Bringing Gemini LLM to Your Own Projects:
Leveraging pre-trained LLMs to build applications that can read, understand, and respond naturally. Learning Outcomes: 1. Understand definition of Artificial Intelligence. 2. Differentiate between Conventional AI and Generative AI. 3. Understand definition of Large Language Models (LLM). 4. Identify the capabilities and practical use cases of the Gemini API. #Binusonline #kuliahfleksibelkarirmeningkat #informationsystems
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Don't learn AI Agents without Learning these Fundamentals
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Model Context Protocol (MCP) Explained for Beginners: AI Flight Booking Demo!
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How GPT, Claude, and Gemini are actually trained and served – Reiner Pope
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Designed to Keep You Scrolling: How information systems shape the way we use technology
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Harness Engineering Masterclass: Technical Deep Dive on how to build Agentic Systems
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The AI Breakthrough That Will Change Everything (Google DeepMind CEO Interview)
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Yann LeCun's $1B Bet Against LLMs [Part 1]
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Android 17 sucks. So I put Linux on a phone.
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Andrej Karpathy: From Vibe Coding to Agentic Engineering w/ Stephanie Zhan
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AI Course for Developers – Build AI-Powered Apps with React
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How To Think SO CLEARLY People Assume You're A Genius
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AI-Assisted Coding Tutorial – OpenClaw, GitHub Copilot, Claude Code, CodeRabbit, Gemini CLI
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Keynote: After the AI Hype – What’s Real, and What’s Next - Richard Campbell - 2026
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Complete Agentic AI Course - AI Agents, RAG, Embeddings, Architectures, Framework, VectorDB & Memory
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Full Stack Engineering Course | Build and Deploy a Full Stack PERN Admin Dashboard in 2026
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Creator of C++: Bell Labs, Negative Overhead Abstraction, Mistakes | Bjarne Stroustrup
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Yann LeCun: World Models: Enabling the next AI revolution
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Should You Still Become a Software Engineer in 2026? GitHub VP
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AI Agents Full Course 2026: Master Agentic AI (2 Hours)
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