AI Agents = LLMs + orchestration Here are the main types of LLMs powering them General-purpose (GPT, Claude) Domain-specific (Legal, Finance, etc.) RAG-based (real-time knowledge) Tool-augmented (API actions) Open-source (LLaMA, Mistral) The game is no
@ingliguori
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Large Concept Models: AI Architecture Beyond Token-Level Processing
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Large Concept Models (LCM): A New Frontier in AI Beyond Token-Level Language Models https://
linkedin.com/pulse/large-co
ncept-models-lcm-new-frontier-ai-beyond-giuliano-liguori–dnj3f
… via @ingliguori -
Beyond Prompts: Building Scalable LLM Systems
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Most people are using LLMs wrong. They focus on prompts.
Winners focus on systems. Shift from: One-shot prompts
to Iterative AI workflows Generic models
to Task-specific model selection Simple answers
to Structured execution plans LLMs scale when: • -
AI Agent Levels: From Automation to Superintelligent Personas
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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
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AgentOps: Full Stack for Scaling Autonomous AI Agents
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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
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AI Mastery Roadmap: Your Complete Guide to Artificial Intelligence
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AI Mastery Roadmap – Your GPS to mastering artificial intelligence in 2025 and beyond. Whether you're a beginner or aiming to become an AI architect, this roadmap from Mindstream is one of the clearest, most actionable visual guides I’ve seen. It walks you step-by-step from:
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Free AI/ML Learning Roadmap: Math to Production
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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: http://
DeepLearning.AI, http://
fast.ai Frameworks: PyTorch/TensorFlow/Keras -
Beyond ChatGPT: The Full Spectrum of AI Technologies Explained
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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 -
AI Transforms Higher Education: SJTU-Huawei Smart Campus
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AI is reshaping higher education—not just in theory, but in practice. The collaboration between SJTU and Huawei shows what this looks like: AI-driven research at scale hands-on, project-based learning fully digitalized smart campuses At the core: the Zhiyuan-1 platform
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Six Dimensions Aligning Data Science Success Framework
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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,