AI Technology | Claude & ChatGPT Prompts

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