From ChatGPT to AI tools: My journey with Generative AI

The Ultimate Roadmap to Build AI Agents (Even if You’re a Beginner) When people hear “AI Agents,” they often imagine something far too advanced — like building Jarvis from Iron Man. But here’s the truth  Anyone with basic programming knowledge can start learning how to build AI Agents — step by step. And this roadmap perfectly captures that journey. You don’t start by wiring up complex multi-agent systems. You start small — by learning: 🧩 Python or TypeScript basics 📚 Machine learning fundamentals 🧠 How LLMs and prompts actually work Then slowly, you connect the dots — learning about APIs, RAGs (Retrieval-Augmented Generation), and orchestration frameworks that make your AI act instead of just chat. By the time you reach the last box — evaluation, observability, and multi-agent systems — you’ve not just learned how to use LLMs, but how to engineer intelligence that plans, reasons, and adapts. Here’s the cool part — this roadmap doesn’t require a PhD. It just requires consistency, curiosity, and a habit of tinkering. If you’ve ever wanted to build your own AI Agent that automates tasks, answers intelligently, or even collaborates with other agents — this is your starting line. 💡 Tip: Pick one block from this roadmap each week and build something small around it. In a few months, you’ll realize — you’re not just prompting ChatGPT anymore… you’re building your own. What’s the first block you’d start with? #AI #MachineLearning #GenerativeAI #AIAgents #DataScience #RAG #PromptEngineering

Most people stop at learning ChatGpt/prompts. But the real builders master the entire Generative AI skill tree. Here’s the roadmap: -> Foundation — Python, PyTorch, TensorFlow, NumPy -> Models — GPT, Claude, LLaMA, Stable Diffusion, Gemini -> Techniques — LangChain, LoRA, QLoRA, Pinecone -> Agents — CrewAI, AutoGen, LangGraph -> Scaling — Docker, Kubernetes, Bedrock, Vertex AI Want to grow from fundamentals to real-world products? Join our Free Webinar — “From Prompts to Products: Master GenAI” Register here : https://lnkd.in/emA7uHMR Speaker: Shyam Kumar, Senior ML Engineer Thoughtworks 100+ peers already registered. Register now and start building your GenAI future.

Most people stop at learning ChatGpt/prompts. But the real builders master the entire Generative AI skill tree. Here’s the roadmap: -> Foundation — Python, PyTorch, TensorFlow, NumPy -> Models — GPT, Claude, LLaMA, Stable Diffusion, Gemini -> Techniques — LangChain, LoRA, QLoRA, Pinecone -> Agents — CrewAI, AutoGen, LangGraph -> Scaling — Docker, Kubernetes, Bedrock, Vertex AI Want to grow from fundamentals to real-world products? Join our Free Webinar — “From Prompts to Products: Master GenAI” Register here : https://lnkd.in/emA7uHMR Speaker: Shyam Kumar, Senior ML Engineer Thoughtworks 100+ peers already registered. Register now and start building your GenAI future.

😩 Tired of just using LLMs? ⚡️ It's time to build them. 😉 Forget the black box. This incredible GitHub repo is your golden ticket to coding a ChatGPT-like LLM in PyTorch – from scratch! If you've ever wanted to truly understand Generative AI, this is your definitive, step-by-step guide to implementing a ChatGPT-like LLM in PyTorch from scratch. This isn't a high-level overview; it's the definitive, hands-on roadmap to mastering the core mechanics of Generative AI: The Roadmap Highlights: 1. Foundation: Implement the core GPT architecture, including the attention mechanism, from the ground up. 2. Zero Dependencies: It uses pure PyTorch—no external LLM libraries—ensuring you understand every single line of code. 3. Full Cycle: Covers the entire LLM workflow, from working with text data to pretraining on unlabeled data and instruction fine-tuning (the "alignment" step). 4.Accessibility: The code is designed to run on a conventional laptop, utilizing a GPU if available. This is the official code companion for the book "Build a Large Language Model (From Scratch)." If you are a Data Scientist, ML Engineer, or anyone serious about understanding the future of AI, this repository is required reading. Stop relying on API calls and start controlling your own destiny. 🔗 The Ultimate Resource: https://lnkd.in/dSUXyR-g #AIAgents #GenerativeAI #LLMs #DeepLearning #PyTorch #DataScience #MachineLearning #AIdevelopment #Coding

🚀 I just built an AI app that lets you chat with your documents — in real time! Ever wished you could upload multiple PDFs, Word, or TXT files and just ask questions about them naturally? Now you can. 💬 I created a Gradio-based AI interface powered by LangChain and OpenAI (GPT-4o-mini) that: - Accepts multiple documents (PDF, DOCX, TXT) - Summarizes each one with live streaming responses - Stores their content in a vector database (Chroma) for retrieval - Keeps chat memory for smooth, context-aware conversations - Lets you reset, refresh, and re-upload knowledge dynamically What I loved most about this build was combining streaming, retrieval-augmented generation, and conversational memory into one clean experience. It taught me a lot about document chunking, embedding performance, and how to design a truly interactive AI workflow. �� Next step: adding support for long-term memory and multi-user sessions — so each person can have their own private chat knowledge base. 🔗 GitHub Repo: https://lnkd.in/dFN-46tJ 👉 Would you use a tool like this for research, studying, or document analysis? I’d love to hear how you’d improve or extend it! #AI #LangChain #gradio #OpenAI #MachineLearning #python

Things I learned this year as an AI/ML Engineer: - Focus on data; the solution lies within it. - XGBoost outperforms many classic ML algorithms and excels at time-series. - UV is the best tool for Python package management. - For applied ML, build first, then read research papers. - Math and statistics/probability are essential skills. - Caching is critical for ML projects. - Agentic AI frameworks aren’t needed for LLM function calling. - FastAPI and PyTorch are a powerful duo. - When using ChatGPT, provide input and problem statements. Brainstorm pipelines, don’t ask for code. - Instruct ChatGPT: “You are a 10+ year ML Engineer expert in XYZ domain,” then share the problem. - Work with quantized LLMs. - Reinforcement Learning will outlast LLMs in relevance. - Deploy models first, then improve iteratively. - Speed currently outweighs accuracy; I can handle errors but not slow inference. - Data Engineering > AI/ML Engineering. - Use AI to learn Next.js/React.js for high returns. - Apple M-Series chips are powerful but doesn't support CUDA libraries at all. - MLOps is a must skill for ML Engineers and demand is very high. - Making RL to production is a bit complex and we need a dedicated RLOps framework. What's your experience in ML this year? Follow me on X: https://lnkd.in/dUHkiWh3 #MachineLearning #DataEngineering #AI #GenAI #Python

We often hear about OpenAI, Google, or Anthropic creating massive AI systems. But what if I told you that you can start building your own — right from your laptop? 💻 Creating an AI isn’t about heavy servers or huge budgets anymore. It’s about understanding the process — the lifecycle behind how intelligence takes shape: 1️⃣ Define a goal — Chatbot, voice AI, or an image tool. 2️⃣ Collect meaningful data — real examples that help your AI learn. 3️⃣ Train your model — use frameworks like PyTorch or TensorFlow. 4️⃣ Fine-tune for accuracy — tweak parameters until you get better output. 5️⃣ Deploy and integrate — bring your creation to life through APIs or web apps. Every step teaches you not just coding, but how AI thinks, learns, and improves. And once you build even a simple model — your mindset about technology completely shifts. Because the real innovation ahead isn’t about using AI… It’s about those who understand how to create it. 🚀 #ArtificialIntelligence #MachineLearning #Innovation #FutureOfWork #Python #DataScience #AIProjects #TechCommunity

Generative AI isn’t just a concept anymore, it’s something I’m now building with. Over the past few days, I’ve immersed myself in the Generative AI Bootcamp with The Incubator Hub, and it has been nothing short of transformative. From foundational concepts to hands-on labs, we’ve been exploring how AI models, APIs, and Python frameworks come together to power intelligent systems. Here’s what I’ve been working on so far: ⬇️Understanding and exploring Hugging Face models for text generation and embeddings ⬇️Creating and authenticating API tokens for deploying models ⬇️Building and testing models through Google Colab Notebooks ⬇️Implementing Gradio interfaces for real-time interaction ⬇️Using NotebookLM for structured reasoning and conversational memory ⬇️Developing a mini ChatGPT-like application, a chat interface that remembers previous conversations I was tasked with building a functional AI application using the Hugging Face Hub and Inference APIs, integrating prompt design, token usage, and interface design My Solution: I designed a Chat-based interface using Python, Gradio, and Hugging Face Transformers, capable of maintaining context between conversations. This showed me how architecture, data, and user experience intersect to make AI tools truly intelligent. This isn’t just a theory, it’s hands-on, applied AI that connects creativity with computation. The Bootcamp has made one thing very clear: 👉🏽 Those who can build with AI will define the next era of work. I’m excited to keep sharing my journey as we move deeper into real-world projects that combine AI, data, and human innovation. #GenerativeAI #ArtificialIntelligence #HuggingFace #Python #Gradio #AIInnovation #MachineLearning #adejumobijoshua #FutureOfWork #AIinBusiness #IncubatorHub #AIAuthority

What you need to step into the world of AI Agents? As we move deeper into AI Agents and Automation, you may ask “Where do I can start?” Here’s a simple roadmap 👇 (Okay, maybe not that simple 😅) 1. Core Tech Skills • Python: the language behind most agents • APIs: how agents take action • Data handling: clean, structure, feed your agent • NLP: for understanding text & conversations 2. Agent Building Skills • Prompt engineering & reasoning • Memory & tool use • Multi-agent orchestration • Deploying & scaling your agents 3. Human Skills (the underrated ones) • Problem-solving mindset • System thinking • Curiosity & continuous learning The truth? You don’t need to learn everything. You can make agents without coding but knowing how to code can come in very handy. 💬 Which one of these skills are you focusing on right now and which one feels the most challenging? P.S. The best way to learn agents is to build one. Even a tiny one counts. #AI #Agents #Learning

📅Day 10/30 of my #GenAIDeveloperJourney — Entering the World of AI Agents! 🚀 Today I took my first serious step into AI Agents with the Kaggle course "Agents." That opened an entirely new layer of AI development to me. 🔧 Until now myth, AI felt like: Prompt → LLM Output But today I realized something powerful: 👉 Agents don't just respond, they THINK, ACT, and IMPROVE. And this is the next big skill companies want in 2025. AI Agents: What Are They? (My Deep Dive Learning Today) Here is the clearest definition I got today: AI agents = LLM + memory + tools + actions + feedback loop Meaning: They can ✔ Understand a task ✔ Break it into steps ✔ Decide what to do next ✔ Use tools: search, Python, APIs ✔ Learn from previous steps ✔ And complete the task on their own This is 10X bigger than just prompting. .🔍 Key Concepts I Learned Today (Kaggle Notes) 1️⃣ Observation → Reasoning → Action Loop Agents follow a loop similar to humans: ▶️ They observe the environment. ▶️ They reason using an LLM. ▶️ They act using tools. ▶️ They reflect and retry if needed ▶️ This makes them reliable for automation. 2️⃣ Tools Make Agents Powerful ▶️ An LLM alone is limited. ▶️ But with tools, agents can: ▶️ Run Python code ▶️ Search the internet ▶️ Run APIs ▶️ Perform data transformation. ▶️ Automate workflows That's where they turn from "smart chatbot" → "autonomous system". 3️⃣ Memory = Agents That Evolve ▶️ Agents store: ▶️ Previous actions ▶️ Outputs ▶️ Mistakes 🗨️ Agents don’t act blindly — they think step-by-step before calling tools. 🗒️Note: The back-end part I'm gonna share within this week as it's 40% left because of my commitment towards daily tasks in the journey of 30 days challenge it wasn't likely to skip a day #genai #agents #kaggle #developerjourney #openai #aiagents #machinelearning #python