Artificial Intelligence

AI is finally behaving like a Human💀

Learn AI for free directly from top companies. 1 - Anthropic: anthropic.skilljar.com 2 - Google: grow.google/ai 3 - Meta: ai.meta.com/resources/ 4 - NVIDIA: developer.nvidia.com/cuda 5 - Microsoft: learn.microsoft.com/en-us/training/ 6 - OpenAI: academy.openai.com 7 - IBM: skillsbuild.org 8 - AWS: skillbuilder.aws 9 DeepLearning. AI: deeplearning.ai 10 - Hugging Face: huggingface.co/learn

These YouTube channels will guide you step by step to master AI Whether you're building Agentic AI workflows or just starting with Python, this llist covers everything: 1. Agentic AI & ChatGPT Watch creators like Krish Naik and Freecodecamp for hands-on projects with GPT, LLMs & automation tools. 2. Machine Learning & Deep Learning Explore channels like StatQuest, Yannic Kilcher and DeepLearningAI for algorithm intuition & implementation walkthroughs. 3. Python & Data Science From Corey Schafer to Ken Jee, these channels help you master the foundations of Python, analytics and deployment. 4. Math for AI Need help with the math behind the models? Start with 3Blue1Brown, Khan Academy & PatrickJMT. 5. Bug data and Data Engineering Follow Alex The Analyst, edureka, Data Engineering to scale your AI pipelines and manage large datasets. 6. Tools, Productivity & AI Assistants Stay up to date with Ansh Mehra, Jeff Su, Skill Leap AI & Howtoai to explore ChatGPT hacks, AI agents & productivity automation.

Weekly AI News 🚀 _This Week in Artificial Intelligence_ The AI industry is moving faster than ever. Here's your quick summary of the biggest announcements, product launches and industry shifts from this week. 🔥 Top Headlines 1. Anthropic launches Claude Sonnet 5 Anthropic introduced Claude Sonnet 5, bringing major improvements in coding, reasoning, and AI agent capabilities, positioning it as one of the strongest frontier models available. 2. U.S. lifts restrictions on Claude Fable 5 The U.S. government has removed export restrictions on Claude Fable 5 after Anthropic implemented additional safety protections, allowing the model to return globally. 3. Anthropic may introduce KYC for advanced AI access Reports suggest Anthropic is considering stronger identity verification (KYC) for access to its most powerful AI models as governments increase oversight. 4. xAI launches Grok 4.5 Beta Elon Musk's xAI has officially launched the beta version of Grok 4.5, adding stronger reasoning, coding, and research capabilities. 5. China's 360 Security unveils Mythos competitor Chinese AI competition continues to accelerate, with 360 Security announcing a new frontier model aimed at competing with Anthropic's latest offerings. 6. OpenAI teases Codex-branded hardware OpenAI has hinted at a new Codex-branded hardware product, signaling that the company is expanding beyond software into AI-first devices. 7. OpenAI discovers a breakthrough to reduce inference costs by nearly 50% Researchers at OpenAI reportedly developed a new inference optimization technique that could dramatically reduce AI operating costs, making large-scale deployment significantly cheaper. 8. Google unveils Nano Banana 2 Lite Google announced Nano Banana 2 Lite, bringing faster image generation with lower compute requirements while maintaining impressive quality. 9. Codex gets an effort selector OpenAI is testing a new slider inside Codex that lets users choose how much reasoning effort an AI agent should spend before responding. 10. Amazon engineers are distilling Claude Amazon engineers are reportedly using model distillation techniques to build smaller, faster, and cheaper AI models inspired by Claude for internal applications. 11. Claude is helping build the next Claude Anthropic CEO revealed that engineers now use Claude extensively while developing future versions of Claude, accelerating AI-assisted software engineering. 12. 50% of Claude users believe AI can already perform half of their work A recent survey shows growing confidence in AI productivity, with half of Claude users believing today's AI can already complete around 50% of their daily tasks. 13. Managing AI agents may become the most valuable skill by 2031 According to KPMG, organizations will increasingly value professionals who can effectively manage teams of autonomous AI agents. 14. Meta is testing its own coding assistant — MetaCode Meta has begun testing an internal coding assistant called MetaCode, joining the rapidly growing AI coding race. 15. NotebookLM now creates 60-second Shorts Google's NotebookLM can now automatically generate short-form vertical videos from research notes and documents. Other Important Updates • Mistral launched OCR 4 supporting over 170 languages. • ByteDance introduced Seedance 2.5 capable of generating 30-second 4K AI videos. • Runway launched Agent 2.0 for AI-powered marketing workflows. • Adobe acquired AI image enhancement company Topaz Labs. • New reports show more paid AI users are choosing Claude over competing assistants. • Cursor announced its first fully self-trained AI model and acquired Continue. • Meta plans to automate nearly half of its moderation requests using LLMs by 2025.

Want to become an AI Engineer in 2026? Don't start with LangChain. Don't start with Pinecone. Don't even start with AI Agents. Start by understanding this roadmap. 👇 Most people jump straight into building AI apps. They copy tutorials. They connect APIs. They launch chatbots. But when something breaks... They have no idea why. Because they skipped the fundamentals. Here's the AI ecosystem every aspiring AI Engineer should understand. 🧠 1. LLMs – The Brain These power reasoning, coding, conversations, and content generation. Examples: • GPT • Claude • Gemini • Llama 4 • Qwen 3 • DeepSeek • Mistral • Gemma 3 • Phi-4 👉 Learn what each model is good at and when to use it. ⚡ 2. Frameworks – The Orchestrator Frameworks connect LLMs with tools, APIs, memory, and workflows. Popular choices: • LangChain • LlamaIndex • Haystack • txtai 👉 These help you build production-ready AI applications. 📚 3. Vector Databases – AI Memory LLMs don't remember your documents. Vector databases do. Popular options: • Pinecone • Chroma • Qdrant • Weaviate • Milvus • PostgreSQL (pgvector) • Cassandra • OpenSearch 👉 Essential for Retrieval-Augmented Generation (RAG). 📄 4. Data Extraction – Feed Your AI Before AI can answer questions... It needs clean, structured data. Tools include: • Crawl4AI • FireCrawl • ScrapeGraphAI • MegaParser • Docling • LlamaParse • ExtractThinker 👉 Great AI starts with great data. 🚀 5. Open LLM Access Experiment, self-host, and deploy open-source models with: • Hugging Face • Ollama • Groq • Together AI 👉 Perfect for local development and production deployments. 🔍 6. Text Embeddings – The Search Engine Embeddings convert text into vectors that AI can understand and retrieve. Popular providers: • OpenAI • Voyage AI • Google • Cohere • Nomic • SBERT 👉 The quality of your embeddings directly impacts your RAG system. 📊 7. Evaluation – The Most Overlooked Layer A good AI app isn't the one that looks smart. It's the one that's measurably reliable. Evaluate: ✅ Accuracy ✅ Hallucinations ✅ Retrieval quality ✅ Response consistency Tools like Giskard and DeepEval help you build AI you can trust. If I were starting from scratch today, I'd learn in this order: 1️⃣ LLM Fundamentals 2️⃣ Prompt Engineering 3️⃣ Embeddings 4️⃣ Vector Databases 5️⃣ RAG 6️⃣ AI Frameworks 7️⃣ AI Agents 8️⃣ Evaluation Master these, and you'll understand how modern AI systems are actually built. Not just how to copy them. ❤️ Save this roadmap. 🔁 Share this so someone preparing for an AI job in 2026 doesn't waste months learning the wrong things. One share could help a student, developer or job seeker understand the AI stack that companies are actually hiring for. Follow us: https://t.me/Artificial_intelligence_in