AI Technology | Claude & ChatGPT Prompts

🔥 5 Free Courses to Go From LLM Beginner to Practitioner Want to go from LLM beginner → understanding → building → fine-tuning → deploying AI agents? Here are 5 free courses to follow 👇 1️⃣ Neural Networks: Zero to Hero — Andrej Karpathy Learn neural networks and language models from scratch. Build micrograd, language models, GPT, and tokenizers with hands-on coding. 🔗 Course: https://karpathy.ai/zero-to-hero.html 2️⃣ Full Stack LLM Bootcamp — Full Stack Deep Learning Learn how to build production-ready LLM applications, including prompting, evaluation, LLMOps, deployment, and system design. 🔗 Course: https://fullstackdeeplearning.com/llm-bootcamp/ 3️⃣ Stanford CS336 — Language Modeling from Scratch Go deeper into how LLMs are actually built — data processing, tokenization, transformers, training, scaling, evaluation, and alignment. 🔗 Course: https://cs336.stanford.edu/ 4️⃣ Hugging Face LLM Course Learn practical LLM development with Transformers, Datasets, tokenization, fine-tuning, LoRA, SFT, DPO, and GRPO. 🔗 Course: https://huggingface.co/learn/llm-course 5️⃣ DeepLearning.AI — AI & LLM Short Courses Learn practical topics like AI agents, LangGraph, RAG, model serving, and production AI workflows. 🔗 Courses: https://www.deeplearning.ai/courses/ 📌 Save this post for later!

🔥 𝐓𝐨𝐩 𝟏𝟐 𝐀𝐈 𝐆𝐢𝐭𝐇𝐮𝐛 𝐑𝐞𝐩𝐨𝐬𝐢𝐭𝐨𝐫𝐢𝐞𝐬 𝐭𝐨 𝐄𝐱𝐩𝐥𝐨𝐫𝐞 𝐢𝐧 𝟐𝟎𝟐𝟔 1️⃣ OpenClaw — Build personal AI agents that can run on your device. 🔗 https://github.com/openclaw/openclaw 2️⃣ n8n — Build powerful visual automation workflows with AI. 🔗 https://github.com/n8n-io/n8n 3️⃣ Ollama — Run LLMs locally and experiment with open models. 🔗 https://github.com/ollama/ollama 4️⃣ Langflow — Build AI applications and agent workflows visually. 🔗 https://github.com/langflow-ai/langflow 5️⃣ Dify — Create and deploy production-ready AI applications. 🔗 https://github.com/langgenius/dify 6️⃣ LangChain — Build applications powered by LLMs, tools, and agents. 🔗 https://github.com/langchain-ai/langchain 7️⃣ Open WebUI — Self-hosted interface for interacting with AI models. 🔗 https://github.com/open-webui/open-webui 8️⃣ DeepSeek-V3 — Explore DeepSeek’s open AI model ecosystem. 🔗 https://github.com/deepseek-ai/DeepSeek-V3 9️⃣ Gemini CLI — Work with Gemini directly from your terminal. 🔗 https://github.com/google-gemini/gemini-cli 🔟 RAGFlow — Explore advanced RAG workflows for real-world applications. 🔗 https://github.com/infiniflow/ragflow 1️⃣1️⃣ Claude Code — Explore agentic AI coding workflows. 🔗 https://github.com/anthropics/claude-code 1️⃣2️⃣ CrewAI — Build multi-agent systems where specialized agents collaborate. 🔗 https://github.com/crewAIInc/crewAI 📌 Save this list and pick one repository to explore today. #AI #GitHub #AIAgents #LLM #RAG #GenerativeAI #MachineLearning #OpenSource ❤️ Follow AIJobs for more AI drops

5 FREE AI Courses You Should Know About 1️⃣ Introduction to AI for Work — DataCamp New to AI? This beginner-friendly course explains AI, machine learning, Generative AI, and LLMs with a focus on practical workplace use. No technical background required! 👉 Best for: Students, managers, marketers & beginners 🔗 Click Here: https://www.datacamp.com/courses/introduction-to-ai-for-work 2️⃣ Easy-Vibe AI Coding Guide from Scratch Want to build apps with AI? Learn vibe coding, frontend, backend, databases, deployment, AI agents, and AI coding tools through a practical, project-focused approach. 👉 Best for: Beginners, founders, creators & product managers 🔗 Click Here: https://datawhalechina.github.io/easy-vibe/en/ 3️⃣ LLM Course — Maxime Labonne A powerful free roadmap for learning Large Language Models. Explore LLM fundamentals, fine-tuning, quantization, evaluation, deployment, and LLM engineering. 👉 Best for: Developers, ML learners & AI builders 🔗 Click Here: https://github.com/mlabonne/llm-course 4️⃣ LLM Zoomcamp — DataTalks.Club Learn by building real-world LLM applications! Cover RAG, vector search, embeddings, AI agents, function calling, evaluation, monitoring, and reranking. 👉 Best for: Software engineers, data engineers & ML learners 🔗 Click Here: https://github.com/DataTalksClub/llm-zoomcamp 5️⃣ Hugging Face LLM Course Learn LLMs and NLP using the Hugging Face ecosystem. Explore Transformers, Datasets, Tokenizers, fine-tuning, model sharing, and advanced LLM concepts. 👉 Best for: AI developers & learners interested in open-source LLMs 🔗 Click Here: https://huggingface.co/learn/llm-course/chapter1/1 ❤️ Follow AIJobs  for more AI drops

🔥 10 YouTube Channels Keeping You Ahead in AI 1️⃣ Two Minute Papers Complex AI research explained in simple, visual, and exciting videos. Perfect for discovering the latest breakthroughs. 👉 Click Here: Two Minute Papers 2️⃣ Yannic Kilcher Want to understand AI papers in depth? Yannic breaks down models, mathematics, architectures, and research methodology. 👉 Click Here: Yannic Kilcher 3️⃣ AI Jason Learn how to build practical AI applications, AI agents, RAG systems, and multi-agent workflows with real-world examples. 👉 Click Here: AI Jason 4️⃣ AssemblyAI A great resource for developers building with LLMs, speech AI, RAG, vector databases, and modern AI APIs. 👉 Click Here: AssemblyAI 5️⃣ Sentdex Learn Python, Machine Learning, Deep Learning, and AI by actually building projects from the ground up. 👉 Click Here: Harrison Kinsley's Sentdex 6️⃣ Andrej Karpathy Learn AI from first principles with one of the most respected AI engineers. His Neural Networks: Zero to Hero series is a must-watch. 👉 Click Here: Andrej Karpathy 7️⃣ StatQuest Confused by statistics or Machine Learning? StatQuest makes difficult concepts simple, visual, and easy to remember. 👉 Click Here: StatQuest with Josh Starmer 8️⃣ DeepLearning.AI Learn Machine Learning, Deep Learning, Generative AI, and AI concepts through structured educational content. 👉 Click Here: DeepLearning.AI 9️⃣ AI Explained Stay updated on new AI models, benchmarks, research, and industry developments without the hype. 👉 Click Here: AI Explained 🔟 Matt Wolfe Discover the latest AI tools, apps, startups, automation platforms, and productivity tools before they become mainstream. 👉 Click Here: Matt Wolfe #AI #ArtificialIntelligence #MachineLearning #ChatGPT #GenerativeAI #LLM #AIAgents #DeepLearning #Python #AITools ❤️ Follow AIJobs  for more AI drops

5 Free Courses to Go From AI Beginner to Practitioner 1️⃣ Harvard CS50: Introduction to AI with Python 🎓 Learn the fundamentals of AI before diving into machine learning. Build AI projects like Tic-Tac-Toe, search algorithms, and logic solvers while mastering core AI concepts. 👉 Click Here: https://cs50.harvard.edu/ai/ 2️⃣ Google Machine Learning Crash Course 📊 Google's official ML course teaches gradient descent, TensorFlow, feature engineering, and model training with interactive lessons used by Google engineers. 👉 Click Here: https://developers.google.com/machine-learning/crash-course 3️⃣ fast.ai – Practical Deep Learning for Coders 💻 Build real deep learning models from the very first lesson. Learn computer vision, NLP, PyTorch, and deploy AI applications with practical projects. 👉 Click Here: https://course.fast.ai/ 4️⃣ Hugging Face NLP Course 🤖 Master Transformers, LLMs, and modern Generative AI. Learn to fine-tune open-source models using the Hugging Face ecosystem. 👉 Click Here: https://huggingface.co/learn/nlp-course 5️⃣ Andrej Karpathy – Neural Networks: Zero to Hero 🧠 Build neural networks and a mini GPT completely from scratch. One of the best free resources to deeply understand how LLMs actually work. 👉 Click Here: https://www.youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ ❤️ Follow AIJobs  for more AI drops

🚀 5 FREE Resources to Master Agentic AI 📘 Microsoft AI Agents for Beginners Learn AI agents, RAG, MCP, memory, and multi-agent systems with hands-on Python examples. 👉 Click Here: https://github.com/microsoft/AI-For-Beginners/tree/main/12-building-ai-agents 🤗 Hugging Face AI Agents Course Build real-world AI agents using LangGraph, LlamaIndex, and other popular frameworks. 👉 Click Here: https://huggingface.co/learn/agents-course 🧠 Anthropic – Building Effective Agents Learn proven agent design patterns, workflows, routing, and evaluation strategies. 👉 Click Here: https://www.anthropic.com/engineering/building-effective-agents 📚 Multiagent Systems (Free Book) Understand the theory behind agent coordination, negotiation, and decision-making. 👉 Click Here: https://www.masfoundations.org 🔍 Google & Kaggle Agents Whitepaper Series Learn agent architectures, MCP, memory, evaluation, and production deployment. 👉 Click Here: https://www.kaggle.com/whitepaper-agents ❤️ Follow AIJobs  for more AI drops

Best YouTube Channels to learn AI in 2026 1. AI Explained 👉 http://youtube.com/@aiexplained-o… 2. Andrej Karpathy 👉 https://www.youtube.com/@AndrejKarpathy 3. Cole Medin 👉 http://youtube.com/@WesRoth/ 4. DeepLearningAI 👉 http://youtube.com/@Deeplearninga… 5. Futurepedia 👉 http://youtube.com/@futurepedia_i… 6. Matthew Berman 👉 http://youtube.com/@matthew_berma… 7. Skill Leap AI 👉 http://youtube.com/@SkillLeapAI/f… 8. Tech With Tim 👉 http://youtube.com/@TechWithTim/v… 9. Tina Huang 👉 http://youtube.com/@TinaHuang1/vi… 10. Two Minute Papers 👉 http://youtube.com/@TwoMinutePape ❤️ Follow AIJobs  for more AI drops

🚀 7 Real-World Python Projects You Can Build in 2026 1. AI Scam & Notice Checker Detect scam SMS, phishing messages, and fake notices with AI. 📖 https://huggingface.co 2. Multi-Agent Research Assistant Build AI agents that research the web and generate reports. 📖 https://machinelearningmastery.com 3. Breast Cancer Prediction API Train an ML model and deploy it with FastAPI. 📖 https://machinelearningmastery.com 4. AI Market Research Dashboard Automate market research and trend analysis using AI. 📖 https://www.olostep.com/blog/agentic-market-research-olostep 5. Recycling Data Analysis Analyze recycling data and create insightful visualizations. 📖 https://towardsdatascience.com 6. AI Resume & Job Match Analyzer Match resumes with jobs and identify skill gaps. 📖 https://www.datacamp.com/tutorial/kimi-k2-6-api-tutorial 7. AI Data Analysis Report Generator Generate charts, insights, and reports from datasets with AI. 📖 https://www.datacamp.com/tutorial/gemini-3-api-tutorial ❤️ Follow AIJobs for more AI drops

7 Best Small Language Models Under 10B Parameters in 2026 1. IBM Granite 4.1 8B With industry-leading coding performance and a massive context window, Granite 4.1 8B is built for enterprise applications, RAG systems, and tool-calling workflows. 🔗 Click Here: https://huggingface.co/ibm-granite/granite-4.1-8b-instruct 2. Qwen3.5-9B One of the strongest reasoning models under 10B parameters, Qwen3.5-9B excels in multilingual tasks, science QA, and advanced problem-solving. 🔗 Click Here: https://huggingface.co/Qwen/Qwen3.5-9B-Instruct 3. Gemma 4 E4B Google's Gemma 4 E4B is optimized for AI agents, tool calling, and edge deployment, delivering powerful performance with minimal hardware requirements. 🔗 Click Here: https://huggingface.co/google/gemma-4-e4b-it 4. Qwen3-8B A proven favorite for developers, Qwen3-8B offers excellent code generation capabilities and supports more than 29 languages. 🔗 Click Here: https://huggingface.co/Qwen/Qwen3-8B 5. DeepSeek-R1-Distill-Qwen-7B Built for mathematical reasoning and logical thinking, this compact model delivers exceptional step-by-step problem-solving performance. 🔗 Click Here: https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B 6. Phi-4-mini Microsoft's Phi-4-mini packs impressive AI capabilities into just 3.8B parameters, making it ideal for laptops and low-resource environments. 🔗 Click Here: https://huggingface.co/microsoft/Phi-4-mini-instruct 7. Llama 3.1 8B Instruct Llama 3.1 8B remains one of the most versatile open-source models, backed by a massive ecosystem of fine-tunes and community support. 🔗 Click Here: https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct 🔥 Which small LLM is your favorite in 2026? ❤️ Follow AIJobs for more AI drops

5 Fun Papers That Explain LLMs Clearly 1️⃣ Attention Is All You Need 📝 Description: Introduced the Transformer, the architecture behind every modern LLM. Replaced older recurrent/convolutional models for sequences. 🔑 Key Ideas: Self-attention • Multi-head attention • Positional encoding • Transformer block 🔗 Paper: https://arxiv.org/abs/1706.03762 ━━━━━━━━━━━━━━━ 2️⃣ Language Models Are Few-Shot Learners 📝 Description: The GPT-3 paper. One 175B model handles many tasks just by reading prompts — no retraining. 🔑 Key Ideas: In-context learning • Few-shot prompting • Autoregressive next-token prediction 🔗 Paper: https://arxiv.org/abs/2005.14165 ━━━━━━━━━━━━━━━ 3️⃣ Scaling Laws for Neural Language Models 📝 Description: Showed model performance improves predictably as parameters, data & compute grow. The logic behind going big. 🔑 Key Ideas: Scaling laws • Compute-optimal training • Data vs. model size tradeoffs 🔗 Paper: https://arxiv.org/abs/2001.08361 ━━━━━━━━━━━━━━━ 4️⃣ Training LMs to Follow Instructions with Human Feedback 📝 Description: The InstructGPT paper. Turns a raw text predictor into a helpful, instruction-following assistant. 🔑 Key Ideas: RLHF • Supervised fine-tuning • Reward model • Human preference ranking 🔗 Paper: https://arxiv.org/abs/2203.02155 ━━━━━━━━━━━━━━━ 5️⃣ Retrieval-Augmented Generation (RAG) 📝 Description: LLMs fetch external documents instead of relying only on stored memory — great for facts that change over time. 🔑 Key Ideas: Dense retrieval • Document index • Grounded generation • Knowledge-intensive QA 🔗 Paper: https://arxiv.org/abs/2005.11401 ❤️ Follow AIJobs for more AI drops