AI Technology | Claude & ChatGPT Prompts

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

AI Technology | Claude & ChatGPT Prompts pinned «🚀 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…»

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

5 Must-Know Python Concepts for AI Engineers 1. 🔥 Tensors & Autograd Stop writing backprop by hand. requires_grad=True tracks every operation → .backward() applies the chain rule automatically. import torch x = torch.tensor(2.0) y = torch.tensor(5.0) w = torch.tensor(0.5, requires_grad=True) b = torch.tensor(0.1, requires_grad=True) pred = w * x + b loss = (pred - y) ** 2 loss.backward() print(w.grad.item(), b.grad.item()) ✅ Exact gradients, zero math errors. 2. ⚙️ The __call__ Method Why model(x) works, not model.forward(x). call runs hooks before forward. class LinearLayer: def __init__(self, w, b): self.w, self.b = w, b self._hooks = [] def __call__(self, x): for hook in self._hooks: hook(x) return self.forward(x) def forward(self, x): return x * self.w + self.b ⚠️ Always call model(x) — .forward() skips hooks → silent bugs. 3. 💾 Pickle vs ONNX pickle = Python-locked + code execution risk 🚨. ONNX = static, language-agnostic graph. import torch model.eval() dummy_input = torch.randn(1, 10) torch.onnx.export( model, dummy_input, "model.onnx", export_params=True, opset_version=15, input_names=["input"], output_names=["output"], dynamic_axes={"input": {0: "batch_size"}} ) ✅ Portable, fast, decoupled from training code. 4. 🧱 Abstract Base Classes @abstractmethod forces subclasses to implement methods. Miss one → fails at startup, not mid-request. from abc import ABC, abstractmethod class ModelInterface(ABC): @abstractmethod def predict(self, x: list) -> list: ... @abstractmethod def get_metadata(self) -> dict: ... ✅ Fail fast, fail safe. 5. 🔐 Env Variables & Secrets Never hardcode keys. Store in .env, gitignore it, load with python-dotenv. import os from dotenv import load_dotenv load_dotenv() api_key = os.getenv("OPENAI_API_KEY") if not api_key: raise ValueError("OPENAI_API_KEY is not set!") ✅ Same code locally + Docker/Lambda. Zero leaks. ❤️ Follow AIJobs  for more AI drops

7 Real World AI Projects to Build in 2026 🤖 Build an AI Job Search Assistant Searching for jobs is repetitive — JobFit AI reads your CV, searches live postings, and generates a ranked job-fit report automatically. 📖 Guide: Kimi K2.6 API Tutorial 🐙 GitHub: kingabzpro/JobFit-AI 🔬 Build a Multi-Agent Research Assistant Most research workflows involve several steps — this multi-agent system handles web search, source filtering, and report writing all in one pipeline. 📖 Guide: Multi-Agent Research Assistant in Python 🐙 GitHub: Multi-Agent-Research-Assistant 📈 Automate Investment Research with Olostep and n8n Investment research means checking news, financials, and public sources — this workflow automates the entire process and delivers AI-generated reports. 📖 Guide: How to Automate Investment Research Using Olostep and n8n 🐙 GitHub: kingabzpro/olostep-n8n-investment-agent 📊 Build an Agentic Market Research and Trend Analysis App Manually collecting competitor updates and trend reports takes hours — this agentic pipeline handles research, extraction, and brief writing automatically. 📖 Guide: Agentic Market Research & Trend Analysis with Olostep 🐙 GitHub: kingabzpro/agentic-market-research-olostep 🧾 Build an AI Invoice Processing Pipeline Invoice processing combines document understanding and structured extraction — this pipeline uses vision AI to pull useful fields and output clean structured data. 📖 Guide: Qwen 3.6 Plus API Tutorial 🐙 GitHub: BexTuychiev/qwen-invoice-pipeline-tutorial 📉 Build a Chart Digitizer with Claude Opus 4.7 Visual data trapped inside static charts and PDFs is now extractable — this tool reads chart images and saves the data points into a clean CSV or DataFrame. 📖 Guide: Building a Chart Digitizer 🏋️ Build an Exercise Trainer with Persistent Memory Most AI agents forget everything after a session — this exercise trainer remembers your workout history and suggests personalized sessions every time you run it. 📖 Guide: Add Persistent Memory to AI Agents ❤️ Follow AIJobs for more AI drops

10 Python Libraries for Building LLM Applications 🔹 1. Transformers Core library for loading, fine-tuning, and running LLMs with ease. 👉 Learn more: https://huggingface.co/docs/transformers 🔹 2. LangChain Connect prompts, tools, APIs, and models into powerful workflows. 👉 Learn more: https://docs.langchain.com 🔹 3. LlamaIndex Bring your own data into LLMs for smarter, grounded responses (RAG). 👉 Learn more: https://docs.llamaindex.ai 🔹 4. vLLM High-performance LLM serving with faster inference and better scaling. 👉 Learn more: https://docs.vllm.ai 🔹 5. Unsloth Efficient fine-tuning with LoRA & QLoRA — even on limited hardware. 👉 Learn more: https://github.com/unslothai/unsloth 🔹 6. CrewAI Build multi-agent systems where AI agents collaborate on tasks. 👉 Learn more: https://docs.crewai.com 🔹 7. AutoGPT Create goal-driven autonomous agents with step-by-step execution. 👉 Learn more: https://github.com/Significant-Gravitas/AutoGPT 🔹 8. LangGraph Design advanced, stateful workflows with branching logic. 👉 Learn more: https://docs.langchain.com/langgraph 🔹 9. DeepEval Test and evaluate LLM outputs for accuracy and reliability. 👉 Learn more: https://github.com/confident-ai/deepeval 🔹 10. OpenAI Python SDK Quickly integrate powerful AI features without managing infrastructure. 👉 Learn more: https://platform.openai.com/docs ❤️ Follow AIJobs for more AI drops

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 AWS: skillbuilder.aws 9 - DeepLearning.AI: deeplearning.ai 10 - Hugging Face: huggingface.co/lear ❤️ Follow AIJobs  for more AI drops

Best YouTube Channels To Learn AI in 2026 1. Fundamentals – 3Blue1Brown 2. Deep Learning – Andrej Karpathy 3. AI Research – Yannic Kilcher 4. Practical AI – AssemblyAI 5. LLMs – AI Explained 6. ML Theory – StatQuest 7. Papers Simplified – Two Minute Papers 8. GenAI – Matthew Berman 9. AI Agents – Nicholas Renotte 10. Applied ML – Krish Naik 11. PyTorch – Aladdin Persson 12. Math for ML – Serrano Academy 13. Industry Insights – Lex Fridman 14. Real-world AI – DeepLearningAI ❤️ Follow AIJobs  for more AI drops

Top 100 Claude AI Tips ❤️ Follow AIJobs for more AI drops

8 Types of AI Agents You Should Know ❤️ Follow AIJobs  for more AI drops

10 YouTube channels that teach AI better than most CS degrees in 2026: 1. Andrej Karpathy Deep, intuitive walkthroughs of neural networks and modern LLMs https://youtube.com/@AndrejKarpathy 2. 3Blue1Brown Visual intuition for math, linear algebra, and neural networks https://youtube.com/@3blue1brown 3. StatQuest with Josh Starmer Clear, friendly explanations of statistics and ML fundamentals https://youtube.com/@statquest 4. Stanford Online University-grade ML and AI lecture series (Andrew Ng, CS229, etc.) https://youtube.com/@stanfordonline 5. sentdex Practical machine learning and Python projects https://youtube.com/@sentdex 6. Yannic Kilcher Deep dives into ML and AI research papers https://youtube.com/@YannicKilcher 7. MIT OpenCourseWare Rigorous academic courses on ML, AI, and applied mathematics https://youtube.com/@mitocw 8. Siraj Raval High-level overviews and motivation around AI concepts Link: https://youtube.com/@SirajRaval 9. DeepLearningAI Structured learning paths for deep learning and generative AI https://youtube.com/@DeepLearningAI 10. Two Minute Papers Fast, accessible summaries of cutting-edge AI research https://youtube.com/@TwoMinutePapers ❤️ Follow AIJobs  for more AI drops

5 Lightweight and Secure OpenClaw Alternatives to Try Right Now 1️⃣ NanoClaw Container-based agent runtime focused on safer execution and isolation. Great for teams building secure Claude-based workflows with messaging and memory support. 2️⃣ PicoClaw Ultra-lightweight, portable, and easy to deploy anywhere. Perfect for developers who want fast agent automation without heavy infrastructure. 3️⃣ TrustClaw Managed agent platform designed for usability and structured deployment. Best for users who prefer hosted agent systems over complex self-hosted setups. 4️⃣ NanoBot Minimal Python-based agent framework with core tools and memory. Ideal for builders who want a clean, auditable, and easily extensible codebase. 5️⃣ IronClaw Modular framework built for structured autonomy and reusable components. Strong choice for teams building scalable, production-grade agent workflows ❤️ Follow AIJobs  for more AI drops

Top 5 Super Fast LLM API Providers 1️⃣ Cerebras Extreme throughput using wafer-scale hardware architecture. Best for high-QPS workloads, long generations, and maximum tokens per second. 2️⃣ Groq Ultra-low time to first token with deterministic execution. Perfect for chat apps, agents, copilots, and real-time responsiveness. 3️⃣ SambaNova Strong sustained performance with reconfigurable dataflow architecture. Ideal for Llama-family deployments needing stable, high throughput. 4️⃣ Fireworks AI Software-first optimization with quantization and speculative decoding. Reliable, consistent speed across multiple large model families. 5️⃣ Baseten Highly optimized serving with strong GLM-4.7 performance. Great choice when GLM speed matters more than peak GPT throughput. ❤️ Follow AIJobs for more AI drops

100 Nano Banana Prompts - 2026 Download Link: Prompt ❤️ Follow AIJobs for more AI drops