Iulia Feroli’s Post

This video is a collaboration with JetBrains — produced for the PyCharm channel as part of an ongoing DevRel partnership. It's a good example of what that kind of work looks like in practice: a technically honest, hands-on tutorial that serves the audience first and happens to showcase the tool naturally along the way. This is exactly the kind of content production I'm open to doing with more teams in 2026. If you work at a developer tools, AI, or robotics company and want to reach a technical audience through video content that actually gets watched - I'd love to talk. The Back to Engineering channel covers Physical AI, robotics, and embedded systems. But the production model of deep technical content, learn-in-public format, real code on screen translates across the developer tooling space. #DevRel #DeveloperEducation #TechnicalContent #BackToEngineering #JetBrains

Reinforcement Learning is a subfield of machine learning that involves an agent learning to take actions in an environment to maximize a reward, learn more with Python and Gym https://lnkd.in/g233cAvV #ReinforcementLearning Read the full article https://lnkd.in/g233cAvV

AI-based Flappy Bird game developed using Python and NEAT 🐦🤖💻 Developed as a learning experience, this project helped us understand AI concepts while having fun 🎮📚✨ #AIProject 🚀 #MachineLearning 🧠 #NEATAlgorithm 🔬 #PythonProjects 🐍 #GameDevelopment 🎯 #FlappyBirdAI 🐤 #DeepLearning 📊 #AIFun 😄 #LearningByDoing 📖

Built a GenAI application that converts YouTube videos into structured articles, downloadable PDFs, and responsive webpages. The system uses a multi-step pipeline: transcript extraction, content cleaning, article generation, and multi-format output creation. Tech stack: Python, Streamlit, LangChain, Groq (LLaMA 3.3), FPDF This project helped me understand how to design end-to-end GenAI workflows beyond simple summarization. 🔗 GitHub: https://lnkd.in/gygZUgGG #Innomaticsresearchlabs #GenerativeAI #Langchain #Python #Datascience

How can you evaluate an AI model's robustness before real-world failures occur? In this webinar, we’ll demonstrate how to use the open source Natural Robustness Toolkit (NRTK) to create reproducible workflows for testing model performance. You’ll learn how to: ✅ Install and configure NRTK in Python ✅ Apply perturbations to expand existing datasets ✅ Design parameter sweeps to measure performance degradation ✅ Evaluate models under simulated operational conditions 📅 April 15, 2026 | 12–1 PM 👉 Register here: https://ow.ly/Ncnr50YBmK7 #AIResearch #MachineLearning #ModelValidation #NRTK #Python

NumPy Practice – Day 2 🚀 Continued my NumPy learning and practiced: 🔹 Reshaping & flattening arrays 🔹 Stacking arrays (horizontal & vertical) 🔹 Random number generation 🔹 Finding unique & duplicate elements 🔹 Sorting & moving averages Key learning: NumPy enables efficient array operations and reduces the need for loops. 📒 Sharing my Google Colab notebook: https://lnkd.in/gs3aZcfY #Python #NumPy #DataScience #LearningInPublic

🚀 LeetCode Grind: Find First and Last Position of Element in Sorted Array Just solved a classic searching problem! 💻 Problem: Given a sorted array, find the starting and ending position of a given target value. The Challenge: Achieving $O(\log n)$ runtime complexity. Key Takeaway: While a linear scan works, leveraging Binary Search twice (once for the left boundary and once for the right) is the key to meeting the performance constraints. It’s a great reminder of how powerful binary search is for optimizing search operations on sorted data. Checking off another one as I continue to sharpen my problem-solving skills! 🛠️ #LeetCode #CodingChallenge #Python #Algorithms #DataStructures #ProblemSolving #TechJourney #BinarySearch

🚀 Day 04 of My Machine Learning Journey: NumPy Data Types (dtypes) Today, I learned about NumPy data types (dtypes), which define the type of elements stored in an array. I explored: ✅ Different types like int, float, and bool ✅ How NumPy uses fixed data types for better performance ✅ Why choosing the right dtype helps optimize memory usage Understanding dtypes helps write more efficient and faster code — an important step for Machine Learning. 💡 #MachineLearning #NumPy #Python #LearningJourney #Day04

Learn about reinforcement learning, a subfield of machine learning, and how to implement it using Python and the Gym library, with this comprehensive guide https://lnkd.in/gqQnHdpq #ReinforcementLearning Read the full article https://lnkd.in/gqQnHdpq

I've been using AI agents every day for months without actually understanding how they work. A cracked friend built a tool that breaks the whole thing down. No frameworks. ~60 lines of Python. Turns out an agent is just a function. Tools are a dict. Memory is a tool that writes to a file. That's it. Went through all 9 lessons in one sitting. If you've been using LangChain or CrewAI without knowing what's underneath, honestly just start here first. #AIAgents #SoftwareEngineering #BuildingInPublic