Learning Rust with Free Introductory Book

If you're wondering how to teach AI literacy without overhauling your entire curriculum — I wrote this for you. In this blog post, I walk through a classroom challenge called Chopped: AI Stars that helps students develop real AI decision-making skills. And inside the post, there's a free, classroom-ready lesson plan you can adapt for any grade level or content area. One lesson. Real skills. No overwhelm. Read the post + grab the free lesson plan here 👉 https://lnkd.in/g6AwWrMr

There was some good feedback from several people interested in the free AI 101 summer learning curriculum I posted a couple of weeks ago: provide a more detailed "lesson plan" with step-by-step guidance for walking through each week's exercises. I've updated the page with actionable plans at the bottom of each week's topic. I would love to know if this is helpful for people! https://stryker.fm/ai-101/

Demystifying Transfer Learning: Making Machines Learn Faster Imagine if you could get an A+ on a test without ever cracking open the textbook for that subject. Sounds like magic, right? Well, in the machine learning world, transfer learning tries to do just that by letting models learn from what others have already learned. Instead of starting from scratch and spending ages training a model, transfer learning borrows knowledge from pre-trained models to speed things up....

🚨 TensorFlow vs. PyTorch in finance is the wrong debate. On Wall Street, the real edge is rarely just the model. It is the ability to go from research → signal → execution → risk control without losing speed, discipline, or reliability. Here’s a practical example: A trading team wants to detect signals from earnings calls, news flow, and market microstructure data. At the research stage, PyTorch is often the better fit. It gives quants and ML teams the flexibility to test new architectures, iterate quickly, and sharpen alpha ideas. Once that signal proves useful, TensorFlow becomes valuable at the production stage. It helps scale the model into live workflows where latency, stability, and monitoring matter — whether that is: 📊 Portfolio surveillance ⚡ Intraday risk 🧠 Real-time decision engines That is what AI in finance actually looks like: PyTorch for discovery. TensorFlow for deployment. One drives innovation. The other enforces operational rigor. And in finance, a model is only powerful if it can survive the real world: ✔️ Tight controls ✔️ Compliance expectations ✔️ Market volatility ✔️ Decisions that must happen in seconds That is where the combination becomes powerful. The real advantage is not choosing one side. It is knowing how to use both. #Finance #WallStreet #AI #MachineLearning #TensorFlow #PyTorch #FinTech #QuantFinance #RiskManagement #DataScience

Growing up, most of us never questioned how we were learning. You took whoever was around, whoever the elders recommended, and trusted it was right. No way to verify credentials, no structure, no roadmap. You just hoped what you were absorbing was accurate. That gap is real, and it affected a lot of us more than we realize. I'm glad to see Sherman Singh, Omar Abbas, and Ibrahim Ahmed building something that actually fixes this. Riwayah.Ai is a personalized learning platform that connects you with the right teacher, provides a structured path, and makes the journey accessible, no matter your schedule or where you're starting from: something our generation didn't have, but the next one will. Always knew these three were building something meaningful behind the scenes. Glad to see it come to light. Go check them out and see exactly what I'm talking about: Riwayah.Ai

even if it was a simple YouTube video. don't lose your appetite to learn . in this fast paced world, you must put emphasis in learning, even if you don't use the skill in your current work, even if you sacrifice some of your personal time. because nothing is permanent and you should always be ready. that's why I started learning ai automation , I might not use is straight away, but I want to stay relevant in the upcoming changes of this world

A student asked me what to learn first. I gave him 12 weeks of the wrong answer. I sent him down the textbook path. Linear algebra. Calculus. Then ML fundamentals. Then deep learning. Then LLMs. Then maybe, after six months, build something. He came back, motivated but stuck. The friend who started by building a chatbot the same week — no math, no theory, just the API — had shipped three projects in the time my mentee was still on backpropagation. Three things I tell mentees now instead. 1. Build before you understand. Ship a working agent in week one. The questions you get from a broken thing are sharper than the answers you read in a textbook. You'll learn vectors faster when a retrieval is wrong than when a slide says they're important. 2. The stack matters less than the eval. Mentees obsess over LangChain vs Mastra vs raw SDK. None of it matters if you can't tell whether your output got better or worse this week. A 40-row golden set beats a framework change. 3. Pick one specific problem you actually care about. Not "build an AI agent." Something like: "summarize my reading list weekly and email me the best three." Specific problems force specific decisions. Generic problems produce generic learning. The roadmap isn't wrong. It's just second. The first thing is to build something embarrassing, in public, this week. #AIEngineering #BuildInPublic #LearnInPublic #AgenticAI #IndianTech

We’ve reached the finale of our 10-part coding logic series! Part 10 is all about Decomposition—the art of breaking impossible problems down into manageable pieces. This entire series was created to prove how quickly you can scale high-quality educational videos using an AI training video generator. Ready to upgrade your L&D content? Try for free link in bio.

After months of building, we are thrilled to share what we've been working on. TUT'TOP is a personalized AI tutor that answers students' questions directly from their own course material — with cited sources every time. The problem is simple: students spend too much time searching through slides, PDFs, and textbooks for a single answer. TUT'BOT finds it in seconds, grounded in the actual content their professors gave them. No hallucinations. No generic answers. Just their courses, made searchable and conversational. Visit our website : tuttop.io

Traditional studying is broken. And it might be your fault. I sit for the SIE exam this Saturday. I spent weeks studying the traditional way. Lectures, PowerPoints, 281-page textbook, generic quizzes... You know the drill. But here's the problem: Lectures assume everyone learns at the same pace. PowerPoints assume everyone's weaknesses are the same. Textbooks assume you have unlimited time. So 3 days ago, I stopped. Instead, I uploaded all my study materials into AI and had it tailor everything to me. My learning style? ✅ My weak areas? ✅ My schedule? ✅ A study regimen built around you, in real time. One you can actually have a conversation with. It's a private tutor without the private tutor price tag. I've learned and retained more in 3 days than I did in the 3 weeks before it. So if you're still grinding through the traditional methods and not seeing results, there's a better way. And it's sitting right in front of you. The only question is whether you'll use it.