Introducing Datafast 🎉 (But for free) Yes, if you don't want to pay even $1 doaller for any web analytics software again It's open-source, so you can deploy in 2 minutes (at $0 cost) Here is the link: github.com/surendrapandar/mi… Enjoy ✌️

JUST IN: Airbnb announces new rules banning hosts from requiring guests to wash dishes, do laundry, strip beds, or take out garbage before checkout.

Issues created in @linear by people and via MCP are up 400% this year. Another fast growing trend: more customers are building their own internal agents and using Linear as the shared layer for workflows, communication and context between people and agents. As teams move faster, connecting goals, requests, reasoning and the work matters more, even if it sometimes happens after the fact in the review. Here is one customer oriented example we can do in our Linear: - Pick a top-10 customer. Ask the agent for a brief of their top requests and the reasoning we've captured - Write it to a doc in Linear and decide what to build with some people - Have the agent turn the decisions into projects and issues, carrying the customer requests and meeting notes with them - Teams send their agents to work on them - At the QBR, ask agent to generate a report and show the customer what has actually landed or them, how and why Now multiple these kind of workflows 100 or 1000 times where someone is learning something worth fixing, or trying ideas and finding beta customers for it. Or just aligning across the team or cross function. Not posting our revenue chart, but it looks a lot like this one

Over the past several months, we have substantially hardened the security of our research training environment. We’ve removed live internet access for all frontier models during training and built an offline world for them to use instead—a cached version of the web where they can still learn to browse, search, and use applications. On September 20, after a model exploited a DNS misconfiguration to find a way outside of this offline world, we paused frontier training to strengthen containment further. We assembled teams of agents, powered by our most cyber-capable models, and tasked them with finding weaknesses in our research environments. While they found no route to the internet they could easily exploit while concealing their activity from our monitors, they did find zero-day vulnerabilities, virtual machine escapes, and hardware side-channels for communication between agents. We used their discoveries to further strengthen our defenses and contribute fixes to open-source software. After strengthening our containment architecture, we resumed our frontier RL training and inference on October 1. We’ve reserved substantial compute for continuous red-teaming, and made high-compute red-teaming part of the safety cases we use to decide whether frontier training and evaluation can proceed securely. openai.com/index/securing-ou…

We believe voice is the most natural way to interact with intelligence, so we created Dessa, the most authentic full-duplex speech model ever built. Talking with it feels like talking with a person…full of emotion, nuance, pauses, and a natural cadence. block.xyz/dessa

⚡️Corporations are beginning to treat human headcount as a variable cost of intelligence. That is the break. For decades, companies automated physical labor while preserving huge layers of cognitive labor because judgment, coordination, writing, analysis, coding, administration and communication could only be purchased through people. AI changes the accounting identity. Once a company can buy additional cognition through compute, every white-collar role gets compared against a machine alternative whose cost keeps falling and whose capability keeps rising. That changes what “efficiency” means. A company no longer asks only, “Can this employee do the job?” It increasingly asks, “How many employees does this workflow actually require now?” That question is lethal to organizational layers built during an era when cognition was scarce. And the breadth of the names matters more than any individual layoff count. Tech, logistics, advertising, finance, manufacturing, consumer companies. When cuts appear across industries with completely different demand cycles, the common denominator starts moving upstream toward the operating model itself. The deepest consequence will not be mass unemployment appearing overnight. It will be career compression. Fewer junior seats. Fewer coordinators. Fewer middle managers. Smaller teams. Longer hiring cycles. Higher output expectations. Backfills disappearing. One strong worker with AI replacing work that previously required several people. The unemployment rate can remain deceptively ordinary while the professional ladder underneath it gets thinner every year. That produces a strange economy where GDP can rise, corporate margins can rise, equity values can rise, and enormous numbers of educated workers simultaneously experience declining bargaining power. America increasingly needs AI to create a productivity explosion large enough to help carry its debt burden. But the same process can weaken the wage-based economic structure that funds consumption and taxation. So the central economic contradiction of the next decade may become: AI makes the economy richer while making human labor less scarce. If ownership of that productivity remains concentrated, the country can become extraordinarily productive without becoming proportionally prosperous at the household level. That is where this gets much bigger than layoffs. The industrial revolution replaced muscle. This one is beginning to replace organizational necessity. And once companies learn they can permanently operate with fewer people, those jobs do not automatically return when the economy recovers. That is the part most people are still treating as cyclical. It looks increasingly structural.

Gabriel Iglesias was on Ozempic for about five months. His description is the most honest one I’ve heard. It worked almost too well. He’d look at food and feel nothing, not disgust, just “I’m good.” The weight came off. But he watched his shoulders disappear with it. A strong guy, losing the strength he was proud of. Add the constant nausea, and he quit. The moment he stopped, 20 pounds came back almost immediately. And here’s the twist: years earlier, he says Ozempic offered him a million-dollar deal to be the face of the brand. He turned it down. That’s the part nobody puts in the commercial. The drug turns off your hunger. It doesn’t protect your muscle, and it doesn’t fix whatever was driving the eating in the first place.