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சுகத்தில் துடிக்கும் அக்கா மாமாவின் விரல் விளையாட்டு - 3 |🔞NSFW | 18+ only | Sensitive content ahead | #DesiSeduction# #IndianErotica# #BrownAndBold# #DesiFantasy# #KinkyDesi# #SareeSeduction# #EroticArt# #IndianBeauty# #BoldAndBeautiful# #exotica# #desibikini# #bikinidesi#

In 1990, the World Wide Web was invented on Steve Jobs' computer. Steve ignored it. This is the story I tell in my new book Steve Jobs in Exile. Here is what it should tell the rest of us about the moment we are in now. Steve was running NeXT, an unsuccessful computer company. He had been pushed out of Apple five years earlier and was burning his fortune trying to build a successor to the Macintosh. The machine NeXT sold was a matte-black magnesium cube -- expensive and beautiful and not selling. In October of that year, on the other side of the Atlantic, a British physicist named Tim Berners-Lee took delivery of a NeXT Cube at CERN, the physics laboratory on the Swiss-French border. He used it to invent the World Wide Web. The web ran on the Cube for its first year of existence. The revolution was happening on Steve's hardware, and yet Steve ignored it. Here is the question I keep thinking about from my book. If Steve Jobs, the most visionary tech mind of his generation, missed the Web, the most civilization-shaping tech of his lifetime, how are the rest of us supposed to see anything coming? Berners-Lee had been asking his boss at CERN for a NeXT Cube for months. His boss finally signed off, hoping to test the exotic Cube. "He suggested that I should buy one of these NeXT machines I'd been talking about so enthusiastically," Berners-Lee later told Fresh Air. "And if we needed a sort of test project to run on the NeXT machine ... 'Why not just do this hypertext thing you're talking about?'" The "test project" evolved into the World Wide Web. The problem Berners-Lee was trying to solve was not a glamorous one. CERN employed thousands of scientists from over a hundred countries, most cycling through on short assignments and taking their knowledge with them when they left. Berners-Lee was trying to keep institutional knowledge from walking out the door. He wanted a system that worked the way human memory does, where any piece of information could connect to any other without permission or central control. Through late 1990, he coded in his gray-floored office. The Cube's object-oriented system let him build in months what would have taken a year on anything else. By December, the first website went online. The World Wide Web now existed, running on a single black NeXT Cube in CERN's Building 31. Berners-Lee scrawled a warning on it in red ink: "This machine is a server. DO NOT POWER IT DOWN!!" Underneath the elegant interface he was building HTTP, HTML, and the server software that would deliver web pages. These three inventions would form much of the invisible plumbing of our modern connectivity. When a colleague of Berners-Lee's brought a demo of the Web to NeXT's headquarters in California, he could not get anyone there to pay attention. Nobody even dared show it to Steve, afraid he would dismiss it. NeXT was busy with its own internet plans, which Steve eventually killed. So back to the question. If Steve Jobs missed the web, how are the rest of us supposed to see whatever comes next? The honest answer is that we cannot. Nobody can. The rest of us are not going to outpattern-match Steve Jobs. But here is what I learned writing Steve Jobs in Exile. Transformations almost always begin in obscurity, on the margins, solving boring problems with boring tools. The web did not look revolutionary in 1990. It looked like a tool for sharing physics papers. We are in another such moment now. AI is the obvious changemaker. But the biggest transformations are rarely the obvious ones. The next one is happening somewhere right now, and it is trickier to spot than any sweeping proclamation about AI. We will recognize it, if we recognize it at all, from the unglamorous work few people are focused on. I will not speculate on what Steve would have made of AI today. But if he could miss the Web, the rest of us are going to have to look harder. Photo of the original CERN NeXT Cube courtesy of Robert Scoble.

Madonna's peaks on Billboard's Pop Airplay chart (chart began in October 1992) Erotica - #9# Deeper and Deeper - #2# Bad Girl - #26# Rain - #7# I'll Remember - #2# Secret - #3# Take A Bow - #1# Bedtime Story - #38# Human Nature - #30# You'll See - #7# You Must Love Me - #23# Don't Cry For Me Argentina - #7# Frozen - #4# Ray of Light - #13# The Power of Goodbye - #18# Nothing Really Matters - #25# Beautiful Stranger - #8# American Pie - #16# Music - #2# Don't Tell Me - #4# What It Feels Like For A Girl - #14# Die Another Day - #4# American Life - #27# Me Against The Music - #11# Hung Up - #17# 4 Minutes - #5# Give Me All Your Luvin' - #24# Girl Gone Wild - #38# Living For Love - #36# Popular - #14# Bring Your Love - #26# (still charting)

Congrats to the 25 teams graduated from @EASYResidency S3! Our incubation is strictly thesis-driven. We don’t follow trends; we try to define categories. In Season 2, we focused on the prediction market, leading to the successful integration of and into the Binance ecosystem. For Season 3, we moved deeper into Agentic Finance. We looked for: •Next-Gen Oracles: to verify complex off-chain data without trust assumptions—we found Cournot; •On-Chain FX & Stablecoins: to reach the underserved in frontier markets, like USD-i/Issac—a neo-bank for 2 billion Muslim people—and Orbswap; •Sovereign Privacy: we discovered essential infrastructure like 0xBow and SilentSwap; •Exotic RWAs: and now we are moving beyond treasuries into new frontiers with Openstocks, Renaiss, and Gemint; •We are so excited about those AI Infra projects and Applications: Newsliquid, Bank of AI, Brief Tech, and Taco AI, and Functor; •Proprietary AMMs & Everyday DeFi: Innovative models from Lunarbase, Nemesis, Dapital, and PokerFi. Every team is building sth interesting, meaningful, and unique. We are going to work on Payment/Stablecoin/FX for theme for S4, exciting details will be released shortly. Builders, stay tuned.

Bought a new Mac mini to properly tinker with claws over the weekend. The apple store person told me they are selling like hotcakes and everyone is confused :) I'm definitely a bit sus'd to run OpenClaw specifically - giving my private data/keys to 400K lines of vibe coded monster that is being actively attacked at scale is not very appealing at all. Already seeing reports of exposed instances, RCE vulnerabilities, supply chain poisoning, malicious or compromised skills in the registry, it feels like a complete wild west and a security nightmare. But I do love the concept and I think that just like LLM agents were a new layer on top of LLMs, Claws are now a new layer on top of LLM agents, taking the orchestration, scheduling, context, tool calls and a kind of persistence to a next level. Looking around, and given that the high level idea is clear, there are a lot of smaller Claws starting to pop out. For example, on a quick skim NanoClaw looks really interesting in that the core engine is ~4000 lines of code (fits into both my head and that of AI agents, so it feels manageable, auditable, flexible, etc.) and runs everything in containers by default. I also love their approach to configurability - it's not done via config files it's done via skills! For example, /add-telegram instructs your AI agent how to modify the actual code to integrate Telegram. I haven't come across this yet and it slightly blew my mind earlier today as a new, AI-enabled approach to preventing config mess and if-then-else monsters. Basically - the implied new meta is to write the most maximally forkable repo and then have skills that fork it into any desired more exotic configuration. Very cool. Anyway there are many others - e.g. nanobot, zeroclaw, ironclaw, picoclaw (lol @ prefixes). There are also cloud-hosted alternatives but tbh I don't love these because it feels much harder to tinker with. In particular, local setup allows easy connection to home automation gadgets on the local network. And I don't know, there is something aesthetically pleasing about there being a physical device 'possessed' by a little ghost of a personal digital house elf. Not 100% sure what my setup ends up looking like just yet but Claws are an awesome, exciting new layer of the AI stack.

⚠️AHORA: La autora de “Hinako Note”, Mitsuki, abrió un canal en YouTube donde habló del “lado oscuro” de su manga y explicó por qué decidió terminar la serie. Según contó, la obra no fue cancelada, sino que eligió concluirla porque deseaba dedicarse a dibujar historias más eróticas, algo que la revista original no permitía. #HinakoNote#

Hah judging by mentions overnight people seem to find the ghost analogy provocative. I swear I don't wake up just trying to come with new memes but to elaborate briefly why I thought it was a fun comparison: 1) It captures the idea that LLMs are purely digital artifacts that don't interact with the physical world (unlike animals, which are very embodied). 2) Ghosts are a kind of "echo" of the living, in this case a statistical distillation of humanity. 3) There is an air of mystery over both ghosts and LLMs, as in we don't fully understand what they are or how they work. 4) The process of training LLMs is a bit like summoning a ghost, i.e. a kind of elaborate computational ritual on a summoning platform of an exotic megastructure (GPU cluster). I've heard earlier references of LLM training as that of "summoning a demon" and it never sounded right because it implies and presupposes evil. Ghosts are a lot more neural entity just like LLMs, and may or may not be evil. For example, one of my favorite cartoons when I was a child was Casper the Friendly Ghost, clearly a friendly and wholesome entity. Same in Harry Potter, e.g. Nearly Headless Nick and such. 5) It is a nod to an earlier reference "ghost in the machine", in the context of Decartes' mind-body dualism, and of course later derived references, "Ghost in the shell" etc. As in the mind (ghost) that animates a body (machine). Probably a few other things in the embedding space. Among the ways the analogy isn't great is that while ghosts may or may not be evil, they are almost always spooky, which feels too unfair. But anyway, I like that while no analogy is perfect, they let you pull in structure laterally from one domain to another as as a way of generating entropy and reaching unique thoughts.

Teslas displaced my exotic cars many years ago, but with the Roadster still an indeterminate distance in the future, I had been feeling an urge for something a little more… analog. I was initially thinking about a 60’s Camaro with modernized running gear, but a part of me was also nostalgic for my old MGB. I eventually remembered that there was a car right at the intersection of muscle car grunt and British roadster charm – the ‘65 Shelby Cobra. It is kind of amazing that the federal Low Volume Motor Vehicle Manufacturers Act of 2015 authorizes small runs of replica vehicles to basically ignore all the safety and emissions regulations. All the paternalism and crusading was just pushed aside in the name of letting small companies make badass cars. On my birthday, Trista nudged me into visiting @EMotorcars where they had a dozen different replicas on hand. I was partial to the idea of a carbureted 427 with a magneto as a Mad Max post-EMP LARP, but practicality prevailed, and I wound up with a modern Coyote crate motor in a @Backdraftracing chassis.

Can AI truly understand concepts, or is it just processing data? 🤖 Join @FryRsquared as she discusses this and more with our Principal Scientist @mpshanahan. They break down the meaning of consciousness and explore how it might – or might not – apply to AI, as well as exploring the anthropomorphism of multimodal models. Watch our podcast now ↓ Timestamps: 00:00 Introduction 02:00 Ex Machina 03:28 John McCarthy & Coining "AI" 05:45 Symbolic AI 09:43 AI Reasoning 14:42 Turing Test 18:10 Alternative Tests 21:58 Anthropomorphism 26:42 Embodied AI 28:17 AI Consciousness 34:00 Octopus Analogy 37:48 Prompt Engineering 39:40 Exotic Mind-Like Entities 40:44 Conclusion