📚 My 2nd book "𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐢𝐧𝐠 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈" is HERE! ✍ 180 illustrations 📖 319 pages 🔗 1200+ references 😖 When I explain LLMs to enterprises, I watched them struggle. 🔖 They need to understand transformers and fine-tuning—but they shouldn't have to read research papers! 🔥 Our book takes a different approach ↓ 🧠 Your brain processes images 60,000 times faster than text. We are explaining 𝐜𝐨𝐦𝐩𝐥𝐞𝐱 𝐀𝐈 𝐜𝐨𝐧𝐜𝐞𝐩𝐭𝐬 in Visuals. Each chapter: visual first, explanation second. No jargon. No assumed knowledge. ━━━━━━━━━━━━━━━━━ 𝐖𝐡𝐚𝐭'𝐬 𝐢𝐧𝐬𝐢𝐝𝐞: → How LLMs process language → Training vs fine-tuning vs RAG → Prompt engineering frameworks → Model evaluation methods → Production deployment patterns ━━━━━━━━━━━━━━━━━ This helps you avoid surface-level AI understanding. 🔖 Pre-order now at guaranteed lowest price: amzn.to/4qDVfwz 📦 Paperback ships in 2 weeks ━━━━━━━━━━━━━━━━━ 𝐓𝐡𝐚𝐧𝐤 𝐲𝐨𝐮: @ValliappaLakshmanan for co-authoring through my wild ideas and schedule. @OReillyMedia and our illustrator @Silly_Strokes for bringing these concepts to life. Every early reader who pushed me to make this clearer. #generativeai #genai #llms #aiengineering #aiagents

Most engineers never make it to CTO. And it’s not because of skill. It’s because they fail in one room: the meeting room. Leadership isn’t earned with code. It’s earned with voice patterns. Start speaking like a leader: piped.video/watch?v=Ia3hSUYM…

For humans, CONTEXT is everything we don't say — the emotions, gestures, cultural nuances — but for AI models, context is only what we DO say. 🤓 Yes, that’s my Friday thought!

AI jobs aren’t just for coders. There are 12+ AI career paths—half need no coding. Roles like AI ethicist, prompt engineer, and AI product manager are hiding in plain sight. Some pay more than software engineering. You don’t need to be technical—you need to be curious.

APIs are for humans. MCP is for AI agents. With APIs, you code every step. With MCP, AI figures it out. If you're building with AI and not using MCP, you're already behind. Full video: piped.video/dwlE7TiDXz4

Most AI coding fails don't come from bad prompts. They come from missing specs. Here's how to cut your AI coding time in half 1. Specify: Who is this app for? What will they use it for? 2. Plan: What’s the tech stack, architecture, requirements? 3. Tasks: Break the build into small, specific jobs. 4. Implement: Let AI generate and test each piece. Here I explain Spec driven development: piped.video/watch?v=hxVLdCoE…

Look who I ran into at GitHub universe, old friend @_davideast!

𝗧𝗵𝗲 𝗕𝗲𝘀𝘁 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻𝘀 𝗛𝗮𝗽𝗽𝗲𝗻 𝗪𝗵𝗲𝗻 𝗪𝗲 𝗚𝗲𝘁 𝗩𝘂𝗹𝗻𝗲𝗿𝗮𝗯𝗹𝗲 💬 This week at @github Universe, we hosted a developer leadership roundtable with customers across airlines, financial services, and tech. The energy was incredible—not because we had all the answers, but because we didn’t. 𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀: 🌊 We’re all learning to surf the AI wave - No one has it figured out yet, and that’s okay 🎯 Leadership buy-in is make-or-break - Without it, AI adoption stalls ⏰ AI adoption is a habit, not an event - You can’t build habits overnight 😰 Real fears exist - Skeptics on dev teams, concerns about job displacement 🤝 The non-deterministic nature of AI is our biggest challenge and opportunity - It’s reshaping how we approach application development and agentic lifecycles 𝗪𝗵𝗮𝘁 𝗠𝗮𝗱𝗲 𝗧𝗵𝗶𝘀 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁: We skipped the pitch decks. Instead, we shared what’s actually working, admitted where we’re struggling, and learned from each other in real time. That’s the kind of collaboration that moves us all forward. 🚀

🚀 New Video! 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐄𝐱𝐩𝐥𝐚𝐢𝐧𝐞𝐝 𝐢𝐧 19 𝐌𝐢𝐧𝐮𝐭𝐞𝐬 – 𝐋𝐞𝐭'𝐬 𝐆𝐨!  🤖 My latest beginner’s guide for anyone ready to demystify AI agents! Whether you’re new to the space or looking to enhance your understanding, this 19-minute video will lead to a solid path! ✨ 𝐖𝐡𝐚𝐭 𝐈'𝐯𝐞 𝐜𝐨𝐯𝐞𝐫𝐞𝐝: ↳The true definition of an AI agent (and why ChatGPT isn’t one!) ↳The four game-changing components: ↳Language Model (the brain) ↳Tools (the hands & senses) ↳Memory (for context & learning) ↳Orchestration Layer (for autonomous decision-making) ↳Where to start—even with zero coding experience ↳Core agent architecture patterns and when to use them ↳The latest on AI agent protocols like MCP & A2A ↳4 practical paths to build your own AI agent, tailored for every skill level 🎒 My favorite learning resources and a step-by-step roadmap to level up fast AI Agent Fundamentals track: datacamp.pxf.io/vPJGAd Ready to accelerate your journey? Whether you’re no-code or pro-code there’s a path for you. 🔗 Save & watch the video, and share with me where you are in your AI journey. piped.video/watch?v=4kBEMwrZ… 👩‍💻 Let’s build the future together!

My longer take with the WHY is here linkedin.com/posts/pvergadia…

99% of the AI Startups will DEAD as early as next year!!

𝐈 𝐥𝐨𝐯𝐞 𝐟𝐢𝐧𝐝𝐢𝐧𝐠 𝐭𝐲𝐩𝐨𝐬 𝐭𝐡𝐞𝐬𝐞 𝐝𝐚𝐲𝐬. Because it means a real person actually wrote it. Don't get me wrong—AI is a powerful tool. But there's a difference between using AI and just hitting copy-paste. The internet is drowning in polished, perfect, soulless content right now. So when I stumble across something raw...something that feels like it came from a real human brain, with all its beautiful imperfections? 🛑 I stop scrolling. I ACTUALLY read it! 🤗 We need more messy, genuine, from-the-heart writing. 🔥 Not everything needs to be perfect. Sometimes it just needs to be real. hashtag#authentic

The AI-powered SDLC is a vibrant ecosystem of 80+ specialized tools. But is this the calm before the storm?? ⛈️ 𝐌𝐲 𝐭𝐚𝐤𝐞: Working with large enterprises, I see the same pattern repeatedly. They don't want 15 point solutions that create a Frankenstein stack. They want: 🔹 𝐔𝐧𝐢𝐟𝐢𝐞𝐝 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞: Who approved what AI tool? What data is it trained on? 🔹 𝐂𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐭 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲: One SSO, one audit log, one compliance review. 🔹 𝐂𝐞𝐧𝐭𝐫𝐚𝐥𝐢𝐳𝐞𝐝 𝐂𝐨𝐬𝐭 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭: Not 47 separate invoices. 🔹 𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝𝐢𝐳𝐞𝐝 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬: New devs onboard to ONE platform, not a dozen tools. 🔹 𝐕𝐞𝐧𝐝𝐨𝐫 𝐂𝐨𝐧𝐬𝐨𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧: Fewer contracts, fewer renewals, fewer points of failure. The developer experience gap between "𝐛𝐞𝐬𝐭-𝐨𝐟-𝐛𝐫𝐞𝐞𝐝 𝐭𝐨𝐨𝐥𝐬" and a "pretty good integrated platform" often matters less than the operational overhead. 𝐈 𝐬𝐮𝐬𝐩𝐞𝐜𝐭 𝐰𝐞'𝐥𝐥 𝐬𝐞𝐞 2-3 𝐞𝐧𝐝-𝐭𝐨-𝐞𝐧𝐝 𝐰𝐢𝐧𝐧𝐞𝐫𝐬 with selective acquisitions filling the gaps. The specialized tools that survive will either: ➞ Get acquired ➞ Serve SMBs and startups who value flexibility over governance ➞ Become features, not companies But I could be wrong. Maybe the API economy and composability will actually work this time. What do you think? 🤔 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫𝐬: Am I missing something? Does tool quality and specialization trump the pain of integration? 𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐋𝐞𝐚𝐝𝐞𝐫𝐬: Are you seeing the same consolidation pressure, or is your organization happily managing a diverse AI tool stack? Genuinely curious where I might be off-base here. 👇

𝐄𝐯𝐞𝐫𝐲 𝐀𝐈 𝐉𝐨𝐛 𝐞𝐱𝐩𝐥𝐚𝐢𝐧𝐞𝐝 𝐢𝐧 13 𝐌𝐢𝐧𝐮𝐭𝐞𝐬 Want to figure out which AI role is right for you? In this video, I walk through 12 most-common AI Jobs. I explain what each role does, the skills you’ll need, and the future growth potential. ✅ Data Engineer ✅ Data Scientist ✅ MLE Machine Learning Engineer ✅ AI Engineer ✅ ML Researcher ✅ NLP Engineer ✅ Computer Vision Engineer ✅ AI Product Manager ✅ AI Ethics ✅ MLOps Engineer ✅ Cloud AI Architect ✅ Data Annotator ✅ Prompt Context Engineer ✅ Core skills you need to land AI job ✅ Soft skills you need to land AI job Watch full video: piped.video/watch?v=uaV9Ibdg…

Do you need a boss to grow your tech career?

Hands-on courses and books that can accelerate your AI Engineer jounery: AI Engineering for Developers: datacamp.pxf.io/YRA5JJAI Engineering for Developers Certification: datacamp.pxf.io/Dy6E2jAI Fundamentals: datacamp.pxf.io/LKLvVZAI Engineering book by Chip Huyen: amzn.to/4mGKztY