1. The specs genuinely laptop-sized satellite internet. For context: the Standard Starlink dish weighs 7-9 lbs, measures 20+ inches across, requires a separate router, draws 75-100W, and needs a dedicated power outlet. The Mini delivers 80-90% of the Standard’s performance in a package that weighs 70% less and uses 60-75% less power.

What if you could control a game with nothing but natural language? This is not GPT Astra or Opus 5.5. @PixVerse R2 makes it real. WASD movement drops you straight into an AI-generated scene, moving through it live not watching a clip play out. Here's the interaction: type "Flame Magic," and your character shoots fire forward, hitting whatever's in its path. This is the shift from typical AI video. Instead of prompt, generate, watch you're now inside the world, guiding it as it happens. AI video is quietly becoming something you walk through, not just watch. Try it Now:[world.pixverse.video/home/?u…] #PixVerse R2

Same page. Two outputs: ❌ Raw markdown → 120KB of noise your agent has to parse itself ✅ ZooData → 29KB of clean, structured JSON. Ready to use. That's ~75% fewer tokens. Downstream. Not a gimmick — that's real context saved, real cost cut.

Your agent has eyes now. But the web data it sees? Dirty, bloated, and expensive to clean. I pointed #Zoodata at a real page to see what came back. Here's what happened 👇

MiniMax just closed a $2B funding round. Even bigger: founder & CEO Yan Junjie announced he'll take no salary until MiniMax reaches AGI — and is committing 4% of his personal equity over 4 years to reward long-term employees. He's also dedicating 1% of his equity to the open-source community — a bet that open-source isn't a side project, but core infrastructure for how AI gets built globally. Conviction, backed by capital. 🔗 reuters.com/world/asia-pacif…

I gave Claude 8 prompts and it built me a complete online business from scratch. It found the niche, built the offer, wrote the sales page, created the content plan, and told me exactly who to target. Here are the exact 8 prompts 👇🏽👇🏽

Two economists just published a mathematical proof that AI will destroy the economy. Not might. Not could. Will — if nothing changes. The paper is called "The AI Layoff Trap." Published March 2, 2026. Wharton School, University of Pennsylvania. Boston University. Peer reviewed. Mathematically modeled. The conclusion is one sentence. "At the limit, firms automate their way to boundless productivity and zero demand." An economy that produces everything. And sells it to nobody. Here is how you get there. A company fires 500 workers and replaces them with AI. A competitor fires 700 to keep up. Another fires 1,000. Every company is behaving rationally. Every company is following the incentives correctly. And every company is building a trap for itself. Because the workers who were fired were also customers. When they lose their jobs faster than the economy can absorb them, they stop spending. Consumer demand falls. Companies respond by cutting costs — which means automating more workers — which means less spending — which means more falling demand — which means more automation. The loop has no natural exit. The researchers tested every proposed solution. Universal basic income. Capital income taxes. Worker equity participation. Upskilling programs. Corporate coordination agreements. Every single one failed in the model. The only intervention that worked: a Pigouvian automation tax — a per-task levy charged every time a company replaces a human with AI, forcing them to price in the demand they are destroying before they pull the trigger. No government has implemented this. No major economy is seriously discussing it. Meanwhile the numbers are already tracking the curve. 100,000 tech workers laid off in 2025. 92,000 more in the first months of 2026. Jack Dorsey fired half of Block's workforce and said publicly: "Within the next year, the majority of companies will reach the same conclusion." Nobody is doing anything wrong. Companies are following their incentives perfectly. That is exactly the problem. Rational behavior. At scale. Simultaneously. With no mechanism to stop it. Two economists built the math. The math leads to one place. Source: Falk & Tsoukalas · Wharton School + Boston University ·

Claude is extremely powerful but You are only using it to create emails. Here's how to use Claude better than 99% poeple

Upload your documents. Get back a podcast of two AI hosts discussing them. That is the feature that made NotebookLM go viral. It is not even the most useful thing it does. NotebookLM is Google's free research tool. You give it sources PDFs, Google Docs, web pages, YouTube videos, pasted text and it becomes an expert on those specific documents. Only those. Nothing else. That last part is the whole point. And it is what makes NotebookLM different from every other AI tool you use. Here is the problem with ChatGPT, Claude, and Gemini for research. They know everything. Which means they will confidently answer from their training data, blend in facts you did not give them, and occasionally invent a citation that sounds real and does not exist. When you are trying to understand a specific set of documents, that is a liability. You can never be fully sure whether an answer came from your source or from the model's memory. NotebookLM closes that door. It only answers from the documents you upload. Every answer comes with inline citations pointing to the exact passage in your exact source. Click the citation, jump to the sentence. No hallucinated facts. No outside noise. No made-up references. For research, that single constraint changes everything. Now here is what people actually do with it, beyond the famous podcast feature. The Audio Overview. This is the viral one. Upload your sources and NotebookLM generates a podcast, two AI hosts discussing your material in a natural, conversational back-and-forth. Students turn lecture notes into something they can listen to on a commute. Researchers turn a stack of papers into a discussion they can absorb while walking. It is genuinely good. And it is free. The interrogation. Upload 40 research papers on a topic. Then ask: "What do these papers agree on, and where do they directly contradict each other?" NotebookLM reads across all of them and maps the consensus and the conflicts with citations to which paper said what. The study guide. Upload a textbook chapter and ask it to generate a study guide, a timeline, a glossary, or a set of practice questions. It builds them from your material, not from a generic template. The synthesis. Upload your own messy notes alongside the source material and ask: "Where am I misunderstanding this compared to what the sources actually say?" It finds the gaps between what you think you know and what the documents actually state. Here is why the document-grounding matters more than the features. The best way to understand a complex subject is not to ask an AI what it knows. It is to give the AI exactly the material you need to understand and then question it relentlessly, knowing every answer is anchored to a source you can verify. NotebookLM is the only major AI tool built entirely around that principle. It is not trying to know everything. It is trying to understand your documents and help you do the same. The podcast feature got it the attention. The grounding is what makes it the most trustworthy AI tool you can put your research into. And all of it is free.

FULL Claude Tutorial for Beginners in 2026! (Become a PRO!)