mccarthy

I've tried mapping the entire AI engineering journey into a metro system. The problem with most AI roadmaps: They're linear. Step 1, Step 2, Step 3. As if everyone starts at the same place and wants the same destination. But AI engineering isn't linear. It's a network. → A software engineer skips Python basics, jumps straight to LangChain → A data analyst already knows Pandas, needs Transformers next → A product manager wants RAG and Agentic AI, not CNNs → A researcher needs Ethics & Safety before deployment A metro map captures this reality. Generative AI Hub (Line 4) connects to: → Machine Learning Loop (you need Transformers first) → Applied AI Sector (where RAG becomes chatbots) → Tooling & Deployment (where demos become products) Career Launchpad (Line 8) connects to: → Every other line (skills from any track convert to job offers) Ethics & Safety (Line 7) connects to: → Deployment (you can't ship without guardrails) → Applied AI (real-world projects need fairness and privacy) The 8 lines: 🟠 Foundations - Python, Math, Git (boarding passes) 🔵 Machine Learning - Neural Nets, CNNs, Transformers (the heart) 🟡 Deep Learning Express - LLMs, Fine-Tuning, PyTorch (fast track) 🟢 Generative AI Hub - RAG, Diffusion, LangChain (the magic) 🩷 Applied AI - Agentic AI, Healthcare, Chatbots (real projects) 🟣 Tooling & Deployment - Cloud, Kubernetes, MLOps (production) 🔴 Ethics & Safety - Bias, Privacy, Governance (guardrails) 🟢 Career Launchpad - Portfolio, Interviews, Networking (job offers) You don't take every line. You don't visit every stop. Find where you are. Pick your destination. Transfer as needed. Bookmark this. Start today. If you find my insights and updates helpful, consider following @techNmak for more.

"Do not learn to code" is the worst career advice of the decade. People are telling college students to skip Computer Science because AI will just automate it all. Andrew Ng just killed this myth at Stanford with a brilliant analogy. When he tried to generate images with Midjourney, he typed: "make pretty pictures of robots" and got garbage. His collaborator, however, understood Art History. He knew the exact vocabulary of lighting, genre, and palette. He spoke the "language of art," and generated masterpieces. Andrew Ng is seeing the exact same thing happen in software engineering right now. AI didn't replace the need to understand Computer Science. It made Computer Science the required vocabulary to control the AI. If you don't understand how computers actually work, you are just typing "make a pretty app" into Cursor and shipping fragile, unscalable logic. Here is Andrew Ng's exact hiring hierarchy today: Level 1: 10 years of experience, but codes by hand (He won't hire them). Level 2: Fresh college grad, but highly fluent in AI-assisted coding (He hires them over the 10-year veteran). Level 3 (God Tier): Deeply understands CS fundamentals AND uses AI-assisted coding. When humanity went from punch cards to keyboards, coding got easier, and more people coded. We are at that exact inflection point again. AI doesn't replace fundamentals. It multiplies them.

"We believe really strongly that people have to be in charge. The robots have to work for the people." David Faugno CEO of @1Password dives into the balance between AI innovation and human responsibility. Watch to hear more from top tech leaders at @OneRSAC 2026 ⤵️ youtu.be/m5HhvuoOLTY?si…

AI Agents Tech Stack

Bit-Pong interactive table, a high-tech ping-pong table that blends traditional table tennis with retro arcade gaming.

Amazon Supply Chain Services—our freight, distribution, fulfillment, and parcel shipping network—can now be used by any business. Was built over nearly three decades to move, store, and deliver products across land, air, and sea. All one network, so no need to piece together different providers at every stage. Same reliability and speed our customers rely on. P&G, 3M, Lands' End, and American Eagle already on board. Gonna help a lot of businesses move faster, save money, and simplify things. aboutamazon.com/news/retail/am…