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The panelists explain the realities of running AI systems reliably at scale. While building models is solved, maintaining production databases under constant pressure is not. They discuss the emerging architectural decisions separating teams that scale gracefully from those facing catastrophic outages, and what engineering leaders must rethink today. By Simerus Mahesh, Alex Infanzon, Meryem Arik, Luca Bianchi, Renato Losio

Planning a Fourth of July getaway? Use less gas—and cut your emissions—by easing up on the pedal.

Papa Johns is spying on people’s buying activities to predict when they are low on food: The pizza chain recently tapped NBCUniversal, Instacart and the dentsu-owned media agency Carat for help reaching consumers when they’re low on groceries—and thus more likely to be swayed by a mouth-watering ad. The idea is to reach hungry consumers by “knowing what is in their fridge without being too creepy,” said Carrie Drinkwater, chief investment officer at Carat. To achieve that goal, NBCU and Instacart created a custom audience of shoppers who regularly purchase grocery staples on Instacart, such as eggs, milk, meat and produce. Based on that data, Papa Johns can determine which days of the week certain consumers are likely to run out of groceries and serve them an ad on NBCU streaming content accordingly. The brand served custom creatives to consumers based on their food preferences—such as whether they buy meat regularly—with QR codes and calls to action such as, “Light on groceries?” or “Empty fridge?”...

Talk to your to do list and get what's next Discussion | Link

Welcome to the “Temu experience of telehealth,” where everyone from Grindr to MAGA influencers can open a virtual clinic selling weight loss drugs and more.

https://www.404media.co/apple-hide-my-email-vulnerability-re... , https://archive.vn/mCbBw

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BMW's X5 lineup finally has an electric option with impressive range.

Fujifilm is expanding its QuickSnap lineup with a new disposable camera focused on taking monochrome photos and another built to survive harsh outdoor environments. The $22.90 QuickSnap Black and White and the $24.75 QuickSnap Active are expected to launch sometime later this fall, to the delight of Gen Z snappers driving the current resurgence in […]

A researcher found that using Anthropic’s Claude Opus 4.7, he could break into the website of Front Gate—used by every festival from Lollapalooza to Bonnaroo—and freely issue any ticket he chose.

Seriously, LeetCode in 2026? Everyone is saying HackerRank just killed LeetCode, and yet here I am,...

If you've submitted a BOM for quoting recently and gotten a lead time that made you do a double take, you're not imagining things. Passive component sourcing in 2026 is tighter than it's been in a few years — and MLCCs are the epicenter. I want to break down why this is happening, which component categories are actually at risk, and — more importantly — what you can do at the design stage to make your board less vulnerable to it. This isn't a "just wait it out" post; there are concrete layout and BOM decisions that meaningfully change your exposure. Why now? Three demand sources are converging on the same MLCC/inductor capacity that used to be dominated by consumer electronics: AI server infrastructure — GPU power delivery networks alone can chew through hundreds of decoupling capacitors per board, and hyperscaler order volumes dwarf typical consumer runs. EVs — automotive-grade passives (AEC-Q200, X8R/X7R) come from a narrower qualified supplier base, so even modest EV growth disproportionately tightens that segment. Renewables/grid infrastructure — pulling on high-voltage inductors and power resistors. On the supply side, new MLCC/ferrite production lines take 12–24 months to come online from the capital decision. Semiconductor fabs can reallocate capacity relatively fast; passive component fabs can't. That structural lag is the real reason lead times stretch out faster than they recover. Which parts are actually at risk Not everything is equally exposed: Category Normal LT 2026 Tight-Market LT Exposure Commercial MLCC (X7R, 0402/0603) 4–8 wks 8–16 wks Moderate–High High-density MLCC (0201, high µF) 6–10 wks 16–26 wks High Automotive MLCC (AEC-Q200, X8R) 10–14 wks 20–30+ wks Very High C0G/NP0 (precision/timing) 4–8 wks 6–12 wks Low–Moderate Power inductors (shielded, low DCR) 6–10 wks 12–20 wks Moderate–High Chip resistors 2–6 wks 4–8 wks Low Chip resistors are the least affected — manufacturing capacity is less concentrated and swapping vendors doesn't trigger a

I'm curious... What's the one feature that instantly makes you stop using a resume builder? For me, it was simple: You spend time creating your resume, everything looks great, and then the site asks you to pay just to download it. That experience inspired me to build Resumship, a resume builder where downloading your resume is completely free. Now I'm thinking about the next features to add, and I'd love to hear from the community. If you were building the ideal resume builder, what features would you include? AI-powered resume suggestions? Better ATS optimization? More templates? Portfolio integration? Cover letter generation? Something completely different? If you have a minute, I'd also love for you to try Resumship and share your honest feedback. 🌐 https://resumship.com Your feedback will directly influence what gets built next. Every suggestion, bug report, or feature request helps make the platform better for everyone. Looking forward to hearing your ideas! 🚀