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Elon Musk thinks the entire education system is built on a broken assumption. That every student should learn the same thing. At the same speed. In the same order. At the same time. Musk: “Everyone goes through from like 5th grade to 6th grade to 7th grade like it’s an assembly line. But people are not objects on an assembly line.” The model was designed for a factory economy. Standardized inputs. Predictable outputs. That economy is gone. The assembly line is gone. But the education system still runs on its logic. A student who masters algebra in two weeks sits through eight more weeks because the calendar says so. A student who struggles gets dragged forward because the schedule doesn’t wait. Neither is being served. Both are being processed. Musk: “Allow people to progress at the fastest pace that they can or are interested in, in each subject.” AI doesn’t teach a classroom. It teaches a student. One at a time. Every time. It skips what a student already knows. It finds where they’re stuck and approaches it from a different angle. It adjusts in real time. Not at the end of a semester when the damage is already done. A student obsessed with basketball learns fractions through shooting percentages. A student who builds in Minecraft learns geometry through architecture. The subject doesn’t change. The entry point does. No teacher with thirty students can do this. Not because they lack skill. Because the math doesn’t work. AI doesn’t have that constraint. Musk: “You do not need to tell your kid to play video games. They will play video games on autopilot all day. So if you can make it interactive and engaging, then you can make education far more compelling.” The brain isn’t broken. The format is. Kids learn complex systems and strategic thinking for hours voluntarily. Then walk into a classroom and can’t focus for twenty minutes. That’s not a discipline problem. That’s a design problem. Musk: “A university education is often unnecessary. You probably learn the vast majority of what you’re going to learn there in the first two years. And most of it is from your classmates.” Four years. Six figures of debt. And the real value comes from the people sitting next to you. Not the institution charging you. The degree doesn’t certify knowledge. It certifies endurance. Musk: “If the goal is to start a company, I would say no point in finishing college.” The system was built to train employees. If you’re not trying to be one, it has nothing left to offer you. Every lecture. Every textbook. Every curriculum. Now available instantly. Personalized to any learner. Adapted to any pace. The question isn’t whether the old model survives. It’s how long we keep forcing students through it while the replacement already exists.

Everyone’s spinning up a new L1 these days. Most feel like slight variations on the same theme. What stands out about @NomismaNetwork is the bigger picture they’re actually building a system where AI agents, economic coordination, and real value creation aren’t siloed experiments, but native parts of the same environment. Not just another chain trying to be faster or cheaper. More like infrastructure designed for AI native economies and useful DeFi coordination in one place. Still very early, but the direction feels genuinely forward looking instead of hype driven. Worth watching.

Stop guessing which models fit in your VRAM! llmfit is a CLI tool that auto-detects your hardware and ranks 206 models by what actually runs on your system. You download a 70B model and hope it fits. Or you estimate memory requirements across quantization levels and still end up with models that crash or run too slow. llmfit changes that. It detects your CPU, RAM, GPU, and VRAM, then scores every model in its database against your hardware. Instead of assuming one quantization level, it tries the best quality that fits. Starts with Q8_0, walks down to Q2_K if needed. If nothing fits at full context, it tries half context. You get the highest quality model that actually works. Each model gets scored on Quality, Speed, Context, and Capability. The weights shift based on what you're doing. Chat models prioritize speed, reasoning models prioritize quality. Run it as an interactive TUI to browse models, use CLI mode for a quick table, or get JSON output for scripts. There's a REST API for cluster schedulers. You can also run it in reverse. Give it a model you want to run and target performance, it tells you what hardware you need. The real value: you see ranked options before downloading anything. No more burning bandwidth on 50GB models that won't run. It's 100% open source. Link to llmfit in comments!

Gm, Unis. I’ve been deep in the product every day, making a thousand small changes, testing details, and trying to build something that can actually survive after launch - not something that looks clean on mint day and disappears by week two. Here’s the part most people avoid saying: NFT projects usually don’t die because the art is bad. They die because there is no system behind them. No traffic engine, no content flow, no way to keep attention moving once the first wave is gone. Founders burn out, hire people they can’t really manage, and slowly the community goes silent. That is the problem we are building around. The LMM behind UniPix is designed to support the operational layer of a project: traffic, audience conversion, X presence, WL/GTD analysis, community signals, and the daily routine work that usually drains founders. It helps us see patterns earlier, understand who brings real value, and avoid building around people who are only here to extract and leave. UniPix is not just a collection you mint and forget. It is access to a system that keeps working after launch day. The model gets sharper with use. The product keeps moving. And I think the next cycle will make this obvious: the projects still alive months after mint won’t just be the ones with the loudest launch. They’ll be the ones with a real system behind them. That is what UniPix is building.

Microsoft is reducing Copilot in Windows 11 because users complained there was too much AI and the features felt pushy and annoying. In the latest test versions, the company removed the Ask Copilot button from the Snipping Tool and Photos app. Notepad no longer shows the Copilot name and now calls its AI tools Writing Tools, with other apps getting similar changes. Xbox leader Asha Sharma said Microsoft will remove Copilot features that do not give real value, including stopping development for phones and game consoles. Copilot head Jacob Andreou added that it is important to take it out of places where it does not work well. Microsoft now wants to make Windows more reliable first and will use AI only where it truly helps, especially for business users. Via:windowslatest

这两天收到了来自@MSX_CN @msxcom一个挺有仪式感的礼物，必须单独说一句感谢，一台做工很漂亮的复古相机，质感拉满。 感谢 @BTCBruce1 @BeliaSchoo91221 麦通 MSX 走合规路线，是一个偏去中心化的 RWA 平台，将美股等传统资产代币化上链，同时支持衍生品交易。 在当前环境下，大家越来越关注合规和真实资产，这种把华尔街资产直接带到钱包的模式，正好符合趋势。 Real Value On-chain，从 Wall Street 到你的钱包，这句话放在 2026 年看，反而更有现实意义了。

🚨UBISOFT Breach Details: • Ubisoft was hacked, forcing Rainbow Six Siege servers offline • Hackers gained access to internal systems • Players randomly received billions of R6 credits • Rare / unreleased skins were unlocked on some accounts • $13 million real value in virtual currency given out by the breach. • Accounts were banned & unbanned without reason • Rumours claim up to 4 different hacker groups were involved • Ubisoft confirmed it was a security breach, not a bug • Servers were shut down to contain the damage • Ubisoft says players won’t be punished • All illegitimate items & credits will be rolled back

🧵AMA Recap｜Moonlight × UXLINK CEO Rolland Our AMA title was “Why UXLINK May Be One of the Most Mispriced Mass-Adoption Infrastructures in Web3?” In every Web3 cycle, “Mass Adoption” becomes the loudest catchphrase.  But after hosting countless AMAs and speaking with teams across ecosystems, I’ve learned one thing: very few projects are actually solving the hardest part of adoption, bringing real Web2 users into Web3 and keeping them there through real relationships, real usage, and real value. That’s exactly why I invited Rolland, CEO of UXLINK, to this AMA.  Our goal was simple: cut through the hype and examine who is quietly building the long-term growth infrastructure of Web3. What followed was a conversation that reframed how I, and likely many listeners, should think about mass adoption. Mass adoption is already happening, but not in the way most people think. Rolland began by acknowledging that mass adoption is no longer theoretical.  We’ve already seen projects like Catizen, CYBER, and PARTI drive explosive user growth in their respective domains, gaming, decentralized identity, and AI content.  Each of them is executing extremely well on a clearly defined track and has successfully pulled waves of new users toward Web3. But Rolland challenged us to look beneath the surface. While these projects shine within specific verticals, very few are building a persistent network of real people and real relationships.  UXLINK operates precisely in this overlooked layer, not as a spotlight application, but as the foundation beneath the ecosystem. From my perspective as the host, this was the first key insight: UXLINK is not competing for attention; it is competing to become indispensable. To explain this difference, Rolland introduced an analogy that stayed with me throughout the AMA.  He described most successful applications as “track leaders”, highly optimized products designed to win within a single scenario. UXLINK, by contrast, is building the “soil.” If other projects are digging wells on their own land, UXLINK is laying the underground water network. Instead of optimizing for one product form, UXLINK focuses on connecting real users, verifying social relationships, and creating a reusable growth layer that any project can build upon.  Tracks may change over time, but soil compounds. One of the most important moments of the AMA came when Rolland reframed UXLINK’s core mission.  Most Web3 projects ask, “How do we grow faster?” UXLINK asks a very different question: “How do we make the entire industry grow more easily?” That distinction explains why UXLINK doesn’t always look flashy during short-term market cycles. Infrastructure rarely does.  But once established, it becomes extremely difficult to replace.  From a host’s perspective, this also clarifies why UXLINK may be systematically undervalued, its value shows up in what others are able to launch, scale, and sustain because of it. Rolland then broke down UXLINK’s long-term value into four deep moats, and hearing them explained together made it clear why the project sits in a category of its own. First is the real social graph. In an industry filled with bots, scripts, and artificial activity, UXLINK insists on doing the hardest thing: connecting real people through acquaintance-based social networks.  This approach has enabled the genuine migration of tens of millions of Web2 users into Web3. Real relationships are an asset that cannot be fabricated or gamed. Second is OAOG, UXLINK’s cold-start engine. OAOG is not a marketing slogan but a precision-operated growth system.  By combining social trust, verifiable relationships, and fission mechanisms, it allows projects to bootstrap real communities across chains, regions, and markets, breaking the traditional cold-start curse in Web3.