标签：#ci

Bluesky's latest feature is group chats, arriving amid a shift in focus on building features for smaller communities.

A WIRED investigation found dozens of “nudified” deepfake images and videos on Grok's website, including nonconsensual depictions of celebrities and at least one prominent US politician.

From a wet winter in the Southwest to fewer Atlantic hurricanes, this is what to expect as a potential super El Niño takes shape.

"Some missions are using more than what their paperwork would say."

Reddit comments now allow videos.

Under a new bipartisan bill, Americans could sue for damages if a government official illegally tries to coerce a social media, AI, or broadcasting company to remove their post - regardless of whether the platform actually does it. Senate Commerce Committee Chair Ted Cruz (R-TX) and Sen. Ron Wyden (D-OR) introduced the JAWBONE Act on […]

Archaeologists found apparent scrape marks inside a skull; long bones may have been sharpened into tools.

Australia was the first country to issue a ban in late 2025, aiming to reduce the pressures and risks that young users may face on social media, including cyberbullying, social media addiction, and exposure to predators.

Elon Musk, on the verge of becoming the world's first trillionaire, is whipping up anti-immigration tensions amid ongoing riots in Belfast, Northern Ireland. Following a knife attack in the city on Monday, Musk declared support for Restore Britain, a hard-right populist political party that advocates for large-scale migrant deportation in the UK. He reposted statements […]

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 […]

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 […]

submitted by /u/andix3 [link] [留言]

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]

The spaceman wears Prada.

Alaska's multibillion-dollar fishing industry and vulnerable coastal communities at risk.

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]