Microsoft taps Alt Carbon in sign of India’s growing role in carbon removal
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.
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.
We learned some new details about the upcoming revival for the classic 3D platformer.
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.
submitted by /u/fagnerbrack [link] [留言]
One API for WhatsApp: messaging, calling, and AI agents Discussion | Link
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