GenAI Works

How to learn from AI without losing your mind - The Oxford way AI should challenge you to think harder. That’s the secret behind the Oxford Method reimagined with AI. Here’s how to make AI your smartest study partner: → Use AI as a Socratic tutor and let it quiz you till you really understand → Explain a concept at three levels: child, teen, academic and ask AI to critique you → Build mastery step by step: Remember → Understand → Apply → Analyze → Evaluate → Create → Summarize papers yourself first, then compare with AI’s version Why it works: → No embarrassment asking dumb questions → Learn at your own pace → Identify your real weak spots Remember: AI is your training partner; the command is still in your hands. You think, AI does the challenging. Pick a concept, let AI challenge you step by step and watch your understanding deepen. What will you tackle first? Our AI academy will be open soon. check it out: https://lnkd.in/dmK2fBwQ #education #ai #ml #llm #generativeai

You cannot run a 2026 business on a 2024 intelligence stack. Here’s how to understand the layers of AI that most people miss. If your AI strategy is still stuck inside the green circle, you are operating two generations behind the curve. The four layers of intelligence in AI systems: LEVEL 1: LLM (Foundation):  A model trained on vast amounts of data. It predicts the next word. But it is static. It doesn't know what happened 5 minutes ago. It hallucinates. LEVEL 2: RAG (Knowledge):  That's the context layer.  It uses vector search and retrieval pipelines to ground the model in your specific data rather than just its training set. Instead of relying on its memory, it "looks up" the facts first. LEVEL 3: AI Agent (Executor):  An LLM that has access to Tools (Web search, Calculator, API).  Agents handle task decomposition, API calling, and feedback loops. It can browse the web or run code to answer you. LEVEL 4: Agentic AI (System):  A system that can plan, reason, and error-correct autonomously. Multi-agent collaboration, goal planning and long-term memory management. An Agent does one task. Agentic AI figures out the 10 steps needed to solve a complex problem, assigns them to agents, and fixes mistakes along the way. In a Nutshell: LLM = "I know things." RAG = "I know your things." Agent = "I can use tools." Agentic AI = "I can do your job." What is your #1 priority for 2026: better accuracy (RAG) or better execution (Agents)? Explore GenAI Works Academy for hands-on courses that teach you how to use AI tools in real life and not just in theory: https://lnkd.in/g3aXnMT3 #ai #generativeai #rag #aigent #machinelearning

These 10 YouTube channels teach you more about AI than a $10,000 bootcamp If you’re learning AI in 2026, here’s a list actually worth following to stay sharp with AI trends, tools and insights: Andrej Karpathy → Founding member of OpenAI and the former Director of AI at Tesla. His "Zero to Hero" series is legendary. Posts once a year but that’s enough to create ripples in the AI industry. (Note: He taught Stanford's first Deep Learning course (CS231n), which trained the first generation of modern AI engineers) Serrano Academy → Luis Serrano (PhD in Math and Author) simplifies complex topics through hand-drawn cartoons and analogies. Learn attention mechanisms and LLMs with a friendly and high-energy vibe. GenAI Explained → Turning complex AI breakthroughs into short, practical, and easy to understand videos that help you discover what matters, learn how it works and grow your skills in real-time. Backed by a global community of 13M+ builders and researchers 3Blue1Brown →  Visual poetry. Grant Sanderson uses his own custom animation engine (Manim) to visualize neural networks, backpropagation and linear algebra. He made the Transformer architecture (the T in ChatGPT) visually understandable for the first time. Jeremy Howard →  Top-down learning. Build the app first, figure out the math later. Teaches practical deep learning for coders. No PhD required. Stanford Online →  Ivy League education for free. Famous CS224N (Natural Language Processing) and CS231n lectures.  Watch the world’s leading researchers (like Li Fei-Fei and Christopher Manning) deliver the same content their students pay thousands for. Hamel Husain → Learn the dirty work of AI: evaluations (evals), data curation and deployment. He is the Engineer's Engineer. Former Lead ML Engineer at GitHub and Airbnb and a major contributor to open-source tools like nbdev. Machine Learning Street Talk → Deeply technical and unedited. Interviews with AI rebels like Yann LeCun (former Meta's AI chief), who challenge the current direction of LLMs. Critical, non-hype perspective. Dave Ebbelaar → King of RAG (Retrieval-Augmented Generation). While others talk about models, he talks about pipelines and how to connect your private data (PDFs, Notion, SQL) to an LLM safely. Lex Fridman → Introspective interviews with everyone who matters in AI: Sam Altman, Demis Hassabis, Yann LeCun, Elon Musk. Vision and history of where the field is going. Crowdsourcing the best AI minds, who else belongs here? Check out GenAI Explained on YouTube where we break down the AI research and tools before they go mainstream: https://lnkd.in/gnaqg9hu #ai #youtube #research #machinelearning #generativeai

Data science runs on maths, not just on GPUs Everyone talks about Python, dashboards and AI models. But the real hero of data science is maths, the kind you thought you’d left behind in college. The foundations that actually matter: → Statistics and probability help turn uncertainty into insight. → Linear algebra powers recommendation engines and neural networks. → Calculus makes optimization possible so models can learn instead of guess. → Discrete maths keeps data and algorithms structured, not scattered. Once you understand the maths, data science stops feeling like magic and starts making sense. Which part of data science made you question all your life choices? #datascience #machinelearning #maths #statistics #ai

Google released 8 free tools that save 11 hours of meeting work per week. If your team’s current “AI workflow” is typing a few prompts into ChatGPT, you’re barely using what’s actually available to you. Google built a full stack that can handle research, content, video, meetings and internal automation. The kind of work that usually eats your evenings. The tools:  → NotebookLM: Turns your PDFs, docs and notes into a private research assistant. → Veo 3 / 3.1: Text to video for short, high quality clips with audio. → Gemini Live: Real time voice conversations to brainstorm, plan and get answers hands free. → Gems: Build reusable “mini agents” for your recurring tasks in a few prompts. → Imagen / Imagen 3: High fidelity text to image for campaigns, ads and mockups. → Cloud Vision API: Read, label and understand images at scale from your apps. → Gemini Image Generation (Nano / Pro): Fast image generation and photo editing for daily content. → Firebase + AI tooling: Ship AI powered prototypes quickly with auth, storage and APIs handled. How they connect: → NotebookLM + Gems turn turn meeting notes into automated follow-ups → Gemini Live pressure-tests your decisions in real time → Imagen + Veo turn ideas into ready-to-use assets → Firebase turns it all into working features One workflow to try: Upload last week’s meeting notes → build a Gem that automates follow-ups → use Gemini Live to stress-test the plan → generate visuals in Imagen. What’s one workflow inside your team that deserves its own Gem? #googleai #aitools #productivity #llm #futureofwork

Why every data scientist needs these ML concepts Machine learning isn’t optional anymore. It changes how companies use data in day-to-day work. Knowing these concepts is essential because it equips you to: → Build models that work: Core techniques like Regression, Decision Trees and Neural Networks teach you how predictions are formed and how to tackle different problems. → Handle data effectively: Data Wrangling, Dimensionality Reduction and Exploratory Data Analysis ensure your insights are built on clean, meaningful data. → Build reliable solutions: Understanding Metrics, Regularization and Bagging helps create models that perform well beyond your dataset. → Solve complex problems: Advanced methods like Reinforcement Learning and SVMs prepare you for scenarios where simple models fail. → Think critically: Probability, Statistics and Variance help interpret results correctly and reason under uncertainty. → Apply knowledge in practice: Programming turns theory into actionable solutions that drive impact. Knowing these concepts makes you more capable and creative in solving real-world data problems. Which of these do you find most challenging in your day-to-day work? Save this for your next learning sprint. #datascience #machinelearning #ml #futureofwork #ai

Google’s new research paper drew its own diagrams! PaperBanana, a recently popular AI tool, is an agentic system that converts academic diagrams from theoretical text. If you’ve ever written a scientific piece, you know the final 1% of the design work requires a week of moving arrows in Figma. This is precisely what Google and Peking University's most recent paper addressed. Our most notable observation is this: PaperBanana does not actually need perfectly matched references to work. All you need to do is provide your text and a few references, and it drafts a paper-ready figure with a structured layout. But what’s actually unique is how it achieves this. 5 agents collaborate like a design team: → Retriever learns from elite papers from previous years → Planner maps out a blueprint from your raw text → Stylist applies the aesthetic rules of top-tier journals → Visualizer and Critic work in a loop to catch errors and improve clarity. By observing good diagrams, it learns the visual grammar. In blind tests, people actually preferred these AI-generated figures 75% of the time. It even beat human-drawn originals in conciseness by 37%. And yes, researchers used this tool to make diagrams for the paper itself. It is a rare case of a tool that knows how to explain its own existence. Would you let an AI draw your next Figure 1 or is your core logic too important to automate? We would love to hear your perspective in the comments. #ai #google #paperbanana #research #agenticai

The Battle of AI Protocols: ACP vs MCP vs A2A AI agents are everywhere now. Some book your meetings, some write your code and some manage operations. The challenge is that they speak different languages. Without a shared way to connect, it becomes chaos. That’s where three protocols step in. → Anthropic’s Model Context Protocol (MCP) Imagine an AI sales assistant that needs customer data. Instead of struggling with different APIs, it simply plugs into CRM, email, Slack, GitHub and Postgres through one universal port. That is MCP in action. → IBM’s Agent Communication Protocol (ACP) Think about a hospital. One agent tracks patient vitals, another manages prescriptions, another handles scheduling. ACP acts as the group chat where they coordinate, share tasks and keep everything running smoothly. → Google’s Agent-to-Agent Protocol (A2A) Consider a global supply chain. An agent in a factory in India needs to talk to an agent at a shipping company in Germany. A2A provides the trusted handshake that allows them to collaborate securely across platforms and borders. MCP gives agents the tools to act. ACP gives them the ability to coordinate. A2A gives them the standards to collaborate globally. Together, they form the foundation of the agentic ecosystem that is interoperable and secure. Which of these protocols will emerge as the backbone of agentic AI? #aiagents #agenticai #aiprotocols #llm #futureofai

Inside the Metafication of OpenAI. The company that once said it hated ads is now hiring people who built them. Fidji Simo, the exec who launched Facebook’s News Feed ads is now running OpenAI’s apps. There’s even a Slack channel just for ex-Meta folks. Here’s what’s taking shape: → Simo is leading the push on revenue. → Job posts mention ad platforms and paid growth roles. → The old Facebook ad playbook is getting an AI rewrite. → New products like Sora 2 and ChatGPT Atlas look built for ads. → A billion-dollar burn rate makes the shift hard to ignore. OpenAI is evolving Meta’s ad playbook. The difference is in the data: Facebook learns what you click, OpenAI will learn why you click. Ironically, tech swore it would disrupt advertising but now it’s rebuilding it with one AI product at a time. Are we watching OpenAI solve for alignment... or just for attention? We’re building Copilot, a new class of AI tools that help people think, plan and create faster. This is your chance to be part of it. Join our Reg CF campaign and help shape the future of intelligent work. Learn more & invest: https://hubs.li/Q03QHkjn0 Early investors are eligible for up to 22% bonus shares through Nov 21. Reg CF offering via DealMaker Securities. Investing is risky. #llm #ainews #openai #artificialintelligence #meta

Apple literally built the chip that could make cloud AI feel outdated. The M5 Chip has put a neural accelerator into every GPU core, essentially giving your Mac or iPad its own built-in, private brain. This chip is up to 4x faster at local AI tasks. It means your data stays on your device. This Week in AI: • Apple’s M5: The chip with a brain in every graphics core. • Microsoft: Clippy returns as Copilot’s nostalgic ghost. • Open Source: Apple opens a rare dataset to train vision models. • Anthropic: Claude Code turns the browser into a live dev environment. • Mistral AI: Building the enterprise stack AI will actually run on. For years, Apple watched as everyone else turned AI into a service. Now it’s turning AI into a feature of the device itself. Has Apple finally stopped playing catch-up, or is this only the beginning? #apple #ai #technology #innovation #hardware