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๐ฃ๐ฎ๐ถ๐ฑ ๐๐ผ๐๐ฟ๐๐ฒ ๐๐ฅ๐๐ (PART - 1)
1. Artificial Intelligence
2. Machine Learning
3. Prompt Engineering
4. Claude,Chatgpt,Grok
5. Data Analytics
6. AWS Certified
7. Data Science
8. BIG DATA
9. Python
10. Ethical Hacking
(72 Hours only )
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๐น Videos:
1. LLM Introduction: https://t.co/LaZdiMZHtp
2. LLMs from Scratch: https://t.co/0egvjTkEMh
3. Agentic AI Overview (Stanford): https://t.co/ZMiX8zvSsA
4. Building and Evaluating Agents: https://t.co/1U6NiPHad8
5. Building Effective Agents: https://t.co/BzSjqqwHQE
6. Building Agents with MCP: https://t.co/YBlBFJR8XQ
7. Building an Agent from Scratch: https://t.co/X65T8NT4qs
8. Philo Agents: https://t.co/eoc3rWdtdW
๐๏ธ Repos
1. GenAI Agents: https://t.co/WmleAGvACF
2. Microsoft's AI Agents for Beginners: https://t.co/VZACPfY1yP
3. Prompt Engineering Guide: https://t.co/bYR2qde6jQ
4. Hands-On Large Language Models: https://t.co/LJYu8hALXw
5. AI Agents for Beginners: https://t.co/VZACPfY1yP
6. GenAI Agents: https://t.co/rp4JNSVeI6
7. Made with ML: https://t.co/ccmxen8N0s
8. Hands-On AI Engineering: https://t.co/J6sQ2p0vkl
9. Awesome Generative AI Guide: https://t.co/3mmQI9Ille
10. Designing Machine Learning Systems: https://t.co/Kxeywa3EZQ
11. Machine Learning for Beginners from Microsoft: https://t.co/zr9kZJttNY
12. LLM Course: https://t.co/hHkSpVE3qf
๐บ๏ธ Guides
1. Google's Agent Whitepaper: https://t.co/nNf2owXvkQ
2. Google's Agent Companion: https://t.co/6juFTnEHlM
3. Building Effective Agents by Anthropic: https://t.co/1wElNyKUBd
4. Claude Code Best Agentic Coding practices: https://t.co/ydrbzZTcxC
5. OpenAI's Practical Guide to Building Agents: https://t.co/sFUdJ67EgQ
๐ Books:
1. Understanding Deep Learning: https://t.co/2pIGN8smiB
2. Building an LLM from Scratch: https://t.co/zxcmNXVmJ5
3. The LLM Engineering Handbook: https://t.co/vCScRvHbZT
4. AI Agents: The Definitive Guide - Nicole Koenigstein: https://t.co/FrQPw3ceRk
5. Building Applications with AI Agents - Michael Albada: https://t.co/2a9z0SYrlf
6. AI Agents with MCP - Kyle Stratis: https://t.co/giXPpKPZIW
7. AI Engineering: https://t.co/ZEmTMCTyRF
๐ Papers
1. ReAct: https://t.co/SuTQUbTF8h
2. Generative Agents: https://t.co/dmJRvddHGw
3. Toolformer: https://t.co/hjrMLeCJpE
4. Chain-of-Thought Prompting: https://t.co/LizaUQUf8r
๐ง๐ซ Courses:
1. HuggingFace's Agent Course: https://t.co/eQ9eESEppl
2. MCP with Anthropic: https://t.co/91kuLEzUh9
3. Building Vector Databases with Pinecone: https://t.co/LVZl6z3vPF
4. Vector Databases from Embeddings to Apps: https://t.co/pgT6RtKLhU
5. Agent Memory: https://t.co/zkQy6NwLAI
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AI agents built into cloud apps will always be sub-par compared to state of the art agents that you can use with Obsidian.
This is because your Obsidian data is in your control, in plain text formats that are ideal for LLMs to process.
You can choose to run any of the best-in-class agents and AI tools: Claude, Gemini, OpenAI Codex, etc. You also have the choice to run local models and privacy-preserving options. Or you can use none of these and enjoy not seeing sparkly AI buttons everywhere.
Remember that cloud-based note-taking apps are not end-to-end encrypted. This is inherently less secure, open to breaches, and allows companies to make your private thoughts part of their training set.
Obsidian is not working on adding agents because we don't have to. We'll let the AI labs focus on that. We're 4 developers and our focus is on making great editing tools for plain text Markdown files that are always in your control.