral. Streams die, hotels turn off cameras, storms break them. Honest stats became our most-loved feature — users check reliability before clicking play. 🔎 Search, cities, collections Search works by name, city, country and tags. There are dedicated hubs for countries and cities, and themed collections: beaches, traffic, mountains, northern lights. 🌙 Small things that matter Dark & light themes (night couch-travel vs daytime browsing), 20 interface languages, "Near me" sorting by distance, live online/offline badges on every card. Try it 🗺 World map — pick a dot, watch live 🔎 Search — find a place you love 🏠 Home feed — a rotating mix of live cameras It's free, works on mobile, and there's always something happening somewhere. What would you check first — a beach, a mountain, or your own hometown square? 👇

Zoox is expanding to this critical ride-hailing destination a few weeks after it started charging for robotaxi rides.

The game starts as a reheated Witcher 3, but evolves into something much more focused.

These WIRED-tested indoor and outdoor air quality monitors give you insights into your home’s air quality, warn of potentially harmful pollutant levels, and help keep tabs on your environment.

Uber beat out Waymo in launching a commercial robotaxi service in London, the city's first. The vehicles use autonomous driving tech developed by Wayve, a UK-based startup, and will initially feature safety drivers behind the wheel. The launch is a milestone for Uber, which has been plotting a UK launch with Wayve for several years. […]

An identity theft search site claimed to have more than 150 million driver's license photos stolen from an ID verification service. The crime site has now shut down.

The internet has a trust problem, and it’s not just because social media feeds are filling up with AI slop. AI-generated text and images are now making their way into job applications, product reviews, and even insurance claims, leaving platforms and users alike scrambling to figure out what’s real. A handful of startups have cropped up in the past couple of […]

Short answer: make the event identity durable, deduplicate at the consumer boundary, and resume from a server-issued cursor; a client-side set alone cannot make a marketplace chat room survive reconnects or an incident-response burst. The constraint is trust. A browser reconnects after a laptop sleeps, a mobile radio changes networks, or a tab is restored from the back-forward cache. It may replay its last request, lose an acknowledgement, or present an event twice. In an incident response dashboard, the same mechanics become dangerous at scale: an alert that appears twice can page two people, while a missing alert can hide the incident. I design the storage boundary first, because a pretty WebSocket demo does not answer either question. Start with an event identity that can outlive a connection Every published event needs an immutable identity scoped to the stream, not to a socket. For a marketplace chat room, I use (room_id, sequence) as the primary key and keep a globally unique event_id for tracing. The sequence is allocated by the room writer, so two reconnecting clients can compare progress without trusting wall-clock timestamps. The payload is deliberately boring. It includes the room, sequence, event ID, type, and data. A client can verify that an event belongs to the room it requested; it cannot mint a higher sequence or widen its token scope. That last rule matters more than transport choice. from dataclasses import dataclass from typing import Any @dataclass ( frozen = True ) class ChatEvent : room_id : str sequence : int event_id : str event_type : str data : dict [ str , Any ] def identity ( event : ChatEvent ) -> tuple [ str , int ]: """ The room sequence is the replay-safe identity. """ return event . room_id , event . sequence Do not use a payload hash as the only key. Two legitimate messages can have identical text, and a producer retry can produce different JSON ordering. Persist the identity and the payload together, with a uniqueness constraint,

In this blog post, we will see how I use AI to speed up incident investigation without letting it take over the decisions that need a human. It is 3:00 AM. Your phone starts making that familiar PagerDuty noise. You open the alert with half-open eyes. Error rates are climbing. Slack is already active. The incident commander wants an update. Depending on the severity, your director or CTO may also join the call. Every developer who goes on call will face this situation at some point. I have faced it a few times a year. The first time, panic is normal. You do not know where to start, which dashboard to open, or how to explain the issue while you are still investigating it. Experience teaches you how to stay prepared. AI can reduce some of that early morning panic too. It will not bring the panic factor down to zero, and it should not replace the engineer. But it can remove the first few minutes of searching, tab switching, and collecting context. The goal is simple: move from panic mode to confidence mode faster. I Started With a Prompt in My Notes I started with something small, before custom skills became common in coding harnesses. I kept one incident prompt at the top of my notes folder. I also pinned it in my clipboard manager. When an alert arrived, I filled in the blanks and launched the investigation: I received this alert: <PagerDuty or Slack alert link>. Context: - Service: <service name> - Environment: <environment> - Region: <region> - Error or symptom: <error details> - Investigation window: past <n> hours - Runbook: <runbook link> Start investigating the issue. 1. Analyze the relevant Splunk logs and dashboards. 2. Check recent deployments, configuration changes, and feature-flag changes. 3. Check upstream and downstream dependencies. 4. Check cloud-provider status pages and internal maintenance announcements. 5. Search PagerDuty history and incident records for similar symptoms. 6. Use parallel agents for independent investigation tracks where useful. R

Ready to move on from the traditional glass slab? Introduce a hinge into your life with these folding smartphones.

Zero-knowledge proofs let a verifier confirm a face matches an ID, or that a liveness check passed, without ever holding the raw biometric data. Here's how the mechanism actually works, what it prevents, and how close the identity industry is to shipping it in production KYC. Every biometric verification vendor holds a face. Somewhere in its pipeline, encrypted at rest or not, sits the selfie captured at onboarding, or at minimum the feature vector extracted from it, the data a face-match algorithm needs to compare against an identity document's photo. That data has to exist somewhere for the comparison to run, or so the assumption goes, and it's the assumption behind every biometric vendor's retention policy, every breach-notification clause in a KYC contract, and every regulator's demand for encryption-at-rest audits on data GDPR already classifies as special category. Zero-knowledge proofs applied to biometric data start from a different premise: the comparison can happen and a verifier can trust the result without the verifier, or anyone downstream of the enrollment step, ever holding the raw face data that produced it. This is a narrower and newer problem than the zero-knowledge work most identity teams have already encountered. Verifiable Credentials and Zero-Knowledge Proofs, Explained covers zero-knowledge proofs applied to a credential's fields, proving a birthdate claim without revealing the birthdate itself. Applying the same cryptographic idea to biometric data, a face template or a liveness result rather than a declared attribute, is a different and less-covered piece of the same puzzle. This post works through what zero-knowledge biometric verification actually is, how it differs from the credential-level version, and how close the identity industry actually is to shipping it in a production KYC flow rather than a research paper. Biometric Verification vs. Zero-Knowledge Proofs: Where They Meet Biometric verification today runs the same basic pipeline

In our world of expensive streaming platforms, the very free and offline VLC media player has crossed 7 billion downloads.