I'm one of those people who enjoy on planes and in public spaces in general. But the issue of privacy was always a hurdle.

Uber is rolling out options and features that cater to fans watching FIFA World Cup matches.

uber blew through its entire 2026 AI budget by april, 4 months in. 95% of their engineers use AI, 70% of commits are AI driven, and their COO still said he cant draw a clear line between all that usage and actually shipping more useful features. microsoft and duolingo have pulled back too. at the same time theres a CEO survey going around (oliver wyman) where the share planning to cut junior roles jumped from 17% to 43% in a year, and only 27% said their AI ROI met expectations, down from 38%. what gets me is the combination. companies are trimming entry level headcount because AI can do junior tasks, but juniors are also how you grow seniors. if that pattern holds for a few years the mid and senior pipeline gets thin right when the current seniors age out. cutting the bottom rung while the ROI is still unproven seems like a weird bet. anyone seeing this play out where they work? sauce: https://finance.yahoo.com/sectors/technology/articles/ubers-coo-says-getting-harder-050841491.html submitted by /u/PROfil_Official [link] [留言]

Spoiler: not a single visible cable, not a single piece of furniture moved twice. When I started, I had an apartment and dimensions from the building blueprint. No designer. No clear idea where to go. But there was a desire to make something that would turn a standard apartment in a high-rise into a place of power — a place comfortable to live and work in. Instead of a designer, I took Claude. How it all began The first conversation wasn't about furniture or wallpaper. It was about direction . I didn't know what I wanted. I knew what I didn't want — kitsch, heavy classics, excessive decoration. We worked through options together. Scandinavian minimalism. Japanese wabi-sabi. Loft. Modern classic. The AI broke down each style by character, materials, color logic. Not "this would suit you," but "here's what this means, here's what this requires, here's what you'll get." In the end I arrived at Scandinavian for the bedroom . Warm, light, calm, with one deliberate accent behind the headboard. The living room–kitchen — loft with a red thread running through the whole space, because the furniture there was already concrete-grey with red niches and replacing it wasn't on the table. The hallway and corridor — neutral grey , as a transition between two characters. Three zones, three moods, one logic. The bedroom This was the most detailed conversation. A room with one window, one door, three free walls. Together we came up with: an accent wall behind the headboard with golden geometric lines, the other three walls in cream from the same collection. Tone on tone, different saturation, same texture. The seam between walls reads not as a boundary but as gradation. White matte furniture with black hardware. A wardrobe with a top cabinet almost to the ceiling. Mirrored doors reflect the accent wall — the golden lines are present even where they physically aren't. Then came the centimeters. The AI calculated . Adding up wardrobe depth, gaps, bed width, nightstands, dresser. Checkin

Curious if anyone here has used it for anything serious. Is it holding up for larger projects or is it still more of a playground? submitted by /u/krrish82 [link] [留言]

To get to $600, Apple and HP made different compromises. If you demand more power and performance, the HP Omnibook 3 delivers.

US Infectious diseases centers launched during COVID have lost their funding under Trump.

Consoles with disc drives are the easiest way to enjoy all kinds of physical media, but that could end with the next-gen PlayStation 6 and Microsoft's Project Helix.

Voice AI copilot for coworking & coliving operators Discussion | Link

Built like a tank, the endlessly capable GoRuck GR1 is as close to a do-it-all bag as you can get.

Welcome to May’s Interesting Links ! This month saw the Current conference in London with the usual 5k run , lots of familiar faces and friendly conversations—and plenty of excellent breakout sessions too. It seems live-tweeting conferences isn’t a thing any more, with only myself and Thomas Cooper seeming to post anything, but if you want you can go review the hashtag feed on BlueSky for some highlights of the conference. I got my first Hacker News front page hit with AI Slop is Killing Online Communities (51k views and climbing!), and a nice little halo boost for another rant from earlier this year, AI will fsck you up if you’re not on board . Oh, and I got involved in some thought leadering over on LinkedIn ( which a non-zero number of people thought was serious ) with my shitposting about fried breakfasts . {{< il-header >}} Kafka and Event Streaming 🔥 Apache Kafka 4.3.0 has been released. Check out the release announcement , as well as a video from Sandon Jacobs covering the new features. 🔥 After a few quiet months on his blog, Jack Vanlightly is back with a bang! He’s written a new tool, Dimster, a performance benchmarking tool for Apache Kafka , and has written several more blog posts off the back of it: Benchmarking Apache Kafka Consumer Groups vs Share Groups (overhead test) . Kafka Share Groups and Parallelizing Consumption Part 1: Tuning max.poll.records , Part 2: Producer Batches and share.acquire.mode . 🔥 I had the absolute pleasure to watch Victor Rentea present at Devoxx UK earlier this month. This guy redefines what it means to be an entertaining, energetic, enthusiastic—and educational presenter. Whilst his specific talk, "Event-Driven Architecture Pitfalls" isn’t online yet, you can find the slides here , and a recording from Devoxx last year of a similar talk. The Parallel Consumer library from Confluent has been marked as no longer maintained, prompting a discussion of alternatives (and the concept itself) on LinkedIn, as well as a fork from one

The Transition A few years ago, bug hunting was a manual craft. You scanned subdomains with one tool, tested endpoints with another, and stitched results together by hand. Today, AI changes the speed entirely. Not by replacing the hunter. By eliminating the boring parts. What AI Actually Changes 1. Reconnaissance at Scale Subdomain enumeration, port scanning, and technology fingerprinting used to take hours. AI-powered pipelines now do this in minutes: Passive reconnaissance via Certificate Transparency logs, search engines, and DNS records Automated crawling and endpoint discovery Technology stack detection from response headers and HTML patterns JavaScript file analysis for hidden endpoints and API keys The machine does the grunt work. The human interprets the results. 2. Pattern Recognition Vulnerability classes have signatures. SQL injection looks different from XSS, which looks different from SSRF. AI models trained on thousands of real vulnerabilities can flag suspicious patterns faster than manual code review. This is not about finding zero-days. It is about catching the low-hanging fruit that everyone else misses because they are in a hurry. 3. Intelligent Fuzzing Traditional fuzzers throw random data at endpoints and wait for crashes. AI-guided fuzzers understand the input format and generate test cases that explore edge cases a human would not think of. The result: fewer requests, better coverage, higher signal-to-noise ratio. Where AI Struggles Business Logic Flaws AI does not understand your application's purpose. It cannot tell if a discount code is applied twice, or if a user can access another user's private data through a convoluted API flow. These are the vulnerabilities that require context. Human context. Authentication Logic Authentication bypasses are often creative. They exploit the gap between what the developer intended and what the code actually enforces. AI can find simple auth flaws, but multi-step authentication bypass chains still need h

Someone asked us a sharp question on X this week. Tokenized stocks will drop dividends straight on-chain, so do we see any downsides? It's a fair question, and the honest answer is yes, one big one. The downside isn't the dividend itself. Instant, programmatic, no broker statement to wait for: that part is genuinely good. The downside is that you can't see it. On-chain dividends for tokenized equities are silent. They arrive without a transaction, without a notification, without anything landing in your wallet history. And a payment you never see is a payment you never declare. That's not a tracking annoyance. It's a tax problem, and it gets expensive. The dividend that never sent a transaction Backed Finance's xStocks (the Xs-prefixed mints like AAPLx, TSLAx, NVDAx) and Ondo Global Markets equities (the ondo-suffixed mints) both use the SPL Token-2022 ScaledUiAmount extension. It's an elegant piece of engineering. When the underlying stock pays a dividend, the issuer doesn't airdrop tokens to thousands of wallets. It updates a single number, a multiplier, on the mint account itself. The instant that multiplier changes, every wallet holding the token shows a larger balance. Your 10 shares are now worth the equivalent of 10 shares plus the reinvested dividend. No transfer hit your wallet. No transaction was signed. Nothing appeared in your activity feed. The number simply went up. Compare that with a traditional brokerage. When Apple pays a dividend, you get a line on a statement, an email, a figure on a 1099 or an annual tax summary. The paperwork chases you. On-chain, nothing chases you. The dividend is real, it's yours, and the only evidence it happened is a multiplier value buried in an on-chain mint account that almost nobody thinks to read. Why a number going up is a taxable event Here's the part that catches people. Dividend income is ordinary income. It's taxable in the year you receive it, at your marginal rate, in every jurisdiction we serve: Australia, the