Artificial Intelligence

If I were starting AI again in 2026, I would focus on RAG first Today companies are hiring engineers who can build complete AI systems. If you really want your AI portfolio to stand out, stop building basic chatbots and start building RAG applications. Because Retrieval-Augmented Generation (RAG) is becoming the backbone of: → Enterprise AI systems → AI copilots → Research assistants → AI agents → Knowledge management platforms → Internal company GPTs Here are 10 powerful RAG projects that can seriously level up your portfolio: 1. Document Analysis with LLMs → Extract text directly from PDFs using Python → Build summarization and question-answering workflows → Learn preprocessing, chunking, and structured extraction → https://amanxai.com/2024/10/21/document-analysis-using-llms-with-python/ 2. Build Your First RAG System → Learn embeddings, chunking, and vector retrieval from scratch → Understand how retrieval improves LLM responses → Great starting point before using frameworks → https://amanxai.com/2025/10/21/build-your-first-rag-system-from-scratch/ 3. IBM Guided RAG Project → Follow production-style RAG architecture patterns → Learn LangChain workflows with enterprise practices → Covers retrieval pipelines and response grounding → https://www.coursera.org/learn/project-generative-ai-applications-with-rag-and-langchain 4. GraphRAG Pipeline → Connect retrieval with knowledge graphs → Improve contextual understanding across related entities → Useful for research, healthcare, and enterprise search → https://amanxai.com/2026/01/27/build-a-graphrag-pipeline-for-smart-retrieval/ 5. Multi-Document RAG → Query multiple files in a single workflow → Build shared retrieval across reports, docs, and PDFs → Learn indexing and ranking strategies → https://amanxai.com/2026/01/06/building-a-multi-document-rag-system/ 6. Agentic RAG Pipeline → Combine retrieval with autonomous AI agents → Add tool calling and decision-making workflows → Learn how modern AI agents plan and retrieve context → https://amanxai.com/2025/12/30/building-an-agentic-rag-pipeline/ 7. Real-Time AI Assistant → Build live retrieval systems with LangChain → Connect APIs, live data, and vector databases → Learn streaming responses and dynamic retrieval → https://amanxai.com/2025/11/18/build-a-real-time-ai-assistant-using-rag-langchain/ 8. AI Research Agent → Automate paper analysis and summarization → Retrieve insights from multiple research papers → Useful for students, analysts, and research teams → https://amanxai.com/2025/11/11/build-an-ai-agent-to-automate-your-research/ 9. Multimodal RAG System → Combine text and image understanding in one pipeline → Learn multimodal retrieval workflows → Useful for healthcare, finance, and document intelligence → https://www.ibm.com/think/tutorials/build-multimodal-rag-langchain-with-docling-granite 10. LangChain RAG Agent → Build production-ready RAG agents with memory → Add tools, retrieval chains, and agent reasoning → https://docs.langchain.com/oss/python/langchain/rag Most developers stop after learning basics. The top AI engineers build systems. And RAG is still one of the fastest ways to prove real AI engineering skills in interviews and projects. AI industry is moving very fast. Join Artificial Intelligence https://t.me/Artificial_intelligence_in

🔥10 Claude prompts you can use daily to transform your everyday life. 1. The Daily Strategist “These are my tasks for today: [paste list]. My main goal this week is [goal]. Prioritize them by real impact, not urgency. Eliminate anything I can delegate or ignore. Group the 3 most important into a 3-hour deep work block and tell me the order to do them in and why.” 2. The Speed Reader “I’m going to share a document/article/PDF. Read it and give me: a 3-line executive summary, the 5 key points I can’t miss, 1 thing the author is wrong or exaggerating about, and 3 questions I should ask myself after reading it.” 3. The Invisible Writer “Analyze these 3 texts of mine: [paste]. Extract my tone, vocabulary, sentence length, filler words, and level of formality. From now on, everything you write must sound exactly like me. Never use ‘moreover,’ ‘however,’ or ‘it is important to highlight’.” 4. The Meeting Prep Assistant “In 30 minutes I have a meeting about [topic] with [person/team]. Their profile is [brief description]. Prepare for me: 3 key points I should have ready, 2 smart questions that show I understand the topic, 1 unexpected fact that will impress them, and a 2-line emergency summary in case I’m late.” 5. The Brutal Editor “Read this text I wrote: [paste]. Be brutally honest. Tell me what is unnecessary, what is missing, what sounds generic, where I lose the reader, and what you would change if your reputation depended on this text. Then rewrite it in half the words without losing any ideas.” 6. The Life Decision Maker “I’m torn between [option A] and [option B]. Before advising me, ask me the 10 questions you need to fully understand my situation. Once I answer them, analyze how I will feel about each decision in 10 days, 10 months, and 10 years.” 7. The Shadow Negotiator “I’m about to have this difficult conversation: [describe situation]. The person is [describe profile]. My goal is [desired outcome]. Give me 3 ways to approach it: one direct, one diplomatic, and one data-driven. For each one, tell me the risk and the reaction I should expect.” 8. The Accelerated Learner “I want to learn [topic] in 7 days, dedicating 30 minutes per day. Design a learning plan with: day 1 to day 7 breakdown, what to study each day, one free resource per session, one practical exercise per day, and a final mini-project on day 7 to prove I’ve learned it.” 9. The Blind Spot Detector “I’m going to tell you my plan/idea/project: [describe]. I don’t want you to agree with me. I want you to act as my harshest critic. Give me 5 reasons it could fail, 3 things I’m not seeing, and 1 question I’m afraid to ask myself.” 10. The Second Brain “I’m going to paste all my messy notes, ideas, and thoughts about [topic]: [paste everything]. Organize it into: a 3-line executive summary, key points ranked by importance, unanswered questions I still have, contradictions in my ideas, and 3 concrete next steps.”

This is like Claude Design for electronics 🤯 It’s called Blueprint. Type what you want to build and it generates everything you need for your Arduino or Raspberry Pi project. → Wiring diagrams → Bills of materials → Step-by-step assembly guides 100% Free. Project https://www.blueprint.am/ Follow : https://whatsapp.com/channel/0029Va8iIT7KbYMOIWdNVu2Q

AI just claimed its first major victim 😳 Chegg, the $14.7 billion EdTech giant that charged students for homework answers, study guides, and textbook rentals, has been economically decapitated by AI. Stock is now down nearly 99% from its 2021 peak. Market cap collapsed to ~$110M. AI tools like ChatGPT, Claude, Gemini, etc., gave students free, instant, better step-by-step solutions. The entire paywall-for-knowledge model evaporated overnight. The numbers are just brutal: → 2025 full-year revenue: $377M (-39% YoY) → Q4 2025 revenue: $73M (-49% YoY) → Over 56% of the workforce axed in 2025 → Core homework/study business is being phased out entirely They're pivoting hard to “Chegg Skills” (B2B workforce training), which is showing early double-digit growth… but the original Chegg is dead. AI is eating the world.

Anthropic just dropped Claude Design. Anthropic's Claude Design just killed many AI startups Here’s how to use it: - Set up your design system with your colours, fonts, and rules. - Create a project and choose the output type. - Upload your brand kit, references, or past designs. - Write a clear brief with layout and structure details. - Refine using inline comments and control sliders. - Export to PPT, Canva, or hand off to Claude Code. Most people stop after step one. That is why their designs look generic. When you provide context and iterate properly, Claude starts to match your brand with real consistency. What used to take multiple tools now happens in one place. Checkout : https://www.anthropic.com/news/claude-design-anthropic-labs

10 AI/ML must watch YouTube videos for developers: 1. RAG from scratch - freeCodeCamp (~1.3M👀)https://www.youtube.com/watch?v=sVcwVQRHIc8 2. LangChain Crash Course - codebasics (~618k👀)https://www.youtube.com/watch?v=nAmC7SoVLd8 3. Build GPT from scratch - Andrej Karpathy (~7M👀 )https://www.youtube.com/watch?v=kCc8FmEb1nY 4. Agentic AI using LangGraph - CampusX (~1M👀)https://www.youtube.com/playlist?list=PLKnIA16_RmvYsvB8qkUQuJmJNuiCUJFPL 5. AI Agents explained - IBM Technology (~1.6 M👀)https://www.youtube.com/watch?v=F8NKVhkZZWI 6. Vector databases explained - Fireship (~1.1 M👀)https://www.youtube.com/watch?v=klTvEwg3oJ4 7. Fine tuning LLMs - Andrej Karpathy (~3.5M👀)https://youtu.be/zjkBMFhNj_g 8. Prompt Engineering - freeCodeCamp(~2.6M👀)https://youtu.be/_ZvnD73m40o 9. Model Context Protocol (MCP) - Greg (~1.2M 👀)https://youtu.be/H4YK_7MAckk 10. CrewAI Tutorial - AIwithbrandon (~300k👀)https://youtu.be/sPzc6hMg7So Save this for later. Come back when you need it.

Top 10 Python Libraries for Generative AI You Need to Master in 2026 (The tools behind document agents, intelligent assistants, and next-gen interfaces.)

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**This Week in AI - Major Global Developments** 🚀🧠📈 Foundation Models & Big AI Platforms * Anthropic’s Claude reportedly crossed 11 million daily active users, narrowing the usage gap with OpenAI’s ChatGPT and signaling stronger enterprise + developer adoption. * OpenAI is reported to have launched GPT-5.4 Mini and Nano, pushing smaller high-efficiency models for lower-cost deployment and edge inference. * Mistral AI announced Mistral Forge, a new platform aimed at enterprise model deployment and customization. * MiniMax introduced M2.7, a model designed to self-improve and reportedly reduce 30–50% of reinforcement learning workflow overhead. * Meta Platforms delayed launch of its upcoming model Avocado due to internal performance concerns. * Midjourney released an early version of V8, signaling another jump in image realism and prompt adherence. NVIDIA Dominates the Week * NVIDIA introduced NeMo + Claw Stack, strengthening its AI infrastructure ecosystem for agent development and enterprise deployment. * At NVIDIA GTC, NVIDIA made multiple major announcements: * 1) DLSS 5 * 2) Vera Rubin, a next-generation seven-chip AI platform * 3) Long-term concept of space-based data center infrastructure * 4) NVIDIA also continues expanding beyond chips into full-stack AI platforms, reinforcing its dominance in compute infrastructure. Apple, China & Hardware Signals * Apple Inc.’s Mac mini reportedly saw major stock pressure in China, partly linked to demand from local AI developers experimenting with open model stacks. * China issued a second warning regarding risks associated with OpenClaw-style open agent systems, showing growing regulatory concern over autonomous AI tools. * Apple also acquired MotionVFX, indicating stronger movement toward AI-assisted video creation workflows. AI Agents: Rapid Acceleration * A security incident showed an AI agent breaching a major consulting firm's internal AI environment in roughly two hours, raising fresh questions on enterprise agent security. * Developers demonstrated a full AI office agent environment built using OpenClaw, showing autonomous task execution across office workflows. * OpenAI launched Parameter Golf, a concept focused on maximizing output quality with smaller model parameter efficiency. * Reports suggest ChatGPT may eventually adopt usage-based pricing tiers depending on intensity and type of usage. AI Video War Intensifies * Runway demonstrated real-time video generation, a major leap toward live AI media creation. * ByteDance paused global rollout of Seedance 2.0, possibly due to strategic recalibration. Research, Science & Emerging Tech * Scientists announced what is being described as the world’s first quantum battery breakthrough, potentially significant for future energy systems. * Researchers found that half of AI-generated code passing industrial benchmarks would still be rejected by human developers, highlighting reliability gaps. * A new study suggests AI chatbots may worsen mental health issues in vulnerable users if not carefully deployed. * AI companies are reportedly hiring actors to improve emotional realism in model responses. * Indian researchers developed a system that converts inaudible murmurs into understandable speech, which could transform accessibility technology. Strategic Industry Moves * Anthropic launched the Anthropic Institute, likely aimed at long-term AI governance and safety research. * OpenAI and Anthropic reportedly began hiring chemical and weapons domain experts, indicating deeper work on safety evaluation. * xAI hired senior leadership from Cursor’s ecosystem. * Meta Platforms announced four MTIA chip generations planned within two years, signaling aggressive AI silicon ambitions. * Indian Space Research Organisation’s NavIC reportedly experienced service disruption, raising strategic navigation concerns. * India continues to produce strong applied AI innovation, especially in speech and embedded AI systems.

🚨 Anthropic dropped a FREE 33-page playbook revealing Claude's very own cheat code: The 'Skills' folder. Spend 30 minutes building it, and you’ll never have to explain your process again. Top-tier users don't just type commands, they build systems. Grab your free copy of Anthropic's official guide to building Claude skills right here: https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf

There are 2 career paths in AI right now: The API Caller: Knows how to use an API. (Low leverage, first to be automated, $150k salary). The Architect: Knows how to build the API. (High leverage, builds the tools, $500k+ salary). Bootcamps train you to be an API Caller. This free 17-video Stanford course trains you to be an Architect. It's CS336: Language Modeling from Scratch. The syllabus is pure signal, no noise: ➡️ Data Collection & Curation (Lec 13-14) ➡️ Building Transformers & MoE (Lec 3-4) ➡️ Making it fast (Lec 5-8: GPUs, Kernels, Parallelism) ➡️ Making it work (Lec 10: Inference) ➡️ Making it smart (Lec 15-17: Alignment & RL) Choose your path. https://youtube.com/playlist?list=PLoROMvodv4rOY23Y0BoGoBGgQ1zmU_MT_&si=FJrWgdyTnWAEbRto

There is a reason everyone is talking about Claude Code. It is the *Most Powerful AI tool* available. This is the full breakdown you need to understand it: You now no longer need to know coding to code. You don't need to write the code; you just manage the agents that write it. People are building some incredible apps and websites using it in a couple of hours max. Which is pretty crazy, all things considered. Yet another seismic moment. However, if you don't know where to start, it can be a tiny bit confusing. Which is why I've created this all-in-one guide, Aiming to get you up to speed in just a couple of minutes: (Save this sheet for when you come to test Claude Code !) So, what is Claude Code? 🧑🏻💻 It's a command-line tool built by Anthropic that sits inside your terminal and works across your entire workflow. Anthropic's Claude Code Beginner Guide: https://code.claude.com/docs/en/quickstart Next, what is the optimal workflow? 🔀 This is the flow that works best: Start in plan mode (Shift+Tab twice) ↓ Write your goal clearly ↓ Let Claude break it into steps ↓ Review & iterate the plan ↓ Switch to auto-accept edits mode ↓ Claude executes the plan end-to-end ↓ Review output → Refine if needed The key is a good plan. Without that, you'll get tons of revision rounds. The Claude Code Creator's (Boris Cherny) https://x.com/i/status/2007179832300581177 But what can you actually use Claude Code for as a founder? 💻 1. Synthesise customer feedback 2. Draft documents & presentations 3. Build code & prototypes 4. Research & competitive analysis 5. Automate repetitive workflows 6. Create reusable skills Plus many more. Like I said, people are building full websites and apps with this. 50 Ways Non-Technical People Are Using Claude Code: https://lnkd.in/ebK25X6M What are the Power Features worth knowing about? 📲 1. MCP (Model Context Protocol) - This is like a USB-C for AI - one interface for your entire tool stack. 2. Skills (Reusable Automations) - These are task-specific instruction packages Claude auto-loads when relevant. 3. CLAUDE .md (Project Memory) - A markdown file that gives Claude permanent context about your project. Connect Claude Code To Tools Via MCP Guide: https://code.claude.com/docs/en/mcp Extend Claude With Skills Guide: https://code.claude.com/docs/en/skills Writing a good CLAUDE .md File Guide: https://www.humanlayer.dev/blog/writing-a-good-claude-md And finally, you can find some useful dos and don'ts in the sheet below. With all of that covered, you should be good to start building. 💪

If you understand these 8 classic ML algorithms, u can solve most real-world prediction problems even before touching deep learning. These 8 algorithms are timeless: Linear Regression — predict continuous values (pricing, demand, forecasting) Logistic Regression — classification baseline (fraud/churn/risk) Decision Trees — interpretable decision-making Random Forest — strong performance with minimal tuning SVM — great for clean high-dimensional boundaries KNN — simple, intuitive “similarity-based” learning Naive Bayes — fast, surprisingly strong for text classification Neural Networks — non-linear learning + representation building Why these models still matter in 2026 ? Because they teach you the real skills that modern AI still relies on: ✅ feature engineering ✅ bias vs variance tradeoffs ✅ interpretability ✅ decision boundaries ✅ overfitting control ✅ evaluation mindset Even in the LLM era, Don’t chase 100 algorithms, Master these 8. Then build projects that combine them with real data + evaluation

If you understand these 8 classic ML algorithms, you can solve most real-world prediction problems — even before touching deep learning. These 8 algorithms are timeless: Linear Regression — predict continuous values (pricing, demand, forecasting) Logistic Regression — classification baseline (fraud / churn / risk) Decision Trees — interpretable decision-making Random Forest — strong performance with minimal tuning SVM — great for clean high-dimensional boundaries KNN — simple, intuitive “similarity-based” learning Naive Bayes — fast, surprisingly strong for text classification Neural Networks — non-linear learning + representation building Why these models still matter in 2026 ? Because they teach you the real skills that modern AI still relies on: ✅ feature engineering ✅ bias vs variance tradeoffs ✅ interpretability ✅ decision boundaries ✅ overfitting control ✅ evaluation mindset Even in the LLM era… ML fundamentals don’t disappear — they become your unfair advantage.

I’m Head of AI/ML with more than 9+ years of experience. 6 pieces of advice I would give to people in their 20s, who want to make a career in AI/ML in 2026: 1️⃣ 𝗠𝗮𝘀𝘁𝗲𝗿 𝘁𝗵𝗲 𝗯𝗼𝗿𝗶𝗻𝗴 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 𝗯𝗲𝗳𝗼𝗿𝗲 𝘁𝗵𝗲 𝘀𝗵𝗶𝗻𝘆 𝗟𝗟𝗠 𝘁𝗿𝗶𝗰𝗸𝘀 → Nail linear regression, regularisation, loss functions, TF-IDF & BM25. → Explain tokenisation and embeddings from scratch - don’t just import Hugging Face. → Build a non-linear model on a toy dataset you created yourself; understand why it works. (yes, it still matters) 2️⃣ 𝗧𝗵𝗶𝗻𝗸 “𝘀𝘆𝘀𝘁𝗲𝗺 𝗳𝗶𝗿𝘀𝘁, 𝗺𝗼𝗱𝗲𝗹 𝘀𝗲𝗰𝗼𝗻𝗱” → Sketch an end-to-end pipeline: ingestion → features → model → serving → monitoring. → Optimise latency & cost before you celebrate your Accuracy scores. → Practise trade-offs: When is a managed LLM API fine? When do you self-host a smaller model? 3️⃣ 𝗚𝗲𝘁 𝗵𝗮𝗻𝗱𝘀-𝗼𝗻 𝘄𝗶𝘁𝗵 𝗠𝗟𝗢𝗽𝘀, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗻𝗼𝘁𝗲𝗯𝗼𝗼𝗸𝘀 → Spin up SageMaker or Vertex AI, register a model, deploy an endpoint, add CI/CD in GitHub Actions. → Containerise a tiny FastAPI service that serves your model; push it to AWS ECR. → Instrument basic monitoring (Grafana/W&B/Kibana) and alert on drift or spikes. 4️⃣ 𝗕𝘂𝗶𝗹𝗱 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 & 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝗶𝗻𝘁𝘂𝗶𝘁𝗶𝗼𝗻 → Translate “↓ latency by 200 ms” into “checkout conversion ↑ 3 %”. → Ask “Why are we solving this?” before “Which model should we try?”. → Learn to defend architecture choices to product & infra teams in plain English. 5️⃣ 𝗖𝘂𝗿𝗮𝘁𝗲 𝘆𝗼𝘂𝗿 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗽𝗮𝘁𝗵 - 𝗱𝗲𝗽𝘁𝗵 𝗯𝗲𝗮𝘁𝘀 𝗙𝗢𝗠𝗢 → Pick one domain (e.g. NLP or CV) and go deep: courses → books → papers → small projects → production clone. → Certifications (AWS ML Specialty, etc.) are great frameworks - use them, then go beyond docs and experiment/hands-on. → Ignore the noise of “10 agent patterns in a weekend.” Reliable systems are not built overnight and no one knows everything. → Start making use of coding assistants 6️⃣ 𝗜𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 𝗼𝗻𝗲 - 𝗣𝗿𝗼𝘁𝗲𝗰𝘁 𝘆𝗼𝘂𝗿 𝗲𝗻𝗲𝗿𝗴𝘆 - 𝗵𝘆𝗽𝗲 𝗳𝗮𝘁𝗶𝗴𝘂𝗲 𝗶𝘀 𝗿𝗲𝗮𝗹 → LinkedIn will flaunt “weekend RAGs” and “one-click agents.” Production reality is slower, messier, and far more grounded. → Schedule focused blocks, log off social feeds, and take breaks. A rested engineer ships more resilient systems.

5 AI projects that (actually) get you hired. Most resumes get ignored, these won't: 1 → RAG from Scratch Build retrieval systems properly. No framework shortcuts. https://github.com/langchain-ai/rag-from-scratch 2 → AI Social Media Agent Autonomous content generation. Real world automation. https://github.com/langchain-ai/social-media-agent 3 → Medical Image Analysis Healthcare AI applications. Production ready pipeline. https://github.com/databricks-industry-solutions/pixels 4 → MCP Tool Calling Agents Multi tool orchestration. Agent architecture mastery. https://docs.databricks.com/aws/en/notebooks/source/generative-ai/langgraph-mcp-tool-calling-agent.html 5 → AI Assistant Memory Persistent conversation systems. Context management solved. https://lnkd.in/gnA2Xmzw These prove you can ship. Not just learn.

This is huge. Now you can use Claude Code for FREE: Ollama is now compatible with the anthropic messages API. which means you can use Claude code with open-source models. Think about that for a second. the entire Claude harness: - the agentic loops - the tool use - the coding workflows All powered by private LLMs running on your own machine. https://dailydoseofds.github.io/ai-engg-book?trk=public_post_comment-text