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We wanted to show you what happens after the confetti falls. We checked in with some of our recent alumni, many of whom have sat down with us on Build Mode: The Founder Survival Guide, TechCrunch's podcast for founders at every stage.

It's Microsoft's least-weird attempt at a high-end mobile workstation.

submitted by /u/SwoopsFromAbove [link] [留言]

Nvidia's announcement that it's getting into the consumer laptop chip space with RTX Spark is huge. Apple has proved for years that Arm-based chips can perform incredibly well while also delivering great battery life - at least on the Mac. In the Windows world, performance hasn't fully matched up under Qualcomm chips, mostly in the […]

Anyone who has downloaded affected Red Hat packages should investigate immediately.

Meta says it's working on securing accounts that were affected by a hack.

California lawmakers are progressing a bill to preserve access to video games.

Meta's AI support chatbot helped hackers hijack Instagram accounts, as reported earlier by 404 Media. In a video shared on Telegram, a hacker shows how they could take over an account by asking Meta's chatbot to switch the email associated with someone else's profile and then reset the password. The issue, which Meta says has […]

Pat Casey and Josh Miller, the pair behind the Sonic the Hedgehog trilogy, are penning the script for the Streets of Rage movie.

Even Danish researchers think it's bizarre.

Some news: The Vergecast is now a daily podcast! Starting today, we'll be posting every weekday, with even more gadgets and rankings and conversations and feelings and podcasts-within-podcasts. We're excited for all the ways this new schedule lets us tell new kinds of stories, experiment with new tech and new formats, and involve you even […]

If you remember Zeus: Master of Olympus, you'll want to watch this game closely.

A SaaS team adds “Pay with crypto” to checkout. The first test looks fine: create a wallet address, show a QR code, receive USDT, mark the order as paid. Then production starts. One customer sends the right amount on the wrong network. Another pays after the invoice expires. A third sends 99.80 USDT instead of 100 USDT. Support sees a transaction hash but cannot find the order. Finance sees funds received but cannot match them to an invoice. The backend receives the same webhook twice and unlocks the product twice. That is the moment crypto payment integration stops being a QR-code feature and becomes a payment infrastructure problem. This is the first Dev.to post from Cryptoway . We build crypto payment infrastructure for online businesses, and here we will share practical notes about crypto payment API design, invoices, payment webhooks, stablecoin payments, checkout flows, reconciliation and payment status handling. What is a crypto payment gateway? A crypto payment gateway is the layer between a business event and a blockchain transaction. The business event can be: a SaaS subscription invoice; an e-commerce order; a digital product purchase; a marketplace deposit; a service payment link; an internal billing event. The blockchain transaction is the customer sending BTC, ETH, USDT, USDC or another supported digital asset. The gateway connects the two. It creates a payment request, shows the customer what to pay, monitors the blockchain, updates the payment status and notifies your backend when something changes. In other words: a crypto payment gateway is not the blockchain itself. It is the operational layer that makes blockchain-based payments usable inside real products. Crypto Payment Gateway vs Wallet Address A wallet address is enough for a manual payment. It is not enough for a product that needs order tracking, support visibility and finance reconciliation. Area Wallet address only Crypto payment gateway Order matching Manual matching by amount, address o

Most scraper demos lie by accident. They show the happy path: one URL, one clean page, one neat JSON object. Then the first real user tries a marketplace search page, a login wall, a JavaScript shell, a rate-limited product page, or a site that serves different HTML to every fetch path. The response still comes back as JSON, so everyone relaxes. That is the trap. A JSON response is not the same thing as a useful extraction. The failure mode agents hate AI agents do not just need scraped text. They need to know what happened. Bad extraction output looks like this: { "title" : "Example product" , "price" : "$29.99" , "availability" : "in stock" } That looks fine until you inspect the source and discover the page was a login prompt, a bot challenge, or a thin JavaScript shell. The extractor filled the schema because the schema was requested. Helpful. Like a smoke alarm that hums a little song while the kitchen burns. Better extraction output separates the data from the confidence and the failure class: { "status" : "failed" , "failure_type" : "login_required" , "confidence" : 0.94 , "extracted" : null , "evidence" : { "final_url_type" : "restricted_page" , "visible_content" : "login prompt" , "structured_data_found" : false }, "next_step" : "Use an authorised source, public item URL, feed, API, or sample HTML." } That is less flashy. It is also much more useful. The useful contract is not “scrape anything” “Scrape anything” is usually a warning label wearing lipstick. For agent workflows, the better contract is: Return structured data when the page provides enough evidence. Return a specific honest failure when it does not. Preserve enough metadata for the caller to decide what to do next. Never invent fields just because a prompt asked nicely. This matters for ecommerce, lead enrichment, price monitoring, competitor tracking, procurement, and internal research agents. If the agent cannot tell the difference between “product unavailable”, “page blocked”, “login require

PixToCode is a new Figma plugin that turns the frames you've already designed into production-ready code with AI — React, Angular, Vue, or HTML, all Tailwind-first. Just published on the Figma Community: figma.com/community/plugin/1641790551381890223/pixtocode What it does Select one or more frames in Figma, pick a framework, click Generate. About 10 seconds later you have clean code that uses the exact colors, spacing, typography, and layout from your file — not generic Tailwind utility soup. Highlights: 4 frameworks — React (TypeScript), Angular (standalone + Signals), Vue 3, or semantic HTML5. All Tailwind-first. UI library presets — shadcn/ui, Material UI, Chakra, Ant Design on React, Angular Material on Angular. Output uses the real components , not generic divs. Refine with plain English — type "make the button rounded" or "use green for the active tab" and the AI rewrites the component in place. Multi-frame batch — select up to 5 frames, get them all in one pass. Variants → typed props — a Figma Component Set with Primary / Secondary / Disabled becomes one typed prop-driven component, not three duplicate files. Live browser preview — see the generated component rendered in a sandboxed tab before pasting it into your project. Cloud history — every generation saved to your account, synced across devices. How it works Get a free license key at pixtocode.com (5 free generations, no credit card). Install the plugin from the Figma Community. Paste the key into the plugin's license field. Select a frame, choose a framework, click Generate. Copy the code straight into your project. That's the whole flow. Pricing Free — 5 generations on signup Pro — $20/month for 100 generations Power — $39/month for 250 generations Team — $99/month, 5 seats, 600 shared generations (scales to 10 seats) All paid plans have a 7-day refund guarantee. Tips for best results Frames with auto-layout , named layers , and consistent design tokens produce the cleanest output. For huge dashboard

Several users on social media reported having their Instagram accounts hacked over the weekend. Meta's own support chatbot was blamed for allowing hackers to hijack accounts.

Last week I wrote about why healthcare benefit data is still trapped in PDFs . The response told me something: people in this space know the problem is real. But extraction is only half the story. The harder question is: when an AI system pulls a copay amount from a carrier document, can you prove where that number came from? Not "the AI said so." Not a confidence score with no backing. Can you point to a specific page, a specific table cell, a specific paragraph in the source PDF and say: this value came from here? That is field-level provenance. And in healthcare, it is no longer optional. The Regulatory Floor Just Rose In January 2026, two state laws went into effect that changed the baseline for AI-generated content in healthcare. California SB 942 requires AI systems to disclose when content is AI-generated and to maintain audit trails. Texas HB 149 mandates transparency about AI decision-making processes in regulated industries, with healthcare squarely in scope. These are not theoretical. They are enforceable. And they are just the beginning. CMS transparency mandates tighten every year. Gartner declared digital provenance an enterprise baseline for 2026. The industry is not moving toward provenance. It has arrived. The Problem with Self-Reported Citations Most AI extraction systems today work like this: a language model reads a document, extracts values, and reports where it found them. The model does the extraction AND the citation. It is grading its own homework. This seems fine until you look closer. A language model that hallucinates a copay amount will also hallucinate the page number it came from. The citation and the extraction fail together, silently, in the same direction. In a coverage dispute that ends up in a regulatory proceeding, "the AI told us it found this on page 3" is not evidence. It is hearsay from a statistical model. What Deposition-Grade Provenance Looks Like Field-level provenance means every extracted value carries metadata from an

We recently hit a classic gradient boosting trap with our pricing engine (Flyback), and I wanted to share the ablation data. We run LightGBM quantile regression to forecast secondary market watch prices. We engineered a variant-conditioned Bayesian target encoder to isolate within-reference pricing dynamics. LightGBM absolutely loved it. It ranked #1 in feature importance at q90 by a wide margin, with gains several times the next-highest feature, across all our multi seed runs. But when we ran a strict 4-seed × 3-variant ablation on the hold-out set, the results inverted. Test MAPE regressed by +0.28pp and the between-variant delta was 7x the within-variant standard deviation. The encoder was finding effective splits that completely failed to generalize because the signal it was learning was driven by irreducible label variance: unobserved factors like condition nuance, seller behavior, and timing that no feature can capture. I wrote a full post breaking down the architecture, the ablation methodology, and the mechanism behind the divergence. Happy to discuss LightGBM split mechanics, target encoding leakage, or the ablation setup. Full post and ablation results: https://flyback.ai/engineering/target-encoding-divergence submitted by /u/Nj-yeti [link] [留言]

The company says it needs "significant" water resources to cool its data centers, and that access to abundant, affordable water is a challenge.

The Mall lets shoppers build a personalized feed of brands, track sales and drops, and discover products across thousands of retailers.