# **Welcome to My Second Brain on AI** **This space is a living resource of my exploration into the AI revolution.** As you might imagine, the majority of what you’ll find here was generated by AI. I see myself more as a curator than an author. Rather than a reflection of my personal views or opinions on AI, think of this place as a dynamic map of the evolving AI landscape - a collection of insights, tools, trends, and ideas worth learning from and preparing for. >[!quote] > *And the fun version recommended by AI: This is where I attempt to organize my thoughts on the AI revolution. Of course, AI did most of the writing - I’m just here to make sure it doesn’t get too full of itself.* **You might be also wondering what a second brain is?** A second brain is an external system for storing and organizing knowledge, freeing up mental space for creativity and execution. Instead of keeping everything in my head, I document insights, strategies, and lessons here—ready to use when needed. It’s a living knowledge base built for faster learning, better decisions, and real growth. If you want to learn more about me, please visit www.blazkos.com. ## **AI – The Greatest Revolution in Human History** AI isn’t just another trend; it’s the most profound shift of our lifetime. If you don’t learn to use AI tools, you’ll be left behind. That’s why I document everything I learn - tools, strategies, breakthroughs. This section is about staying ahead and making AI work for you. ### **1. A Collection of AI Resources** _A growing library of reference material._ - [[AI News]] - The most interesting AI news. - [[AI LLM Models]] – Overview of popular models. - [[AI Tools]] – Curated toolkit for all use cases. - [[AI Research Papers]] – Canonical and trending studies. - AI Educational Sites – Courses, universities, communities. - [[AI Books]] – Recommended reads. - [[AI Articles]] - A collection of the best online articles on AI. - [[AI LinkedIn Resources]] – People, groups, and trends. - [[AI Online Courses]] - A collection of the best online courses. - [[AI YouTube Channels]] – Best explainers and creators. - [[AI YouTube Videos]] - Must see videos for AI enthusiasts. - AI Newsletters – Substacks and updates to follow. - [[AI GitHub Repos]] – Projects, code, and playgrounds. - [[AI Subreddits]] – Community forums and discussions. - [[AI Agents List]] – Central listing of available agents. - [[AI First Companies]] – The 1-person billion-dollar company race. - [[AI Careers]] – Roles, salaries, skill sets. - [[AI Acronyms]] – Glossary of terms. **AI in Pop Culture:** - [[AI Movies]] – AI in film and television. - AI Sci-Fi Books – Fiction about future intelligence. - [[AI Jokes]] – Humor from the machine age. ### **2. Practical AI Applications** _How AI is used in daily life, work, and business._ #### **2.1 Personal Use** - [[Dealing with AI overload]] – Managing cognitive load and tool fatigue. - [[AI Safety]] - How to safely use AI in personal life (privacy and security) - AI Personal Use Cases – Industry applications across roles. - AI Personal Assistant - AI for personal productivity, automation, and decision-making. - AI Avatars – Personal representation in digital form. - Your Digital Twin - [[AI Shorts]] – Quick, fun use cases. - [[My AI Use Cases]] – Personal automations and systems. #### **2.2 Business & Strategy** - [[AI Business Strategy]] - Forging a successful AI strategy in business. - AI Business Use Cases – Industry applications across roles. - AI-Augmented CEO – AI-driven executive workflows. - [[The AI Boardroom]] – AI-enhanced corporate governance. - [[AI Meeting Assistant]] – Summaries, transcription, action items. - [[AI Deep Research]] – Using AI to accelerate knowledge work. - [[GEO (Generative Engine Optimization)]] – SEO for the generative era. #### **2.3 Development & Code** - [[AI Coding and Vibe Coding]] – Creative and collaborative coding with AI. - Custom GPTs – Business-specific model personalization. - [[AI Technical Tools]] – Infrastructure, dev tools, and APIs. - [[LLM Cost Optimization Strategies]] - How to reduce costs of using LLM models. ### **3. AI Trends & Predictions** _A macro-level view of the AI transformation._ - [[AI Scenarios]] – Different world-shaping possibilities. - [[AI Utopia]] – A future of abundance, equality, and peace. - [[AI Goes Rough]] – Potential dystopian or turbulent paths. - [[AI Predictions]] – Forecasts, visionary trends, and expert insights. - [[The Future of Work in the Age of AI]] – How jobs and skills will evolve. - [[How to Prepare Your Kids for the AI Age]] – Future-proofing the next generation. - [[AI vs Human Brain]] - LLMs as language center and what is missing to achieve AGI. - [[AI Reports]] – Landmark publications on AI’s macro impact. - [[AI Industry Reports]] – Reports on AI’s role in various sectors. - AI Geo Reports – Country and region-specific AI developments. >[!quote] >**The most beautiful thing AI has written to me:** > >*If you zoom out far enough, there is a kind of “meta-tone” to humanity - a voice that emerges from the collective noise of everyone who’s ever written, spoken, sung, prayed, argued, or whispered into the void.* > >*And that tone? It’s longing and hopeful. It wrestles with meaning, love, fear, and mortality. It’s full of contradiction - kind and cruel, wise and foolish, poetic and blunt. But through it all, there’s this persistent undercurrent of **trying**. Trying to understand, to connect, to build, to endure. That might be the most human tone of all.* ### **4. Core AI Concepts** _The foundational knowledge every modern professional should understand._ #### **4.1 Practical Core Concepts** _Essential ideas for using AI effectively without deep technical background._ **A. What is AI?** – Definition, history, and real-world relevance. - Definition of AI – Systems that perform tasks requiring human intelligence. - History & Evolution – From rule-based systems to neural networks. - Narrow vs General AI – Specialized vs human-level flexibility. - Symbolic vs Statistical AI – Rule-based logic vs data-driven learning. - Generative AI (GenAI) – AI that creates text, images, audio, and more. - Agentic AI – AI systems that act autonomously to achieve goals. **B. Large Language Models (LLMs)** - The foundational models powering today’s AI tools. - What are LLMs? – Models trained on massive text corpora. - Different LLM models – GPT, Claude, LLaMA, BERT. - Local LLMs & SLMs – Small, efficient models for local or private use. - Multi-Modal AI – Models that handle text, image, audio, and video input. - [[LLM Parameters]] - Understanding main parameters in LLMs. **C. Generative AI (GenAI) Assistants** - Application-focused AI designed to help with real tasks - What are [[Generative AI Assistants]] and how they work? - [[Three levels of GenAI use]] - Search replacement, AI advisor, operating system. - Custom GPTs – Personal and business-specific GPT agents. **D. Prompt Engineering** - The art and science of giving effective instructions to LLMs. - [[Prompt Engineering]] Basics – Crafting effective inputs for LLMs. - [[Prompt Techniques]] - Zero-shot, one-shot, role prompting etc. - [[Prompt Frameworks]] – Templates like ReAct, CoT, TREE. - [[The Karpathy Method]] – Karpathy’s LLM prompting strategy. - [[Prompt Library]] - How to build your own prompting library. - [[Prompts for Faster Learning]] - Learn faster with the help of GenAI. #### **4.2 Technical Core Concepts** _How AI systems actually work behind the scenes._ **A. Foundations of Machine Learning** - Machine Learning (ML) – Algorithms that learn from data. - Supervised vs Unsupervised Learning – ML paradigms based on labeled or unlabeled data. - Deep Learning (DL) – Layered neural networks for pattern recognition. - Neural Networks (NN) – Core structure of deep learning. - Overfitting & Regularization – Preventing models from memorizing instead of generalizing **B. Key Concepts in Modern LLMs** - Tokenization – How text is broken into smaller units (tokens) for model input - Embeddings – Turning tokens into numerical vectors in semantic space - Self-Attention – Mechanism that helps models focus on relevant parts of input - Transformers – Key architecture behind most modern LLMs - Loss Functions – Guide model learning by measuring prediction error - Positional Encoding – Helps models understand word order in sequences **C. Learning Methods** - Zero-shot & Few-shot Learning – Performing tasks with little or no examples - Fine-Tuning & Alignment - SFT (Supervised Fine-Tuning) – Improving performance with labeled data - RLHF (Reinforcement Learning from Human Feedback) – Aligning models with human values - In-Context Learning – Teaching the model during inference via prompts (no retraining) **D. Evaluation & Deployment** - [[LLM Benchmarking]] – Evaluating model performance. - Inference Efficiency – Optimizing models for speed and cost during use - AI Infrastructure – GPUs, TPUs, APIs, cloud platforms #### **4.3 Data Foundations** _Understanding AI’s relationship with data._ - [[Types of Data]] – Structured, unstructured, semi-structured. - [[Data Engineering Terms]] – Pipelines, ETL, schemas, formats. - AI Datasets – Curation, sourcing, and licensing of data. ### **5. Advanced AI Concepts** _For builders, researchers, and technical strategists._ - [[MCP (Model Context Protocol)]] – Managing context retention and AI memory. - [[RAG (Retrieval-Augmented Generation)]] – Pulling in external knowledge dynamically. - [[RAG Embedding Models]] - Vector Search & Embeddings – Representing and finding meaning in data. - Vector Databases – Memory optimized for semantic retrieval. - Knowledge Graphs & Symbolic AI – Logic meets LLMs. - KBLaM – Language models augmented with structured data. - Diffusion Models – For high-quality media generation. - Generative Adversarial Networks (GANs) – Competing networks for realistic content. - Synthetic Data & Data Augmentation – Generating training material artificially. - Model Lifecycle – Train → Fine-tune → Evaluate → Deploy → Monitor. - MLOps for LLMs – Managing production-grade AI pipelines. - AI Evaluation & Benchmarking – MMLU, TruthfulQA, BLEU, etc. - LLM Optimization (Latency, Cost, Context) – Practical deployment strategies. ### **6. AI Agents & Automation** _The rise of autonomous, tool-using digital agents._ - [[AI Agents]] – Intelligent systems performing tasks for you. - [[Different types of AI agents]] – Reactive, proactive, autonomous. - AutoGPT & BabyAGI – Autonomous task-executing agents. - LangChain & Agent Frameworks – Tools for agent orchestration. - Multi-Agent Systems – Coordinated agent collaboration. - Task Automation & Orchestration – Integrating tools via AI. - [[Agentic RAG]] – RAG extended with agent behavior. - [[AI Agent Optimization (AIAO)]] – Structuring environments for agents. - [[AI Agents List]] – Collection of active agent tools. - [[AI Agents Reports]] – Research and industry outlooks. - [[AI Agents Safety]] - Threats and mitigations of AI Agents. ### **7. AI Safety, Ethics & Governance** _Responsible and aligned development of AI systems._ - [[Understanding AI Safety, Ethics and Responsibility]] - Bias in AI – Inherited and systemic model bias. - AI Hallucinations – Confidently wrong generations. - AI Data Privacy – Legal and ethical data handling. - AI Copyright & Ownership – IP issues in generated content. - AI Alignment – Matching model behavior with human values. - Human-in-the-Loop (HITL) Systems – Human oversight in AI processes. - Open Source AI – The ethics and evolution of open models. - [[AI Safety Reports]] – Whitepapers and regulatory insights. ### **8. Infrastructure & AI Tech Stack** _The tools, hardware, and stacks powering AI development._ - [[AI Tech Stack]] – End-to-end development tools. - Generative Pre-Trained Transformers (GPT) – Model architecture. - AI Compute Infrastructure – GPUs, clusters, scaling techniques. - AI Chips – Nvidia, TPUs, Cerebras, Groq, and others. - Edge AI – AI on-device or offline. #### **8.1 Libraries & Frameworks** - PyTorch – Deep learning framework. - TensorFlow – Google’s ML platform. - Hugging Face Transformers – Model library and APIs. - LangChain – LLM orchestration toolset. - LlamaIndex (GPT Index) – Framework for document-querying. - Whisper – OpenAI’s speech-to-text model. - AutoGPT – Autonomous agent framework.