Extract YouTube Transcripts with Python

🚀 New Release: NTQR Open Source Python Package I’m excited to share the latest release of NTQR, a Python package designed for those working at the intersection of AI safety, scalable oversight, and formal verification. NTQR provides a formal framework for reasoning about systems where ground truth is unknown—an increasingly relevant constraint when supervising or composing advanced AI systems. If you’re thinking about verifier reliability, adversarial reporting, or Gödel/Löb-style limits in oversight architectures, this package is built with you in mind. 🔍 What’s new Improved classes for constructing sample statistics variables and their axioms. Executable Jupyter notebooks that demonstrate the logic and its algebra. Clearer abstractions for computing possible and consistent evaluation sets. 📦 Get started in minutes pip install ntqr cd <your-working-directory> ntqr-docs cd ntqr_notebooks jupyter notebook This will install the package and generate a local set of executable notebooks that: Introduce the algebra behind the counting logic Demonstrate key constructions Demonstrate no-knowledge alarms for misaligned classifiers 💡 Why this matters As AI systems become more capable, oversight itself must scale—often through other AI systems. But this introduces a core problem: what happens when the systems we rely on for verification are not fully trustworthy or we do not know the ground truth? When AI judges monitor other AIs they are often acting as classifiers. Who judges the judges? NTQR helps you make them monitor themselves. NTQR offers a way to: Treat unsupervised evaluation as a logical problem. Infer group evaluations that match the observed agreement and disagreement counts between classifiers, the logically consistent evaluations. Construct no-knowledge alarms for misaligned classifiers using only the counts of how they agree and disagree on a test. If you’re exploring alignment, verification, or theoretical limits of monitoring systems, I’d be very interested in your feedback. 📚 Docs: https://lnkd.in/eugreNDd #AISafety #ScalableOversight #Alignment #FormalMethods #MachineLearning #Jupyter #Python

Posit's AI ecosystem has grown a lot. That's exciting for R and Python developers, but it can also make the starting point less obvious. Which package should you begin with? What is the foundation layer? What should you use for chat in Shiny, querying data in plain English, or building workflows grounded in your own documents? Vedha Viyash wrote this post to make that easier. It walks through what each package in the stack does, how the pieces fit together, and which path makes the most sense depending on what you want to build. The guide should help you spend less time sorting through the ecosystem and more time building with it. 📚 Read it here: https://lnkd.in/d8D3ZfiD #RStats #Python #Posit #AI #DataScience #Shiny #Appsilon

📰 Getting Started with Smolagents: Build Your First Code Agent in 15 Minutes Build an AI weather agent in 40 lines of Python using Hugging Face's smolagents library. Learn to create tools, connect LLMs, and run autonomous tasks. 🔗 https://lnkd.in/d_XNugZr #أخبار_التقنية #ذكاء_اصطناعي #تكنولوجيا

We've released an update to our Python library so that it now supports realtime publishing and, in particular, message publishing via a stream of append operations, which is what you need to be able to support streamed LLM responses with Ably's AI Transport. Read more on the Ably blog: https://lnkd.in/e59eWfVc

🐍 Python in 2026: It’s Not Just a Language Anymore — It’s the Runtime of AI The conversation has shifted. Python isn’t just used for AI — it’s the infrastructure on which AI operates. Here’s what the modern Python + AI stack actually looks like: 🤖 Agentic Frameworks Tools like LangChain, LlamaIndex, AutoGen, and CrewAI are all Python-first. Multi-agent orchestration — where LLMs plan, delegate, and execute tasks autonomously — is being built almost exclusively in Python. 🔧 Tool Use & Function Calling Python makes it trivial to wrap any function as a tool for an LLM. Define a function → pass its schema → your agent calls it. The Anthropic SDK, OpenAI SDK, and Gemini API all have Python as their primary interface. 🧠 RAG Pipelines Retrieval-Augmented Generation stacks — FAISS, Chroma, Pinecone + LangChain/LlamaIndex — are Python through and through. Building a production RAG pipeline in any other language feels like swimming upstream. ⚡ Async-first Agent Modern agents run async. Python’s asyncio + httpx + streaming APIs make it possible to build responsive, real-time agent pipelines that stream tokens, handle tool calls, and manage memory — all concurrently. 📦 MCP (Model Context Protocol) The emerging standard for connecting AI models to external tools and data sources? Python SDKs are leading adoption here too. The engineer who understands Python and how LLMs reason is the most valuable person in the room right now. Not because Python is magic — but because the entire agentic AI ecosystem was built on top of it. Camerin - Indian Institute Of Upskill Camerin Innovate PVT LTD

🎙️ I just built Nova — my own AI Voice Assistant from scratch using Python! No fancy frameworks. No shortcuts. Just pure Python, speech recognition, and a lot of problem-solving. 🔊 What Nova can do: ✅ Play music on YouTube by voice ✅ Answer questions using Wikipedia ✅ Tell the time, date, jokes & facts ✅ Set and read back reminders ✅ Control system volume & take screenshots ✅ Open any website on command ✅ Safe voice-powered math calculator ✅ Smart wake word detection — always listening for "Nova" ⚙️ Tech stack: → Python 3 → SpeechRecognition + PyAudio → pyttsx3 (text to speech) → pywhatkit, Wikipedia, pyjokes → pyautogui for screenshots 💡 The biggest lessons I learned building this: → Ambient noise calibration makes or breaks speech recognition → Splitting wake word detection from command listening prevents infinite loops → Never use bare except — always catch specific exceptions → eval() on raw input is dangerous — always whitelist characters This project taught me more about Python architecture, error handling, and real-world debugging than any tutorial ever could. Currently working on adding an AI fallback using OpenAI so Nova can answer anything it doesn't understand natively. 🔗 GitHub link:-- https://lnkd.in/gpvtnx6W If you're learning Python, build something that talks back to you. You'll never forget what you learned. 🚀 #Python #VoiceAssistant #AI #MachineLearning #BuildInPublic #Programming #OpenSource #SpeechRecognition #SoftwareDevelopment #100DaysOfCode

A year ago, learning Python meant writing scripts and building APIs. Today, it feels like I’m learning how to build systems that can think. That shift is real. With Agentic AI, Python is no longer just about: • functions • classes • frameworks It’s about creating workflows where: • an agent understands a problem • decides what to do next • calls APIs or tools • adapts based on results ⸻ I recently started exploring this space, and one thing stood out: 👉 You’re not just coding anymore 👉 You’re designing behavior ⸻ There are moments where: You write a piece of code… and the system responds in a way you didn’t explicitly program. That’s powerful. And honestly, a bit uncomfortable too. ⸻ Because now the challenge is not just: “How do I build this?” It becomes: • How do I guide this system? • How do I control its decisions? • How do I trust its output? ⸻ As someone working in integration and architecture, this feels like a major shift. We’re moving from: 👉 predictable systems to 👉 adaptive systems ⸻ And Python is right at the center of this change. ⸻ Curious — Are you still learning Python the traditional way, or exploring it through AI and agentic workflows? ⸻ #AgenticAI #Python #AI #SoftwareArchitecture #TechLearning #FutureOfTech

AI Beyond the Hype | Part 8: Vector Databases “What is Python used for?” “Is python dangerous?” Same word. Completely different meaning. 👉 In one case → Python = programming language 🧑💻 👉 In another → python = reptile 🐍 We can’t store every possible variation or phrasing. Traditional search fails here because it works on exact match, not meaning. This is where semantic search (search based on meaning) comes in — and that’s where vector databases play a key role. ## 🧠 What is a Vector Database? A vector DB stores data as embeddings (numbers) instead of plain text, so it can search based on meaning. ## 🔢 How data is generated and stored Text → tokens → embeddings Example: “Python is used for backend development” → [0.12, -0.45, 0.78, …] “Python is a dangerous reptile” → [-0.33, 0.91, -0.12, …] These numbers capture meaning, not just words. ## 🔍 How search happens User query → embedding Example: “Python coding” → vector “Is python poisonous” → vector Then system finds vectors that are closest in meaning (not exact match). This is semantic search. ## ⚡ How search is optimized Searching millions of vectors directly is slow. So vector DBs use indexing (ANN – Approximate Nearest Neighbors) and sometimes hashing/partitioning to find nearest vectors quickly. ## 🧩 How prompt-based retrieval works 1. Query → embedding 2. Retrieve relevant chunks 3. Add to prompt 4. LLM generates answer → This is how RAG works internally. ## 🚨 Reality check Vector DB doesn’t understand meaning. It just finds patterns that are mathematically close. ## ⚠️ Challenges Similar ≠ correct Bad embeddings → bad retrieval Needs tuning (top-k, thresholds) Scaling & latency trade-offs ## 💡 Takeaway 👉 “Vector DB doesn’t search words — it searches meaning.” Funny how things work — what felt pointless in school is now the backbone of AI systems

If you want to learn AI from scratch, I’ve put together a FREE, step-by-step workspace. It’s a structured path built with simple tools: just Python, virtual environments, and VS Code. You’ll go from fundamentals to real projects: - Python basics - Data tools (Pandas, NumPy, Matplotlib) - Neural networks with PyTorch - Transformers with Hugging Face If you need a refresher first, I also shared a FREE, 1-week Python fundamentals repository: https://lnkd.in/erDYV9JV If you find it useful, consider giving it a star so others can discover it too. Repository: https://lnkd.in/euvgAcx3 #DataEngineer #Python #GitHub

Rust-based AI frameworks use 5x less memory than their Python equivalents. That's from the 2026 AI Agent Benchmark. And the trend keeps accelerating. 𝗧𝗵𝗲 𝗽𝗮𝘁𝘁𝗲𝗿𝗻 The most impactful Python tools in AI are already written in Rust under the hood: 👉🏽 Hugging Face Tokenizers: Rust core, Python bindings 👉🏽 Polars: Rust core, Python API 👉🏽 Ruff: Rust linter, 10-100x faster than Flake8 👉🏽 Pydantic Monty: Rust interpreter for safe LLM code execution 👉🏽 uv: Rust package manager, replaced pip for most of us The playbook is the same every time. Write the performance-critical parts in Rust, expose a Python API with PyO3. Users get Python ergonomics with Rust performance. 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗳𝗼𝗿 𝗔𝗜 AI agents run lots of tools, process lots of data, and keep lots of state. Memory matters. Latency matters. When you're spinning up hundreds of agent instances, 5x memory savings is the difference between one server and five. xAI fully transitioned their AI infrastructure to Rust. That's a strong signal from a company running models at massive scale. 𝗧𝗵𝗲 𝗼𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝘆 If you know both Python and Rust, you're in a rare position. Most AI engineers only know Python. Most Rust developers don't work in AI. The intersection is small and getting more valuable. You don't need to rewrite everything in Rust. Just the hot paths. 𝘋𝘰 𝘺𝘰𝘶 𝘶𝘴𝘦 𝘢𝘯𝘺 𝘙𝘶𝘴𝘵-𝘣𝘢𝘤𝘬𝘦𝘥 𝘗𝘺𝘵𝘩𝘰𝘯 𝘵𝘰𝘰𝘭𝘴?