20 must-try AI tools for 2025. This stack covers everything: chat & reasoning image gen video gen voice/music AI search automation AI agents My top 3 right now: ChatGPT, Perplexity, Midjourney. What are yours? #AI #AITools #GenerativeAI #Automation
@ingliguori
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XPENG’s IRON Robot: Humanoid AI That Challenges The Uncanny Valley
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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.
— Giuliano Liguori (@ingliguori) 6 janvier 2026
Sci-fi is NOW. But is this the future we want? Creepy genius or dystopian nightmare? Watch & tell me: Real robot or… pic.twitter.com/49Nv7K7Nl1They 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? Watch & tell me: Real robot or
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AI Development: 8-Step Process for Creating Real Value
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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 -
Five Pillars of Modern AI: From Answers to Execution
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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
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Understanding Data Buzzwords: Mining, Analysis, Visualization, and Science
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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) Swamp
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9 Steps to Build Functional AI Agents from Scratch
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How to build AI agents from scratch (9 steps): 1. Purpose & scope 2. I/O schemas 3. System instructions 4. Reasoning + tools 5. Multi-agent orchestration 6. Memory & context 7. Multimodal 8. Structured outputs 9. UI / API Ship agents that do work, not just talk.
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AI Agents: Reasoning, Planning, and Tool Use for Goal Achievement
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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 memory, adaptive traits. Shift: chatbots give answers; agents deliver -
Exploring AI Layers: Neural Networks and Deep Learning Fundamentals
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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' http://
bit.ly/3u4pILl #ArtificialIntelligence #MachineLearning #DeepLearning -
AI-Powered Learning: Master Anything Faster with Smart Techniques
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How to learn anything faster with AI Explain it like I’m 5
Examples & analogies
Quizzes, mind maps, role-play
Expert debates + feedback loops AI = tutor, coach, and study partner. #AI #Learning #Productivity #GenAI -
Machine Learning Success: Mastering the Consistent Workflow Loop
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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