标签：#t

Pool's new app automatically sorts screenshots into personalized collections, tracks down the original links behind saved content, and helps you rediscover products, recipes, travel ideas, and other things you meant to revisit.

Woot is hosting a small, but welcome deal on the Nintendo Switch 2 through June 19th. New customers can save $15 on the $449.99 console with the code NEW15 used at checkout. Sure, these are microscopic savings, but come September, the Switch 2 will rise in price to $499.99, so any deal is worth telling […]

The Weather Company announced an "enhanced allergy experience" now available through its The Weather Channel app designed to help allergy sufferers better understand when their symptoms might flare up, and what's causing them. While the app already provides static pollen counts, its "Health & Wellness" section is being expanded to take into account other factors […]

Alt Carbon said the agreement followed more than a year of scientific review and due diligence, with Microsoft requiring additional verification and data-sharing measures.

submitted by /u/Foreign-Swan4271 [link] [留言]

It feels like just a couple of years ago, using AI for everyday tasks still felt like something new or even a bit weird. Now it seems like a lot of people are using it without thinking twice, whether for writing, learning, brainstorming, or just quick answers. I’m curious how others see this shift. Do you think AI has become normalized quicker than most people predicted, or does it still feel like a big deal to a lot of users? submitted by /u/NoFilterGPT [link] [留言]

submitted by /u/aarshie [link] [留言]

I’ve been thinking about a support automation story I read recently. A team replaced a simple rules engine with an LLM classifier. The model was around 92% accurate. Sounds good. Until you realize that at 100 tickets a day, that’s roughly 8 mistakes every day. The interesting part wasn’t the accuracy though. It was what happened when the model was wrong. Nobody could explain why a ticket was classified a certain way. Nobody could point to a specific rule. Nobody could quickly fix the behavior. The team eventually started reviewing every classification manually. The automation was still running, but the trust was gone. That got me thinking. A lot of discussion around AI agents focuses on making decisions better. Better prompts. Better models. Better reasoning. But I rarely see people discussing what happens after the decision. How is the decision verified? How is it audited? How do you know an action should actually be executed? Maybe the biggest challenge for AI agents isn’t getting from 92% to 96%. Maybe it’s building systems that people can trust when things go wrong. Curious how others are thinking about this. submitted by /u/docybo [link] [留言]

Three years ago, when the women's World Cup kicked off in Australia and New Zealand, my social feeds were in a strange place. Twitter had just transformed into X, newcomer Threads was seemingly ascendant, and places like Bluesky had yet to garner much momentum. It left me with an odd, and admittedly silly, dilemma: I […]

The new chatbot, called Ask DoorDash, allows users to search the app for what they're looking for in their own words instead of having to scroll through restaurants and stores to build a cart.

Lyft details a new pickup experience to improve reliability in gated communities, where 25–30% of rides face routing and access challenges. The system uses mapping signals, boundary detection, and routing improvements to reduce cancellations and coordination overhead between riders and drivers, highlighting how real-world constraints drive evolution in geospatial systems. By Leela Kumili

submitted by /u/andix3 [link] [留言]

SpaceX alumni Andrew Redd is betting the ocean has vast amounts of untapped geothermal energy.

Brutal self-assessment paints a picture of a Microsoft gaming division in crisis.

Everyone talks about reasoning, agents, and larger models. But the more I learn about AI systems, the more I think we're missing something fundamental: AI doesn't accumulate experience the way humans do. A senior engineer isn't valuable only because of raw intelligence. They're valuable because years of experience have shaped how they think. They're valuable because they've spent years building mental models, learning from failures, recognizing patterns, updating beliefs, and connecting knowledge across thousands of experiences. That accumulated experience becomes a competitive advantage. Modern AI systems are different. They can solve difficult problems, write code, and explain complex concepts, yet most of what they "know" remains largely fixed after training. New information is often handled through context windows, retrieval systems, databases, or retraining pipelines rather than being integrated into a continuously evolving understanding of the world. This creates an interesting question: Can intelligence continue to scale if experience doesn't? Humans become more useful over time because experience compounds. An AI that could reliably learn from interactions, update its worldview, resolve contradictions, remember what matters, forget what doesn't, and improve without catastrophic forgetting might represent a larger leap than another increase in parameter count. Maybe the next frontier isn't making AI smarter. Maybe it's making AI capable of growth. Do you think future breakthroughs will come primarily from better reasoning models, or from systems that can continuously learn from experience? submitted by /u/Shreyansh_awasthi01 [link] [留言]

South Korean authorities issued the record-breaking fine following a data breach that affected over 30 million customers.

We posted here last week about infrastructure walls that show up when AI moves from personal use to team use. We had a few people described walls we hadn't named, which is more useful than the confirmations. Following up to collect more of those. If you've hit something that isn't on the list, or one of the six that looked different in your context, drop it here. What were you building and where did it break? The six walls for reference: Identity (who the AI is when it talks to your team), Decision Memory (whether past decisions inform future ones), Attention (how the system knows what to prioritise), Write-Back (whether AI outputs actually change the systems of record), Governance (who checks the AI's work), Economics (whether the cost structure holds at scale). Which one came first for your team? submitted by /u/Framework_Friday [link] [留言]

GitHub热门项目 | Fast Rust bundler for JavaScript/TypeScript with Rollup-compatible API. | Stars: 13,714 | 133 stars this week | 语言: Rust

GitHub热门项目 | Open source, composable payments platform | PCI compliant | SaaS and Self-host options | Enables connectivity to multiple payment, payout, fraud, vault and tokenization providers | Uplifts authorization with intelligent routing and revenue recovery | Reduce payment processing costs with cost observability | Reduces payment ops with reconciliation | Stars: 42,898 | 50 stars today | 语言: Rust

GitHub热门项目 | A Rust compiler front-end for IDEs | Stars: 16,534 | 10 stars today | 语言: Rust