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The move comes after the company left potentially sensitive data from the initiative exposed internally.

Employees had previously raised concerns about the initiative, which involves collecting workers’ keystroke data to train AI models.

Spoiler: It's more Siri stuff.

What does an AI company do after one of those not-acqui-hire deals? Groq raised money, is leaning into its neocloud business, and is hiring new execs.

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. For those of you enjoying your summer unaware of Anthropic’s latest feud with the US government, here’s a recap: In April the company said it had built an AI model called Mythos…

Smart glasses are hitting the mainstream, so companies are now getting into the accessories game.

WhatsApp gets a new boss, as Will Cathcart moves to a new role at Meta, while Shah steps down as CEO of Indian fintech giant CRED to replace Cathcart.

Save up to $190 on your pass to TechCrunch Founder Summit 2026 by June 26, 11:59 p.m. PT. Designed for founders first on November 4 in Boston. Register here.

Sometimes designers have silly ideas that eventually grow on you. That happened to me with this concept where I had to build columns of items moving in opposite directions when a user scrolls the page. CodePen Embed Fallback Note: This … Using Scroll-Driven Animations for Opposing Scroll Directions originally handwritten and published with love on CSS-Tricks . You should really get the newsletter as well.

Big Tech is throwing big money into data center buildouts. As national opposition to the facilities grows, some workers are beginning to question whether it’s worth it.

Claude Opus 4.7 shipped last week, and the question any engineering team reaches for is how it compares to its peers. It is the strongest frontier coding model we tested on the baseline leaderboard, and it will be the easy default a lot of teams reach for. But in 2026, the model you reach for could matter less than the skill you load with it. That is what 880 evals across nine models (Opus 4.7, Opus 4.6, Sonnet 4.6, Haiku 4.5, gpt-5.4, gpt-5.3-codex, gpt-5-codex, and Cursor's Composer-2) tell us. Let’s take a step back. It’s now 2026, and agent skills are spreading like wildfire… (even our favourite movies are catching up to them). Watch on YouTube Every major agent ecosystem now has some version of them. So the question worth asking, whether you are a dev, a platform engineer, or an engineering leader, is which skills actually earn their context weight, and which ones just add cost. At Tessl, we believe context -particularly agent skills- and the broader concept of a context development lifecycle are where this space is heading (see also: Why the best AI coding teams will win on context ). The results below add to a growing body of signals pointing to a shift that is already underway. Top-line results Model Native behavior rate coverage (e.g "without skill") Adherence to skill ("with skill") Lift $/run (with skill) Avg time (with skill) claude-opus-4-7 80.5% 94.5% +14.0 $1.00 158.9s claude-opus-4-6 77.1% 93.8% +16.7 $0.53 126.6s claude-sonnet-4-6 75.6% 93.3% +17.7 $0.31 125.1s claude-haiku-4-5 61.2% 84.3% +23.1 $0.12 77.8s gpt-5.4 75.9% 92.7% +16.8 N/A* 135.4s gpt-5.3-codex 75.8% 91.9% +16.1 N/A* 87.9s gpt-5-codex 73.8% 85.1% +11.3 N/A* 136.2s cursor-composer-2 73.6% 90.5% +16.9 N/A* 152.0s We’ve evaluated 11 node.js development skills ( documentation, fastify-best-practices, init, linting-neostandard-eslint9, node-best-practices, nodejs-core, oauth, octocat, skill-optimizer, snipgrapher, typescript-magician ) , and aggregated “with vs without” skill performance. F

Mach's approach differs sharply from some of its peers.

A smart contract does not need an overflow, reentrancy bug, or broken access-control check to lose money. Sometimes, the exploit is hidden inside an ordinary division: uint256 result = amount * rate / SCALE; The expression looks harmless. It may even produce the expected answer in every unit test. But financial smart contracts operate with integer arithmetic. Fractions are discarded, rounding direction changes who receives value, and an error of one unit can be repeated across thousands of transactions. In a financial protocol, rounding is not merely a mathematical implementation detail. Rounding is a value-transfer policy. Every division should therefore answer three questions: Which direction does the calculation round? Which party benefits from that direction? Can the rounding advantage be repeated or amplified? This article examines the most dangerous precision problems in Solidity and the engineering patterns used to prevent them. Solidity Does Not Have Native Fixed-Point Arithmetic Most financial formulas use fractions: interest = principal × rate × time fee = amount × fee percentage shares = assets × total shares ÷ total assets collateral value = token amount × oracle price Solidity primarily performs these calculations with integers. For unsigned integers: uint256 result = 5 / 2; The result is: 2 The fractional component is discarded. For positive values, this behaves like rounding down: 2.5 → 2 This appears insignificant until the result represents: vault shares; debt; collateral; protocol fees; interest; rewards; liquidation bonuses; exchange rates; token prices. The lost fraction does not disappear economically. One party receives less value, while another party retains the remainder. Precision Loss Is Not Always Small Consider a protocol calculating a percentage: function calculateFee( uint256 amount, uint256 feeBps ) public pure returns (uint256) { return amount * feeBps / 10_000; } For a 0.3% fee: amount = 100 feeBps = 30 fee = 100 × 30 ÷ 10,000 fee =

Somewhere right now a guy at a bar is making a stranger sign an NDA on a napkin. For an app idea. Just sit with that. That napkin is going in a drawer. The drawer is a graveyard. Quick tour. Exhibit A: bro we should open a bar Two beers in, you're suddenly a hospitality mogul. Picking a name. Arguing about taco night. By the time the check comes, the bar is already dead. It died of "let's talk about this again soon," which never happens. Exhibit B: bro we should start a band A guitar shows up at a party. Someone says "we should actually start something." One rehearsal happens. In a garage. A neighbor complains. The band dies before it has a name or a single original song. RIP. Exhibit C: the app idea, may it rest in your Notes app The big one. Open your Notes app, it's a cemetery. "App that reminds you to text people back." Dead. "Tinder but for gym buddies." Dead. These ideas didn't fail. They never even got born. Why none of these make it out alive The ideas aren't even bad. Some are genuinely good. The problem is ideas are free and easy to say out loud. Building one makes it real, and real things can fail in public with your name on them. Saying "we should open a bar" costs nothing. Actually opening one costs $400,000, a liquor license, and every Saturday for five years. Guess which one people actually do. Building also got way easier, which makes this worse. You don't need a technical cofounder anymore. You can describe an app to a chat box and watch a prototype show up before your coffee's cold. The wall that used to stop people is mostly gone. People are still standing where it used to be, out of habit. A small ceremony for the ones we lost HERE LIES: "the app idea I had in the shower" born: tuesday died: tuesday, when I got out of the shower HERE LIES: "our band" born: one guitar, one party died: one noise complaint HERE LIES: "the bar we were gonna open" born: 1:47am died: 1:48am, checked the bill Two graves that still have a heartbeat Here's the part nobody

If you had a Roomba, especially in the early days of the robot vacuum, it was in many ways a fairly unsophisticated machine. It would just bump around your house, looking for something to suck up, until its battery died or its (way too small) tank filled up. Not that it mattered, though. You probably […]

Sure, anyone can use OpenAI’s chatbot. But with smart engineering, you can get way more interesting results.

I spent the last several months solo-building Tuneline , a cross-platform media player, from a single Flutter codebase that ships native apps to macOS, Windows, Linux, Android, Google TV, and iOS . No Electron. Here is the stack and a few things that bit me. The stack Flutter 3.38 / Dart 3.10 — one codebase, six targets. media_kit for playback — libmpv on desktop, ExoPlayer on Android. Avoiding per-platform video plugins was the single biggest sanity win. Riverpod for state, Hive for local storage, Dio for HTTP. Node.js + Prisma backend for the cloud-sync layer, so your library, favorites, and settings replicate across devices. GoRouter with a single-route, tab-driven shell so the same layout reflows from a phone to a 10-foot TV UI. Things that bit me TV is its own design language. A 10-foot, focus-based UI is not a big phone. D-pad focus traversal, larger hit targets, and a separate Google TV store listing were all non-trivial. Per-platform video quirks. Desktop (libmpv) and mobile (ExoPlayer) disagree on enough edge cases that a shared abstraction over media_kit earned its keep. Sync is a distributed-systems problem in disguise. "Set up once, never rebuild it" sounds simple until two devices edit the same data offline. Keeping one canonical decoder for both the socket sync-down and the REST pull saved me from a whole class of drift bugs. One codebase is not one design. Window management on desktop, picture-in-picture per platform, and safe-area handling on mobile each needed platform-specific care even with a shared core. The product Tuneline is a bring-your-own-content player, like VLC — you supply your own playlists and it does not host anything. Every viewing feature is free on one device, and the only paid tier is cloud sync plus multi-device. No subscriptions. Site: https://tuneline.app — happy to answer any Flutter or cross-platform questions in the comments.

Shinkei makes a refrigerator-sized robot called Poseidon to kill fish quickly and humanely.

Il venture capital con progetti a 4-5 anni incarna perfettamente la tensione tra la teoria di Manso e la filosofia di Taleb. È un orizzonte temporale che suona contro-intuitivo: troppo lungo per la logica del "fail fast" da incubatore, troppo corto per la pazienza della ricerca fondamentale. Eppure è proprio qui che si gioca la partita dell'innovazione dirompente. Il problema strutturale I fondi VC operano tipicamente su cicli di 10 anni. Un progetto a 4-5 anni occupa il cuore del fondo: non è un esperimento rapido da liquidare, ma nemmeno un investimento da tenere per un'intera generazione. Manso ci dice che il contratto ottimale per l'innovazione richiede tolleranza nel breve termine e ricompensa nel lungo. Ma cosa significa "breve" e "lungo" quando il progetto stesso dura 4-5 anni? Qui emerge un paradosso. Il VC tollerante — quello che Manso celebrerebbe — potrebbe essere tentato di mantenere vivo un progetto che sta fallendo, perché il fallimento prematurato distruggerebbe il valore dell'opzione. Ma Taleb ci avverte: l'antifragilità non è la persistenza a oltranza, è la capacità di trarre beneficio dallo stress. Un progetto che assorbe risorse per 5 anni senza generare informazioni utili non è antifragile: è semplicemente costoso. La soluzione di Manso: il contratto come orologio Per Manso, la risposta sta nella struttura contrattuale. Il contratto ottimale per un progetto a 4-5 anni non è lineare: non è un flusso costante di finanziamento legato a milestone arbitrarie. È qualcosa di più sofisticato. Il principale (il VC) deve commettere a un livello di finanziamento iniziale che copra la fase esplorativa — i primi 12-18 mesi — senza richiedere risultati misurabili. Questa è la fase di "tolleranza eccezionale per il fallimento" di cui parlava Holmström. Poi, a intervalli predeterminati, il VC ha l'opzione — non l'obbligo — di continuare. Ma la soglia di abbandono deve essere più bassa del livello ottimale ex-post. In altre parole: il VC deve essere disposto a co

Everyone optimizes the buying experience. Almost nobody builds for what happens after checkout. Every appliance, device, and tool you buy comes with records that matter later: the receipt, purchase date, model number, serial number, the manual, and the warranty terms. Most people have no system for keeping those together — they're scattered across email, a kitchen drawer, screenshots, and random cloud folders. So when something breaks, the warranty claim dies on a single question: "Can you send proof of purchase and the serial number?" That's the gap we're building SnapRegisters for. The simple version of the fix (works with any notes app) The day anything substantial arrives, capture four things: The product The receipt The model / serial label (it fades — grab it early) The warranty card or manual Organize them by product, not by document . Instead of "where's that receipt," it becomes "open the dishwasher record." When support asks for details, it's a 10-second lookup instead of a 20-minute hunt. Where AI actually helps after the purchase The interesting part for builders: the post-purchase layer is a great fit for AI. Point a camera at a receipt and you can extract the model, serial number, and purchase date, then track the warranty automatically — turning a tedious filing chore into a 5-second snap. It's not flashy AI, but it's the kind that quietly saves people money (most warranty coverage goes unused simply because the paperwork is gone). If you've ever eaten a repair bill for something that was technically still covered, you've felt this problem. Curious how other builders think about the "boring but valuable" software gaps like this one. 📲 SnapRegisters is free on iOS: https://apps.apple.com/app/id6757603213