
20 ChatGPT prompts for OT / ICS cybersecurity 👇 From asset inventories to threat hunting, tabletop exercises, IR plans, and secure remote access. AI won’t replace OT experts — but it can scale the few we have. Credit: Mike Holcomb #OTSecurity #ICSCyber #CriticalInfrastructure #CyberSecurity
Core concepts in Artificial Intelligence — clearly mapped in one visual. From Machine Learning and Deep Learning to NLP, Computer Vision, Reinforcement Learning, Robotics, and Generative Models, this overview highlights how the AI ecosystem fits together and where each discipline...

AI dashboards > spreadsheets 📊🤖 Describe the question AI builds the dashboard You tweak logic & visuals Insights arrive in the meeting Finance shifts from maintenance → decisions. #AI #FPandA #Dashboards #GenAI #Analytics https://t.co/F6j5I7rAAH

Top 6 AI skills for 2026 👇 • Prompt engineering • Workflow automation • AI video creation • RAG systems • Vibe coding • AI search optimization The edge isn’t tools. It’s systems + execution. #AI #GenAI #FutureOfWork #Automation https://t.co/6WGm2kakeX

Most AI systems don’t fail because of bad prompts. They fail because context breaks at scale. If you’re building AI agents, LLM workflows, copilots, or automation, this is the layer that quietly decides whether your system is reliable or unpredictable. We’re hosting a...

🚀 Artificial Intelligence is reshaping the future of transportation — from predictive maintenance and smart mobility, to logistics, infrastructure, and customer experience. In my latest article, I explore how AI is becoming a strategic lever for CEOs and entrepreneurs, helping organizations...
RAG vs Self-RAG vs Agentic RAG — three generations of retrieval-augmented intelligence that show how fast AI systems are evolving. This visual breaks down the differences between: • RAG Pipeline – classic retrieval + reranking • Self-RAG – autonomous query...

The Generative AI ecosystem is evolving into a full tech stack — powering autonomous AI agents. From infrastructure and LLMs to RAG pipelines, agent behaviors and orchestration layers, this framework shows the 6 layers driving next-gen AI systems. Credit: @goyalshalini #AI #GenerativeAI #AgenticAI...
Machine Learning comes in four fundamental flavors — and mastering them is essential for anyone working with AI. This visual guide breaks down Supervised, Unsupervised, Reinforcement, and Semi-Supervised Learning with clear workflows that show how each approach actually works in...
They SLICED OPEN a "human" on stage... and IT WALKED AWAY. XPENG's IRON robot just shattered the uncanny valley—silicone skin, fluid steps, zero humans inside. Sci-fi is NOW. But is this the future we want? Creepy genius or dystopian nightmare?...

AI isn’t magic. It’s a process. 8 steps: 👉define problem 👉collect/prepare data 👉choose model 👉train 👉evaluate 👉fine-tune 👉deploy 👉ensure ethics & safety Real value comes from running this loop well. #AI #MachineLearning #DataScience #ResponsibleAI https://t.co/5w11ldKd6Q
Building an AI Agent isn’t just about picking a model — it’s a full engineering workflow. This infographic breaks down the complete blueprint, from defining scope and crafting system prompts to choosing the right LLM, adding memory, integrating tools, orchestrating...

Modern AI = 5 pillars 🧠⚙️ ✨ Generative AI (create) 🧩 LLMs (reason) 🔎 RAG (ground + verify) 🤖 AI Agents (act) 🚀 Agentic AI (coordinate + scale) We’re moving from AI that answers → AI that executes outcomes. Which pillar wins next? 👇 #AI #GenerativeAI #LLMs #RAG...

Data buzzwords ≠ same meaning. Here’s a clear map: 🔍 Data Mining = find patterns 📈 Data Analysis = interpret insights 📊 Data Viz = communicate visually 🧠 Data Science = knowledge + ML 🏢 Warehouse (structured) 🌊 Lake (raw at scale) 🏞️ Lakehouse (best of both) 🐊...
Learning AI can feel overwhelming — but when you break it down into clear, structured steps, the journey becomes achievable. This infographic captures a powerful 15-step roadmap, from foundations in math and programming to ML/DL fundamentals, NLP, RL, cloud deployment,...

AI Agent = AI that reasons + plans + uses tools + acts to hit a goal. Loop: goal → sense → plan → tool use → act → eval → memory → improve. Core parts: system prompt, tools/APIs, short- & long-term...
Most people see only the tip of the iceberg when it comes to AI — tools like ChatGPT, generative models, and digital assistants. But beneath the surface lies a far deeper ecosystem: Machine Learning, Deep Learning, Neural Networks, Computer Vision,...

Peeling back the layers of AI! This infographic unravels the complex world from Neural Networks to Deep Learning. Dive deeper into AI with the insights from @ingliguori's 'The Digital Edge' 👉 https://t.co/Nrh6BBTRcF #ArtificialIntelligence #MachineLearning #DeepLearning https://t.co/2q6oTZuXqE
🚀 25 killer AI tools — mapped to the real jobs they help you do. AI is no longer one tool or one category. It’s a growing stack of specialized copilots that are reshaping how we create, research, sell, present,...

Roadmap to master Agentic AI 🧠🤖 Foundations → LLMs → Agent architectures Memory & planning → RAG → Tool use Multi-agent systems → Safety → Deployment → Automation Agentic AI = systems, not prompts. #AgenticAI #AIAgents #GenAI #LLMs #AI https://t.co/BfodYaoFz6
Six dimensions of Data Science — a simple framework to align teams, methods, and outcomes. 1. Goals (Why?) Data Science exists to drive insights, predictions, automation, and optimization — ultimately value creation and better decisions. 2. Methods (How?) Statistics, ML/DL,...

110 AI tools 🤯 But the edge isn’t knowing them all. It’s knowing: • which ones to stack • where they fit in workflows • how they drive outcomes From tool chasing → system building. #AI #GenAI #Automation #Productivity #AIStack https://t.co/VIEK4qz5DX

AI dashboards > spreadsheets 📊🤖 Describe the question AI builds the dashboard You tweak formulas & visuals Insights arrive during the meeting Finance moves from maintenance → decisions. #AI #FPandA #Dashboards #GenAI #DataAnalytics https://t.co/p7AKgRQyQh

For those building agentic AI systems beyond demos: Packt released The Complete Agentic AI Engineering Bundle — a practical, end-to-end set covering autonomous agents, multi-agent workflows, RAG, context engineering, OpenAI Agents SDK, and production architectures. 📚 $9.99 per title | $55.93...

10 most popular Machine Learning models 📊 Linear & Logistic Regression Decision Trees • Random Forest SVM • KNN • Naive Bayes K-Means • PCA • Neural Networks Right model > more data. #MachineLearning #DataScience #AI #ML https://t.co/egQ4x2GyZm

8 types of LLMs used in AI agents 🤖 GPT • MoE • LRM • VLM • SLM • LAM • HRM • LCM Different models for reasoning, perception, planning, and action — not just chat. Agentic AI = model orchestration. #AI #LLMs #AgenticAI...
Quick take: the “best AI” depends on the job, not the logo. Here’s a simple mental model for five popular assistants: ChatGPT — best for creative work + coding + everyday productivity. Strong at brainstorming, drafting, debugging, and building repeatable...

Core AI concepts in one view 🧠 ML, DL, NLP, CV, RL, Robotics, Generative AI — all the building blocks explained. Great refresher for anyone working with AI. Credit in image. #AI #MachineLearning #DeepLearning #GenAI https://t.co/u37wbrc0J6

The Generative AI stack in 4 layers — Infrastructure → Models → Engineering Tools → Apps. A clean breakdown from Gartner showing where value is created in the AI ecosystem. Source: Gartner. #AI #GenAI #TechStack #Innovation https://t.co/9anjXAAYVF

RAG → Self-RAG → Agentic RAG AI retrieval is evolving fast — from simple lookup to autonomous reasoning. Great visual comparing all 3 approaches. Credit in image. #RAG #AgenticAI #AI #LLM #GenAI https://t.co/8NFynMyHJz
AI isn’t just for producing answers — it’s a learning accelerator if you use it deliberately. This framework shows 10 high-leverage ways to learn anything faster with AI: 1. Explain like I’m 5 → simplify tough concepts 2. Examples &...

The 4 types of Machine Learning — Supervised, Unsupervised, Reinforcement, and Semi-Supervised — explained in one clean visual. Great cheat sheet for anyone learning ML or working in AI. Credit in image. #MachineLearning #AI #DataScience https://t.co/1gPD08OMd6

How to build an AI Agent — scope → prompts → LLM → tools → memory → orchestration → UI → testing. Plus a breakdown of the top agent platforms (ChatGPT, Claude, Cursor, n8n, LangGraph, CrewAI, Perplexity, etc.) Great visual guide. Credit in...

People see ChatGPT. But AI is much bigger: ML, DL, Neural Networks, CV, NLP, Predictive Analytics, Speech Recognition, Agentic AI — the full iceberg beneath the surface. Great visual. Credit in image. #AI #ML #DeepLearning #Tech #Innovation https://t.co/lM8eH5TOA5

Data Science works when 6 dimensions align: Goals (value, decisions) Methods (stats, ML/DL, A/B, viz) People (DS+ML+Biz+Domain) Processes (collect→clean→train→deploy→monitor) Tech (Python/R, TF/PyTorch, cloud, SQL/NoSQL, BI) Culture (collab, ethics, learning, experimentation) Models alone aren’t enough. #DataScience #ML #AI

Agentic search = adaptive retrieval loop: 1. Analyze intent 2. Build queries dynamically (not templates) 3. Route across collections/sources 4. Evaluate relevance 5. Iterate until sufficient 6. Generate answer w/ context It’s retrieval + reasoning, repeated. #AgenticAI #RAG #LLM

Free AI/ML learning roadmap: Math → ML/DL → specialization → production. Stats/Linear Alg/Calc: Khan Academy, 3Blue1Brown Practice: Kaggle Learn Core ML: Coursera ML, scikit-learn DL: https://t.co/3GoV9EQdTl, https://t.co/KVrK96vt4H Frameworks: PyTorch/TensorFlow/Keras NLP/LLMs: Hugging Face course CV: OpenCV RL: OpenAI Spinning Up Prod: FastAPI, Streamlit, MLOps guide Build skills that ship. #AI #ML #MLOps #LLM

Peeling back the layers of AI! This infographic unravels the complex world from Neural Networks to Deep Learning. Dive deeper into AI with the insights from @ingliguori's 'The Digital Edge' 👉 https://t.co/Nrh6BBTRcF #ArtificialIntelligence #MachineLearning #DeepLearning https://t.co/8mNht9QunA

AgentOps = MLOps for autonomous AI. 🧠⚙️ To scale agents in production you need the full stack: 🗺️ planning 🧠 memory/context 🤖 execution (tools/APIs/code) 📈 monitoring 🔁 optimization 🛡️ governance 🏗️ infrastructure Agents don’t scale without operations. #AgentOps #AIAgents #AgenticAI #LLMs #Automation
If you’re building with LLMs in 2025, “prompting” is table stakes. LLM engineering is the real differentiator. This infographic nails the 8 skills that separate demos from production-grade AI: 1. Prompt engineering — clarity, constraints, and evaluation-ready prompts 2. Context...

AI agents have levels 📈🤖 1. Rule-based (if-then automation) 2. Tool-using assistants 3. Strategic multi-step agents 4. Context-aware autonomous agents 5. Superintelligent digital personas (theoretical AGI) We’re moving from “AI that responds” → to “AI that executes outcomes.” What level is your org at today? 👇 #AI #AIAgents...
ML isn’t magic — it’s a workflow. 🧠⚙️ 1. understand data 2. choose right algorithm 3. train 4. test 5. optimize 6. deploy + monitor + retrain The winners are the teams who run this loop consistently. #MachineLearning #AI #DataScience #MLops https://t.co/xAkXQC231n
Agentic AI is quickly becoming the next operating layer of business. This “Map of Agentic AI” captures why — and how the stack is evolving from LLMs → agents → multi-agent systems → enterprise-grade ecosystems. What stands out to me...