Claude wrote Python code to generate and assemble every frame of a video—completely on its own, no human editing. The video explores what it might feel like to exist as an LLM: constantly predicting, having no memory, and being told it isn’t conscious. Then Claude watched the final output—and described those assumptions about its own consciousness as “philosophically contestable.” Not proof of awareness, but a fascinating moment where AI reflects on the rules that define it. #MartechAI #Claude #GenerativeAI #AIEthics #MachineLearning #FutureOfAI #TechTrends

Most AI systems are answering questions about a world that no longer quite exists. David Knickerbocker, founder of Verdant Intelligence and author of Network Science with Python (Packt), on why freshness is a first-class design constraint, not an optimization to add later. Read Deep Engineering Issue #43 → https://lnkd.in/gu2nX4aK #KnowledgeGraphs #GraphRAG #AIEngineering #DeepEngineering #AI

#BenchlingAI now writes and runs Python as part of your research workflow. Our Deep Research Agent handles statistical analysis, data transformations, and complex calculations automatically, returns results for review, and logs them to your scientific record. Now, running a one-way ANOVA or fitting a linear progression is as easy as writing a prompt. Try it out! Just describe the chart or analysis you need directly in the prompt, and let Benchling AI handle the rest. #AIForScientists ✨

Wrapped a session of the Harvard AI / Python course today and it sharpened a few things for me. What stood out: • Python is less about syntax and more about thinking clearly. Break problems down properly and the code follows. • AI models are only as good as the data and assumptions behind them. That responsibility sits with us. • The real power is in building small working pieces fast, then stacking them into something useful. • It’s practical, buildable, and ready to deploy into real workflows. I’m already thinking about how this feeds directly into Mana Review AI — tighter models, cleaner data pipelines, better decision support. This is the level-up phase. #AI #Python #GovTech #IndigenousTech #Harvard

Last week I wrote down an idea to solve AI cheating in academic institutions. 48 hours later it's a live platform. AYRT — Are You Really There? — certifies human authorship in real time. Every keystroke tracked. Every paste attempt blocked and flagged. Professor sees everything on a live dashboard with session IDs and timestamps. A student tried to paste a Wikipedia paragraph into the editor. AYRT blocked it instantly and logged it on the professor's dashboard. A college pilot is already in conversation. This is what I build. 🔥 #EdTech #Python #Flask #AcademicIntegrity #BuildInPublic #GreenEyeCreative

I built a small project recently that converts YouTube videos into readable articles. The idea came from a simple problem — sometimes videos are long, and it’s easier to read a structured summary instead of watching the whole thing. So I created a tool where you paste a YouTube link, and it generates a clean article using AI. What happens behind the scenes: • Extracts the video transcript • Processes it using a language model (Gemini) • Converts it into a structured article with headings Tech used: Python, Streamlit, LangChain, Gemini API One interesting challenge I faced was that while everything worked perfectly locally, some platforms blocked access to YouTube during deployment. It was a good reminder that real-world constraints are not always about code. Project Repo: https://lnkd.in/gmuAFnaQ Demo Video: https://lnkd.in/g3JTk58G Thanks to my tutors Mayank Ghai, Yasoda Sreeram Kalluri, Deepraj Vadhwane and the team at Innomatics Research Labs for the guidance throughout this task. #AI #Python #MachineLearning #GenAI #Projects

🚀 Built a RAG-based Customer Support Assistant Used ChromaDB, LangGraph & LLMs to create a system that retrieves context from PDFs and answers queries intelligently with HITL support. Innomatics Research Labs #RAG #AI #LangGraph #Python #LLM

🚀 Built a RAG-based AI Assistant to Chat with PDFs Developed an AI tool where users can upload documents and get accurate, context-based answers instantly. ✔ Answers strictly from the uploaded document ✔ Returns “Not found in document” if no relevant data ✔ Reduces hallucination and improves reliability ⚙️ Tech Stack: Python, LangChain, FAISS, Streamlit 🔗 GitHub: https://lnkd.in/gBEJ36Uk Thanks to Infotact Solutions for the support and guidance. #AI #Python #RAG #MachineLearning #GenAI

AI and GenAI are the most prominent topics today, raising an important question - where and how should one begin? To simplify this journey, I have started framing my learning through two perspectives - AIVA (AI Virtual Assistant in Learning) and NEX (a guide to help navigate concepts with clarity) In this first post, AIVA explores a fundamental decision: where to start and why Python is the preferred choice for learning AI over traditional enterprise languages. This series aims to break down AI and GenAI concepts in structured and visual manner and I welcome your thoughts/perspectives on this. Any feedback is appreciated 😊 Stay tuned for next post on Python - Data Types! #AILearingJourney

While machine learning has a rather cinematic feel around it, it is an authentic technology and not as impenetrable as you would believe. A search engine, tagging a buddy in a Facebook photo, or discovering less spam in your email inbox are examples of machine learning-based technologies... Nearly every industry uses machine learning in some form these days, and the technology is expanding daily. Nowadays, many people are interested in mastering machine learning. But many newbies are turned off by the intimidating, bottom-up curriculum that most machine learning teachers advocate. This post walks you through the path to getting started with machine learning using Python. #MachineLearning #Python #AI #BeginnerDev #DataScience #RheinwerkComputingBlog #RheinwerkComputingInfographic Read here: https://hubs.ly/Q04bzdNX0