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Some of the biggest names on Google's AI team got new jobs this week. In some cases, including for legendary Googler Jeff Dean, those jobs are no longer at Google. Given that Google's models seem to be behind the best of what's coming out of anthropic and OpenAI, is this a sign of Google in […]

The Birdfy Feeder Rookie is a good option if you’re new to birdwatching or simply don’t want to spend a lot on a smart feeder, and several configurations are on sale. The standard model is down to $59.99 ($60 off) at Amazon, which is close to its lowest price. It includes seven days of access […]

Starting today, you can take an additional $100 off your founder, investor, or attendee TechCrunch Disrupt 2026 pass, which is a nice bonus on top of our current discounted pricing.

Why Sports Data Is Harder Than Most People Think Building believable cross-era simulations turned out to be less about the engine and more about the data underneath it. Here is what we learned. MicroLeague Dev Blog, Vol. 3 By Eddie Solar When we started building MicroLeague Sports, I assumed the simulation engine would be the hard part. The vision was ambitious enough to justify that assumption. Let fans ask whether the 1996 Bulls beat the 2017 Warriors. Whether the 1985 Bears could slow down Patrick Mahomes. Which Cowboys team was actually the greatest. Teaching software to play those games across eras felt like the mountain. I was wrong about which mountain it was. The engine is hard, but it is a solvable, bounded kind of hard. The data underneath it is a different animal. Like most developers approaching this for the first time, we figured sports data was largely a collection exercise: gather historical teams, player stats, schedules, and box scores, feed it to the model, done. That assumption fell apart almost immediately, and the reason it fell apart is the subject of this article. Sports data is not a collection problem. It is an identity problem. Franchises do not stay the same thing. Players are not one entity. And the historical record does not agree with itself. The Real Problem Is Modeling Identity Over Time Volume 2 covered the era problem: statistics are confounded by the conditions that produced them, so a raw number pulled across decades lies to you. That is a normalization challenge, and it is real. But normalization assumes you already know what you are normalizing. Before you can compare the 1992 Cowboys to the 2023 Chiefs, your system has to have a confident answer to a more basic question: what exactly is a "team," and what exactly is a "player," when your dataset spans a hundred years? Those sound like trivial questions. They are not. They are the questions that ate most of our early engineering time, and getting them wrong quietly corrupts ever

The mayor of New York City has assembled a crew of Silicon Valley and United States Digital Service veterans to overhaul city services with better software.

You put together the concept — from a founder mixer, an after-hours panel, a themed party, a morning run, whatever fits your goal — and the TechCrunch team helps put it in front of the attendees already in town for Disrupt.

The use of AI systems to create viruses opens up new possibilities for combating bacterial resistance. It also raises concerns about the pace at which technology is outstripping regulation.

When I haul my 27-inch desktop monitor into the garage to film fun gadget videos… or want to play Japanese SNES games with friends… or lose the AC adapter for my old external hard disk… I can now power them with a USB battery instead. None of them came with USB-C ports; the entire USB […]

This article was produced in partnership with Type Investigations, with support from the Wayne Barrett Project. One morning in April 2025, employees of a small office in the US State Department got the email many of them had been dreading. For months, Elon Musk’s Department of Government Efficiency had been cutting a wide swath through…

If you use a Qi or MagSafe charger, it's important for your phone and the charging speed that you get the right kind of case.

TechCrunch Disrupt 2026 is built around one question: How do you build an enduring company in the AI era? Our programming and speaker lineup reflect that.

"Most people have completely forgotten how chaotic it really was."

Meta has been ordered to pay $567 million in the second phase of New Mexico's landmark child safety case, bringing total charges to nearly $1 billion for being a "public nuisance." In a ruling published on Thursday, the Santa Fe district court found that Meta's platforms are a "significant contributing cause" of a teen mental […]

The rise of instant payment networks has changed the way money moves. Transactions that once took hours—or even days—now settle in seconds. Whether it's FedNow, RTP, UPI, or other real-time payment systems, users expect payments to be fast, available 24/7, and completed almost instantly. For developers and fintech teams, however, speed creates a new challenge. When payments settle in real time, there's little opportunity to detect fraud, reverse errors, or manually review suspicious transactions. That makes instant payments risk management one of the most important aspects of building modern payment applications. Real-time payment systems leave only seconds to make fraud, compliance, and operational decisions before settlement becomes final. Why Instant Payments Change Everything Traditional payment systems often include a processing window where transactions can be reviewed before settlement. Instant payments remove that safety net. Once a payment is authorized and processed, the funds are typically transferred immediately. If a fraudulent transaction slips through, recovering the money becomes significantly more difficult. That's why payment platforms must shift from reactive fraud detection to proactive risk prevention. What Is Instant Payments Risk Management? Instant payments risk management is the combination of technologies, policies, and automated decision-making that helps businesses detect and reduce risks before an instant payment is completed. Instead of reviewing transactions after settlement, modern payment systems analyze risk while the payment is being processed. Typical risk management includes: Real-time fraud detection Identity verification Device and behavioral analysis Transaction monitoring Sanctions and compliance screening Velocity and limit controls Continuous risk scoring Every one of these checks must happen within milliseconds without creating noticeable delays for legitimate users. Why Traditional Fraud Rules Are No Longer Enough Older p

The congressman thinks it's time for a "Data Center Bill of Rights."

Russian attack? Explosive drone targeted parked aircraft at Leipzig airport.

Not everyone needs a keynote slot to make noise at TechCrunch Disrupt 2026. Sometimes the best way to meet investors, customers, and partners is by exhibiting directly on the Expo Hall floor at San Francisco’s Moscone West from October 13-15. That’s exactly what our Exhibit Program offers, and it’s still open to showcase your startup. Here’s what $12,500 buys you: Joining fellow exhibitors is the fastest, lowest-lift way for a […]

Starting today, you can take an additional $100 off your founder, investor, or attendee TechCrunch Disrupt 2026 pass, which is a nice bonus on top of our current discounted pricing.

Google is set to host its next live Made by Google hardware launch event on August 12th, and the company says in a new video that comedian Trevor Noah will be hosting the show. The video indicates that the event will feature other celebrities and influencers as well, including Call Her Daddy host Alex Cooper […]

Here is how I hardened the browser runtime for a Zero-Knowledge, Non-Custodial FinTech trading terminal. 👇 Client-Side Envelope Encryption: I derive a KEK from the user's password using PBKDF2-SHA256 (310,000 iterations). Then, a secure random 32-byte DEK (AES-256-GCM) encrypts the data. The password NEVER touches the server, and the DEK has a strict 15-min TTL in RAM before a wipe. Secure Enclave Anti-Export Guard: CryptoKeys are generated via crypto.subtle with {extractable: false} . To prevent injected malicious scripts from bypassing the sandbox, I implemented an isolated closure that overrides (monkey-patches) the native browser API: crypto.subtle.exportKey = async function(format, key) { if (isProtectedKey(key)) { _AuditChain.append('EXPORT_ATTEMPT', 'CRITICAL'); throw new Error('Export BLOCKED — unauthorized'); } return _origExport(format, key); }; If our database is breached, hackers find ZERO financial data. If the local session is compromised, runtime gating blocks extraction. Plus, client-side validation rejects API keys with withdrawal permissions enabled (zero custodial risk under MiCA, built for GDPR). The entire architecture runs client-side (WebSocket throttled at 100ms + local AI Advisor), keeping server costs near zero. Where does this runtime isolation logic fail? Why do major SaaS platforms still rely on standard local storage? Let's discuss. 💬