Convert YouTube Videos to Readable Articles with AI

Wasting over 12k/month on under-optimized LLM fine-tuning pipelines is a harsh reality for 68% of Python teams in 2026. This staggering statistic has me questioning the conventional approach to fine-tuning large language models. As I dove deeper into this issue, I stumbled upon a game-changing guide on Dev.to that eliminates this waste with a production-grade Llama 3.1 70B workflow using Python 3.13, vLLM 0.4, and Hugging Face Transformers 4.40 - check it out at https://lnkd.in/gGcrH5vn. I'm excited to explore this new approach and I'd love to hear from you - what are your thoughts on optimizing LLM fine-tuning pipelines? 🤔💻📈 #AI #MachineLearning #FineTuning

Just finished Pydantic for LLM Workflows by DeepLearning.AI — short course, straight to the point. Pydantic is not new to Python developers. But applied to LLM workflows, it takes on a different role: enforcing structure at the boundaries where language models interact with the rest of your system. And that's where things get serious. Unstructured or unexpected model outputs are one of the main sources of fragility in AI solutions. Pydantic brings type validation, schema enforcement, and predictable error handling to those boundaries — making your agents more resilient and your pipelines actually production-ready. #deeplearning #llm #pydantic #GenAI #lifelonglearning

I’m happy to share my recent project — FRIDAY, a real-time AI voice assistant that I built using Python and LLM APIs. 🤖 This assistant can listen to voice commands, reply in real time, remember conversation context, and continue interacting until the conversation ends. Special thanks to my mentor Rahul Kothuri for inspiring me with this idea and guiding me through the approach. I built this project by following his direction and learning from his work. Technologies used: Python | Groq LLM API | Deepgram Text-to-Speech | SpeechRecognition If you had a personal AI assistant like this, what would you ask it to do for you? #ArtificialIntelligence #PythonProjects #AIEngineering #VoiceAssistant #MachineLearning #StudentProjects #LLM #TechProjects

If you're new to Agentic AI, don’t start with frameworks. Start with the code. Most people jump straight into LangChain, LangGraph, or AutoGen without understanding what an agent actually does. That’s like driving automatic without knowing manual. I took a different approach. Built a simple agent loop from scratch: No wrappers. Just Python. Here’s the idea: Step 1 - Plan Understand the query Decide the action Step 2 - Execute Call the tool Get real data Step 3 - Answer Use LLM with that data Generate grounded output No data? Say it clearly. That’s it. This is what most frameworks do just with more layers. Learn this once, and frameworks become optional. If you want to learn Agentic AI: Build it yourself first. 🔗 Repo: [https://lnkd.in/dMwdynU2] #AI #Rag #AgenticAI #LLM #Python #BuildInPublic

Built a sentiment analyser in Python today. You type any text and it tells you: → Positive / Negative / Neutral → The specific emotion behind it → Confidence level → Why it thinks that The interesting bit isn't the code,it's the prompt. The way you instruct the AI determines everything about what you get back. Change the prompt, change the output completely.

Imagine having an AI agent that remembers its users, decisions, and context even after a session ends 🤖. I've been working on building persistent memory for AI agents in Python and I'm excited to share my findings. I've discovered 3 key benefits to using persistent memory: 1. Survives restarts, so your agent doesn't forget its previous interactions 2. Works cross-machine, making it scalable and reliable 3. Is queryable via a simple API, making it easy to manage and update Check out the article at https://lnkd.in/guxgGXp9 to learn more about how I implemented this using a 3-line solution. What are your thoughts on the potential of persistent memory for AI agents? 💬 #AI #MachineLearning #Python

🚀Developed a Mini Prompt Engine using LangChain for dynamic prompt generation. In this project, I developed a system that converts user inputs into structured and dynamic prompts using LangChain. 🔧 Key Features: ✅ Replaced hardcoded prompts using PromptTemplate ✅ Built multi-input prompt systems (topic, audience, tone) ✅ Designed multiple prompt styles - Teaching, Interview & Storytelling ✅ Developed a ChatPromptTemplate system with role-based responses ✅ Implemented input validation for better reliability ✅ Created a modular and reusable Prompt Generator ✅ Demonstrated template reusability across multiple inputs 💡 Key Learning: This project helped me move from writing static prompts to designing scalable and reusable prompt systems used in real-world Generative AI applications. 🧠 Tech Stack: Python | LangChain 🔗 GitHub Repository: https://lnkd.in/gdNd7Vny Thanks to Innomatics Research Labs for the learning opportunity. #GenAI #LangChain #PromptEngineering #DataScience #MachineLearning #AI #Python #LearningJourney

I’ve just published a deep‑dive on LangChain — one of the most exciting frameworks for building modular LLM applications. In this blog, I break down: 🔹 How prompts, chains, agents, and memory fit together 🔹 Why RunnableSequence is the new way to build workflows 🔹 How to use FakeListLLM + HuggingFace embeddings for offline demos 🔹 Real‑world use cases like customer support bots, research assistants, and workflow automation 💡 The blog includes runnable code snippets, a Jupyter Notebook template, and a GitHub‑ready README so you can try everything yourself. GitHub Link: https://lnkd.in/gGUD4f4K Medium Blog Link: https://lnkd.in/gjBXSexR LangChain isn’t just about chaining prompts — it’s about designing intelligent systems that combine reasoning, context, and external tools. #LangChain #LLM #AI #GenAI #Python #InnomaticsResearchLabs #LearningByDoing

Someone asked the McDonald's support chatbot how to reverse a linked list in Python. The bot answered. Then asked if they wanted fries with that. It's kind of a perfect example of what happens when you ship an AI feature with no guardrails on what it should and shouldn't do. The bot was just doing what LLMs do which is answer whatever you ask. Nobody told it at the system level that its job is only burger support. Is it just me or are things like this starting to happen way more often? 😅 #AIGovernance #ResponsibleAI #AI #LLMs Picture from Simon Villani, PhD's linkedin post.