🔲 AMD Unveils 2nm Data Center Chips: Lisa Su confirmed 2026 launches of Venice Epyc CPUs and MI400 AI accelerators, boasting 70% faster performance and massive scalability gains.
🏗️ Musk Floats Tesla Chip Fab Plan: Elon Musk said Tesla may build a “gigantic” semiconductor plant to meet soaring AI and robotics chip demand as supplier output from TSMC and Samsung falls short.
🤖 OpenAI Urges Chips Act Expansion: OpenAI reportedly asked the Trump administration to extend the Chips Act tax credit to include data centers, not just semiconductor fabs.
🤥 Meta Tied to Scam Ad Profits: Reuters found Meta made up to $16 billion—about 10% of 2024 revenue—by tolerating scam ads that targeted likely victims, prioritizing AI funding over enforcement.
⚠️ AI Frenzy Triggers DRAM Price Spike: Global DRAM prices soared 172% year-over-year as AI server demand drained supplies, forcing chipmakers to halt orders and driving shortages into 2026.
💸 Wall Street Cools on AI Stocks: Tech investors are pulling back as AI growth projections flatten and hardware costs soar. Why it matters: market skepticism signals a maturing phase for the once-booming AI sector.
💸 Meta Commits $600B to U.S. AI Buildout: Meta will invest $600 billion over three years in U.S. data centers and jobs to fuel its AI superintelligence goals, front-loading massive compute capacity.
🎭 Berkshire Warns of AI Deepfakes: Warren Buffett cautioned viewers about AI-generated videos impersonating him, as Berkshire flagged rising deepfake scams spreading false investment advice online.
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📺️ FROM THE LIVE SHOW
Anton Osika on Customers Building Million-Dollar Businesses on Lovable
$3 million in 48 hours.Anton Osika (@antonosika), Co-Founder & CEO of @Lovable, on ignoring the haters.“When you build momentum, some people will try to take you down for clicks. Someone pulled search-traffic data that didn’t match our actual traffic, or our paying… pic.twitter.com/EfgfZoP8r0
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Google's Suncatcher, OpenAI TEAR, Apple $1B Deal for Gemini, Vidu Q2, and More!
Google plans solar-powered AI data centers in space, OpenAI signs massive compute deals, Apple taps Gemini for Siri, and Microsoft debuts a new image model.
👾 FORWARD FUTURE ORIGINAL
The Line Is Disappearing: Inside Figma's Bet on AI-Native Product Development
The best product teams have always understood something counterintuitive: the walls between design and engineering are really just suggestions. At companies like Airbnb or Stripe, designers prototype in code and developers obsess over pixel alignment. Now they're vanishing entirely.
Kris Rasmussen, CTO of Figma, has a front-row seat to this transformation. His company's design platform serves millions of users, from scrappy startups to Fortune 500 enterprises. But here's the thing: only a third of them are actually designers. The rest? Product managers, marketers, executives, engineers—basically everyone involved in shipping software. → Read the full article here.
🦾 ROBOTICS
Elon Musk Bets Big on Humanoid Robots as Tesla’s Next Growth Engine
Elon Musk is positioning Tesla’s humanoid robot, Optimus, as central to the company’s long-term AI ambitions—potentially eclipsing its vehicle business. Following shareholder approval of his $1 trillion pay package on November 7, 2025, Musk reaffirmed plans to produce one million AI-driven robots within a decade, describing Optimus as “bigger than cell phones.”
Tesla’s push aligns with a broader Silicon Valley race for humanoid robots, with Apple, Foxconn, and startups like 1X also developing human-shaped machines. Analysts say falling hardware costs and improved AI dexterity could make humanoid robots commercially viable across factories, homes, and care settings by 2030. → Read the full article here.
📚 RESEARCH
Google Research Introduces “Nested Learning” to Tackle AI’s Memory Problem
Google Research has unveiled Nested Learning, a new machine learning paradigm designed to overcome “catastrophic forgetting”—when models lose old knowledge while learning new tasks. Detailed in a NeurIPS 2025 paper, the approach treats AI systems as a hierarchy of smaller, interlinked optimization problems rather than a single training process.
By merging architecture and optimization into one nested system, each component learns and updates at its own frequency—mimicking how the human brain manages short- and long-term memory. A proof-of-concept model, Hope, built on these principles, showed superior language modeling, reasoning, and long-context memory performance compared to Transformers and Titans models. → Read the full paper here.
💼 JOB BOARD
The Job Board for Al-Fluency
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🧰 TOOLBOX
Trending AI Tools
👥 AG2: Open-source platform for building and deploying collaborative, production-ready AI agents.
📟 Builder.io: AI-powered Figma plugin that converts designs into clean React, Vue, or Tailwind code.
🖼️ BlueWillow AI: Generate high-quality images and art from text prompts for creative and design projects.
🤔 DAILY BYTE
XPENG IRON Robot Cut Open On Stage To Prove It’s Real
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