#Claude Code Vs Copilot Agent

5 AI TOOLS YOU NEED IN 2026 (Part 2) If your goal is to automate your workflow from data analysis to content creation, these are the 5 tools that actually matter. 1️⃣ Julius AI: If you want to analyze complex data and create charts, use this. 2️⃣ Runway ML: If you want to generate cinematic videos from text, use this. 3️⃣ Eraser AI: If you want to generate system architecture diagrams from text, use this. 4️⃣ Mage AI: If you want to build and automate data pipelines, use this. 5️⃣ BigQuery: If you want to run ML models directly on your data with SQL, use this. 📩Save this for later. ♻️Share this with someone to upgrade from tools to agents. 💡Follow for part 3! . . . AI tools, artificial intelligence, automation, productivity hacks, data engineering, cloud computing, tech trends 2026, software engineering, devops, machine learning, data analysis, system design, workflow automation, ai agents, tech stack, innovation

Part 1: Let's build a real AI Data Analyst from scratch 📈 ㅤ This is Part 1 of my Build AI Agent series where we build practical, working agents using Python, LangChain, and OpenAI. ㅤ Comment which agent you want to see next? ㅤ Follow for the next part ㅤ #aiagents #python #openai #dataanalytics

Which AI tool do you use the most in your day-to-day work? ㅤ #datascience #dataanalytics #techcareers #aiworkflow #careeradvice #aitools

Comment “AI” to upskill yourself! Most companies now use data + AI to make everyday decisions, and the real gap isn’t talent, it’s people who know how to apply these tools to business problems. That’s why programs like BITSoM’s 6-month Business Analytics with Gen & Agentic AI exist. Not to teach theory, but to train people on how companies actually work today — using Excel, SQL, Python, Tableau, and AI tools like ChatGPT on real datasets, real cases, real decisions. The focus is simple: build things you can show, not concepts you memorise. If you’re curious: – Entry is through a ₹99 qualifier test – 60 minutes, no coding – Happening this Sunday – Limited seats [business analytics, genai careers, ai jobs india, data analytics course, bitsom, ai in business, upskilling india, genai skills, mba analytics, ai careers]

Master these 10 libraries and companies will pay you $150K+ to build their AI products. Saved this yet? 📌 � Want the complete 92 page cheat sheet with code examples? 👉 Follow me(so i can message) & Comment , I'll send it to you in DM📥 1. LangGraph Build stateful AI agents with complex workflows 2. Instructor Structured outputs from LLMs using Pydantic 3. LlamaIndex RAG systems for document Q&A 4. OpenAI SDK Access GPT models with function calling 5. FastAPI Deploy AI models as fast APIs 6. Anthropic SDK Claude integration for complex reasoning 7. DSPy Auto-optimize prompts like ML models 8. ChromaDB Simple vector database for embeddings 9. LiteLLM Unified interface for 100+ LLMs 10. Transformers Access & fine-tune open-source models 📲 Follow @datasciencebrain for Daily Notes 📝, Tips ⚙️ and Interview QA🏆 . . . . . . [dataanalytics, artificialintelligence, deeplearning, bigdata, agenticai, aiagents, statistics, dataanalysis, datavisualization, analytics, datascientist, neuralnetworks, 100daysofcode, llms, datasciencebootcamp, ai] #datascience #dataanalyst #machinelearning #genai #aiengineering

If you don’t know these basic AI terminologies, you should NOT apply to AI roles 🚫 Here’s what each means: Tokens - Basic units LLMs process (words or word pieces) Embeddings - Numbers representing text/data in vector space Context Window - Max text a model can process at once Fine-tuning - Training a pre-trained model on your specific data Prompting - How you structure input to get better outputs Transformers - Architecture behind modern LLMs Attention Mechanism - How models focus on relevant input parts Parameters - Learned weights in the model (billion parameters = model size) Inference - Running the model to get predictions RAG - Retrieval Augmented Generation - fetching data before generating Vector Database - Stores embeddings for fast similarity search Latency - Response time from your model Quantization - Reducing model size by lowering precision Loss Function - Measures how wrong your predictions are Overfitting - Model memorizes training data, fails on new data Temperature - Controls randomness in model outputs RLHF - Training models with human feedback (how ChatGPT learns) Hallucination - When models generate confident but wrong information This is to give you an idea of what each word means but you should learn these in depth. #AIEngineering #MachineLearning #LLM #AI TechCareers SoftwareEngineering​​​​​​​​​​​​​​​​

🚀 I’m a Data Scientist & ML/AI Consultant.Here’s What My Work Actually Looks Like. Most people think I just build fancy AI models all day. That’s cute. 😄 Here’s the reality 👇 🧠 1. 60% Understanding the Problem • What is the real business goal? • What decision are we trying to improve? • Is this even an ML problem? Sometimes the best solution is SQL + dashboard, not AI. 📊 2. 25% Cleaning Messy Data • Missing values • Duplicate records • Inconsistent formats • Wrong labels No clean data = No smart model. Data > Algorithm. 🤖 3. 10% Building the Model • Feature engineering • Model selection • Training & tuning • Validation Models are tools. Thinking is the real skill. 🚀 4. 5% Deploying & Explaining • Making it production-ready • Monitoring performance • Explaining results to non-technical teams If you can’t explain it simply, you don’t understand it deeply. 💡 Truth Most Courses Don’t Tell You: AI is not about TensorFlow. It’s about solving business problems. 🔥 My Line: “I don’t just build models. I build decisions.”

@openai 's internal AI agent just replaced 200 lines of SQL with 1 question in plain English. And 10 table joins, 5 CTEs, hours of debugging, done in minutes. That's not a small upgrade but a complete workflow shift. Most analysts spend 80% of their time writing code just to get to the insight. The new reality is that the insight comes first, and the code is just background noise. That's a completely different mindset, and most people in data haven't caught up yet. The analysts who will thrive aren't the ones who write the most complex SQL. They're the ones who ask the best questions, understand what the data should tell them, and know how to use AI to get there in minutes instead of hours. Domain knowledge + AI fluency = the new unfair advantage in data. Drop a 🙋 if you're in data and actively learning how AI is changing your workflow. Curious how many of us are already on this. [dataanalyst, datascience, openai, aiagents, futureofwork, sql, python, analytics, datacareer, artificialintelligence, machinelearning, techjobs, careergrowth, dataengineering, llm] #dataanalyst #datascience #openai #aiagents

Let’s build a Data Analyst AI agent together (WITHOUT writing code) I’m using n8n for this. This is one of my favorite no-code automation tools. If you’d like to see a version in Python, let me know! #datascience #aiagent #n8n

Data doesn’t make decisions. Humans do. AI can process numbers — but it can’t ask the right questions or understand context. That’s why AI data analyst jobs still need human thinking, not just tools. Learn how to combine data, AI, and human insight → Gen AI Data Analyst Bootcamp #aidataanalyst #aicareers #careerswitch #futureofdata #dataanalytics #learnai #workforceinstitute

Data doesn’t make decisions. Humans do. AI can process numbers — but it can’t ask the right questions or understand context. That’s why AI data analyst jobs still need human thinking, not just tools. Learn how to combine data, AI, and human insight → Gen AI Data Analyst Bootcamp #aidataanalyst #aicareers #careerswitch #futureofdata #dataanalytics #learnai #workforceinstitute

If you think you need to be an AI engineer or a data scientist to build AI agents... You don’t! The term AI agent is a fancy name for a pipeline that orchestrates a set of functions. It is more software engineering (and not a fancy one) than data science. Here are the four skills that you need to know to start building, programmatically, your AI agents: - Domain knowledge - Basic programming language knowledge - Understanding APIs - Context engineering This week, I am starting a sequence of videos on SQL AI agents, covering the general architecture, components, and best practices. Stay tuned! #ai #datascience #sql #llm