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This is a submission for the DEV Weekend Challenge: Passion Edition . What I Built Everyone will tell you about the passions they have. Nobody talks about the ones they quit. I played cricket every evening from age 11 to 17. I told everyone I'd play Ranji Trophy one day. Then the entrance exam years came, the bat went behind the cupboard, and I never went back. Eight years now. EMBER gives that abandoned passion a voice. You confess what you quit. AI forges its persona: the dusty object, the game itself, or the younger you. Then it talks back , out loud, in a voice matched to its temperament. It asks the question only it can ask: why did you really stop? Then it offers two doors: 🔥 Rekindle it. It negotiates the smallest possible first step ("Pick up your old bat and feel its weight. Sunday evening.") and you seal the pledge on-chain , where you can't quietly delete it. 🕯️ Lay it to rest. It says goodbye properly: a personal eulogy, spoken aloud, and a permanent on-chain stone. Closure is a feature, not a failure state. Every anonymized session joins the Atlas of Abandoned Passions , a live map of what humanity gives up, at what age, and what killed it. When I ran my own confession through it, the app decided my passion should speak as " Your old cricket kit bag ." Its first words: "It's been a while since you hoisted me up here, hasn't it? I still remember the thrill of a good cover drive, too." I built a thing and it emotionally wrecked me on the first test run. Working as intended. Demo 🔗 Live app: https://ember-himanshus-projects-acd54afd.vercel.app Try it in two clicks: tap an example confession (cricket at 17, the closet guitar, the novel at chapter three), headphones on. The voice is the point. A real pledge, sealed on Solana devnet: view the transaction . Code 🔗 Repo: https://github.com/himanshu748/ember How I Built It The loop is confess, converse, decide, commit, belong. Each stage is one sponsor technology doing what it is uniquely good at. Google AI (Gem

You ship all the time. A fix here, a new setting there, a feature you spent a whole weekend on. And your users mostly don't notice. That gap is expensive. When people can't see a product moving, it feels abandoned, even when you're shipping every week. They churn a little faster, they email asking for things you built a month ago, and all the momentum you're actually creating stays invisible. The fix is boring and old: a changelog. But not a changelog rotting in a Notion doc nobody opens. One that shows up inside your app , where users already are. Here's the approach I settled on. The idea: a widget, not just a page A "what's new" widget is a small button or badge in your UI. Click it, and a panel slides out with your latest updates. Users see it in the flow of using your product, not on some /changelog page they'd never visit. You really want three things: An in-app widget users actually see. A public page and RSS feed you can link from emails and docs. A way to write updates in plain language and publish in a click. The one-tag version I ended up building a tool for this (honest disclosure below), but the integration is the part worth showing, because it's the pattern any changelog widget should follow: <!-- Paste before </body> --> <script src= "https://cdn.patchlog.io/widget.js" data-project= "your-project-id" data-position= "bottom-right" async ></script> One script tag. No SDK, no npm install, no framework coupling. It behaves the same in React, Vue, Rails, or a plain HTML page. Two implementation details matter, whether you build one of these yourself or evaluate an existing one: Render it in a Shadow DOM. A changelog widget should not inherit or leak styles. If it uses the host page's global CSS, it will look broken on half the sites it lands on. Shadow DOM isolates it completely. Fail silently. A marketing widget must never break the host app. If the network call fails, it should quietly do nothing. What to actually write in it The tool is the easy part. T

Data centers are driving up the company’s use of electricity—and carbon pollution.

More police and firefighters use drones to catch and deter illegal fireworks.

The incident that taught me to respect pinning looked like nothing. A service freshly migrated to virtual threads, a load test that plateaued at about 420 requests per second no matter how much traffic we threw at it, CPU sitting at 9%, zero errors, zero warnings, nothing in the logs. The machine had 8 cores, and the one downstream HTTP call in the hot path took about 19 ms. Do the arithmetic: 8 × (1000 / 19) ≈ 421. The service that was supposed to scale to millions of virtual threads was serving exactly one request per CPU core. Loom had quietly handed us back a bounded thread pool, and the code looked perfectly innocent. That failure mode has a name — pinning — and this is the field guide I wish I'd had that night: what it is, the two (and only two) things that cause it, what JDK 24 changed, and how to catch it before your throughput graph does. What pinning actually is A virtual thread doesn't own an OS thread. It runs on a small pool of platform threads called carrier threads — concretely, the workers of a dedicated ForkJoinPool living in a thread group named CarrierThreads , with default parallelism equal to Runtime.availableProcessors() . When a virtual thread blocks — on I/O, a lock, a queue — it normally unmounts : it saves its stack, steps off the carrier, and frees that carrier to run another virtual thread. That unmount is the entire trick that lets a handful of OS threads serve millions of virtual ones. Pinning is when the unmount can't happen. The virtual thread blocks but stays mounted, and its carrier sits there doing nothing useful for the whole duration. One pinned carrier is a rounding error. But the default carrier pool is only as big as your core count, so if a hot path pins routinely, you pin every carrier at once — and then no virtual thread anywhere makes progress. That's not a slowdown; it's scheduler starvation, and from the outside it looks a lot like a deadlock. You can raise the ceiling with -Djdk.virtualThreadScheduler.parallelism=N , bu

There's a moment in every Solana project where the work stops being about whether the program works and starts being about whether it's ready . You've tested it, the logic holds, the constraints are tight. Then you point it at mainnet, and a different set of questions shows up: questions about money, permanence, and strangers. This post is about that transition. Not the commands, which are short and well documented, but the shift in what you're responsible for once real users can touch your code. If you've been building on devnet and you're starting to think about a live launch, this is the mental model to carry in. Devnet was a sandbox. Mainnet is not. Devnet is a practice field. The SOL is free, you airdrop more whenever you run low, and if you deploy something broken, the only casualty is your afternoon. That safety is the whole point of devnet: it lets you fail cheaply and often, which is exactly how you should be learning. Mainnet removes the safety net, and three things change the moment you cross over. The SOL is real. Deploying a program allocates an on-chain account sized to your compiled binary, and you pay rent for that space in actual SOL. Larger programs cost more. This isn't a huge sum for a typical program, but it's real money leaving a real wallet, and that alone tends to sharpen how carefully you check things before you hit deploy. The audience is real. On devnet the only person calling your program is you. On mainnet, anyone can find your program and send it any transaction they like, the moment it's live. Everything from the security arc stops being theoretical: the accounts strangers pass in, the inputs you didn't expect, the edge cases you hoped no one would hit. Mainnet is where "every account is attacker-controlled until proven otherwise" becomes a live condition rather than a lesson. The mistakes are visible. A bad devnet deploy disappears into the noise. A bad mainnet deploy is a public event, on a permanent ledger, in front of the users you

What I built I wanted a workout timer that doesn't just beep at me. So this weekend I built one that writes the workout AND talks me through it, out loud, in a voice that actually sounds like it's yelling at you when things get hard. You give it a callsign, how long you've got, what you want to work, and how brutal you want it. It hands that to Gemini, which breaks the whole thing into 30-90 second intervals with a coaching line for each one. Then every one of those lines gets turned into real audio by ElevenLabs before it ever hits your browser. Nothing is pre-recorded, nothing is a fixed track. Ask for a different workout, get a completely different script and a completely different set of audio clips, generated on the spot. Demo Unedited screen recording, straight off my machine hitting the real APIs, sound included. Compose a routine, it comes back in a couple seconds, pacing curve draws itself as an SVG line, then hitting Start walks through each interval with the active one highlighted in red as it counts down and you actually hear it. The Maximum-intensity segments sound noticeably more unhinged because I turn the ElevenLabs stability knob way down for those specifically. Code https://github.com/marwankous/sonic-kinetic How I built it Go backend, one endpoint. It takes your workout params, sends a prompt to gemini-3.1-flash-lite with a JSON schema locked down tight enough that I don't have to think about parsing garbage back out of it, and gets back a full timeline plus a heart-rate pacing curve. The part I actually enjoyed was the audio pipeline. Every coaching line in the timeline gets fired off to ElevenLabs at the same time, one goroutine each behind a sync.WaitGroup , so a routine with a dozen segments doesn't take a dozen times longer than one with a single segment. Whatever comes back gets base64'd straight onto its segment. I also tie the eleven_flash_v2_5 stability setting to the segment's energy level, dropping it to 0.30 for anything marked Maximum

Six years in prison for man who "sold out the very victims he was hired to represent."

Advocates are demanding that the Department of Homeland Security release bodycam footage of the fatal shooting of Lorenzo Salgado Araujo, a Mexican immigrant who was killed by ICE officers in Houston during a traffic stop earlier this week. But DHS claims the agents involved in the shooting weren't wearing body cameras because of the lengthy […]

Solid-state batteries are safer and more capable—but harder to mass-produce. They also represent an opportunity for non-Chinese companies to get back in the game.

Note: Google Cloud credits are provided for this project. In this blog post, I want to demonstrate how I use Antigravity CLI to build an image analysis demo using Angular, Firebase Hybrid & On-device Inference Web SDK, and Gemini models. Users upload an image and use a Gemini model to analyze it to generate a few alternative texts, tags, recommendations, and CSS tips to enhance the image quality. When the demo is running on Chrome 148+, the Hybrid & On-device SDK leverages the Prompt API of the on-device Gemini Nano model to perform the image-to-text tasks, and the token usage is 0. When other browsers such as Safari or Firefox executes the same tasks on the demo, the SDK falls back to Cloud AI (Gemini 3.5 Flash model), and the token usage is greater than 0. Next, I will describe how I installed the skills in my Angular project, and registered the Stitch MCP server in the Antigravity CLI to develop the infrastructure, services, and UI design of my demo. 1. Skills I installed grill-with-docs , angular , and firebase skills in my project for the following reasons: grill-with-docs: Conduct a rigid Q&A session to generate a specification for a feature, refactor or a critical fix. AI is responsible for performing a thorough analysis and putting in more effort to generate code to achieve the task. Angular: Provide the best practices of Modern Angular architecture, such as using signals and signal forms. Firebase: Provide the skill for Firebase AI Logic, Firebase Remote, etc. Resources Firebase Hybrid & On-device Image Analysis App Firebase Hybrid & On-device Inference Chrome Built-in Prompt API Stitch Stitch MCP Server grill-with-docs Angular skill Firebase skill

Yes, Sarah Wynn-Williams violated her deal to stay silent. But the power disparity bolsters the view that Meta is a heartless bully.

A third ransomware negotiator has been jailed for helping a notorious ransomware group extort American victim companies into paying the hackers.

Bryan Oliver discusses the frontier of AI infrastructure: chaos engineering for large-scale GPU clusters. He shares how engineering leaders can handle complex topologies, network protocols like RDMA, and NUMA misalignments. Discover seven practical fault-injection strategies to maximize multi-million dollar hardware efficiency and build robust observability loops. By Bryan Oliver

It follows reports of 100,000 jobs being under threat at the German automaker.

I love it when a company challenges itself to make a cheaper version of a beloved product. Xreal's $299 A01 Plus is a stripped-down version of its $449 1S that's light on features but with just enough of the 1S' best qualities. These AR glasses are comfortable, they look good, and the screens are surprisingly […]

Hello Dev Community! 👋 It is officially Day 128 of my software engineering marathon! Today, I tackled an essential lifecycle design challenge in modern frontend development: managing persistent browser loops, orchestrating ticking background workers, and mastering Timer Cleanups inside the useEffect Hook ! ⚛️⏱️💻 I put these architectural paradigms into action by engineering a lightweight, responsive Real-Time Clock Application that tracks exact server-client time down to the second without triggering rogue background processor spikes! 🛠️ Deconstructing the Day 128 Asynchronous Scheduler As captured across my clean system workspace configurations in "Screenshot (286).png" and "Screenshot (287).png" , the scheduling mechanism enforces strict resource allocation: 1. Initializing Reactive Temporal State Managed our standard state anchor using native JavaScript runtime Date models to trigger instant re-renders upon completion of each interval cycle: javascript const [time, setTime] = useState(new Date());useEffect(() => { let intervalId = setInterval(() => { setTime(new Date()); }, 1000);

An interactive guide to the architecture behind modern language models. Instead of predicting the next word, this Transformer predicts the next move in a game of fading Tic-Tac-Toe—making every step of the model easy to visualize and understand. Play the game, inspect every matrix multiplication, and watch tokens flow through the network in real time. What's covered Tokenization and embeddings Learned positional encoding Self-attention (Q, K, V) Multi-head attention Causal masking and softmax Residual connections and layer normalization MLP (feed-forward network) Unembedding and sampling Model ablations (no positional encoding, no causal mask, no MLP, no residual stream) Includes interactive visualizations for every stage of the Transformer pipeline - from input tokens to the final prediction. https://sbondaryev.dev/articles/transformer

Whether it’s through Google Maps or Emergency SOS, there are plenty of ways to quickly let your loved ones know where you are.

"I have been waiting a long time to finally get up there..."