Resolving ipywidgets issues in Google Colab and Jupyter Notebook

📢 532 downloads in 3 days. No marketing. Open Source. No VC money.🚀 Just a Python framework that gets out of your way. We built SynapseKit because debugging LLM apps was taking longer than building them. Too many layers. Too much magic. Too little control over what was actually happening. So we started over. From scratch. ⚡ Async-native. Streaming-first. 2 dependencies. Code you can read on a Monday morning without coffee. What's inside: 🔌 13 LLM providers behind one interface :swap models in one line 🔍 10 retrieval strategies: RAG Fusion, CRAG, Ensemble, Self-Query 🔀 Graph workflows with human-in-the-loop and SSE streaming 🛠️ 16 tools, 12 loaders, 4 memory backends ✅ 540 tests passing No hidden chains. No magic. Just code that does what you think it does. If you're building LLM apps in Python and want something you can actually debug: 📦 pip install synapsekit[openai] 🔗 https://lnkd.in/d2fGSPkX #Python #LLM #RAG #OpenSource #AI #MLEngineering #MachineLearning #Developers #SoftwareEngineering #AgenticAI

Tkinter Tutorial: Building a Simple Interactive GUI for a Word Counter In the digital age, we're constantly interacting with text. Whether it's writing emails, drafting reports, or simply browsing the web, we're surrounded by words. Understanding the length and composition of our text is often crucial, from adhering to character limits on social media to optimizing content for readability. This is where a word counter comes in handy. But, instead of relying on external tools, imagine having a simple, interactive word counter right at your fingertips, built with Python and Tkinter....

I recently built my portfolio website (https://lnkd.in/gDdqFrMN) using a custom Python tool I wrote. I named it folio_gen. I was looking for something small and easy to update instead of dealing with CMS platforms or WYSIWYG editors. I wanted to manage everything through Markdown files which are easy to handle. While there are many generators out there, in the era of agentic AI, I decided to just build my own. It’s a minimalist static site generator driven by a single YAML configuration. No complex hosting or databases required, it just generates the static HTML, which you can then host for free on GitHub Pages or Netlify. I've released the code to the GitHub repository for anyone else who wants a straightforward way to set up a personal or lab website. Repo: https://lnkd.in/gYnVGcAU

🚀 Day 1 of My Machine Learning Journey — Built My First Web App! Today wasn’t about watching tutorials. It was about execution. I built a Student Marks Analyzer Web App using: Python 🐍 Flask 🌐 HTML + CSS 🎨 💡 What it does: Takes student name & marks Calculates total, average Assigns grade Shows pass/fail result ⚡ What I learned today: Basics of Python (loops, functions, logic) How backend connects with frontend Real debugging (and yes… errors taught me a lot 😄) How to turn an idea into a working system 🔥 Biggest realization: “Learning doesn’t count. Building does.” This is just Day 1. No perfection. Just consistency + execution. 📈 Goal: Become a Machine Learning Engineer by building real projects. If you're also on a similar journey, let’s connect 🤝 #Python #Flask #WebDevelopment #LearningInPublic #100DaysOfCode #BuildInPublic #AIJourney

Tired of slow applications and performance bottlenecks? Try speedscope. It's a fast and interactive flamegraph viewer that helps you pinpoint exactly what's slowing down your code. Here’s why it’s interesting: ✅ 𝗣𝗼𝗹𝘆𝗴𝗹𝗼𝘁 𝗣𝗼𝘄𝗲𝗿: It supports almost any language you can think of. Python, JavaScript, Go, Rust, .NET, and many more. ✅ 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝘃𝗲 𝗩𝗶𝘀𝘂𝗮𝗹𝘀: You get a smooth, 60fps experience while exploring huge performance profiles. ✅ 𝗠𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝗣𝗲𝗿𝘀𝗽𝗲𝗰𝘁𝗶𝘃𝗲𝘀: Analyze your data with three different views: Time Order, Left Heavy, and the super useful Sandwich view. ✅ 𝗖𝗹𝗶𝗲𝗻𝘁-𝗦𝗶𝗱𝗲 𝗮𝗻𝗱 𝗦𝗲𝗰𝘂𝗿𝗲: It runs entirely in your browser. No profiling data is ever sent to a server. 🔗 Link to repo: github(dot)com/jlfwong/speedscope --- ♻️ Found this useful? Share it with another builder. ➕ For daily practical AI and Python posts, follow Banias Baabe.

Meta built a Python type checker in Rust that makes mypy feel like dial-up. 🔥 Pyrefly already has 5.4k stars, and it's not hard to see why. 𝗛𝗲𝗿𝗲'𝘀 𝘁𝗵𝗲 𝗱𝗲𝗮𝗹: Python's type checking story has always been slow and clunky. Pyrefly rewrites the whole thing from scratch in Rust (92% of the codebase). Giving you a lightning-fast type checker AND a full language server in one tool. Install it with pip, point it at your code, done. 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀 👉🏽 Three-step pipeline: resolve module exports, convert modules to bindings, solve bindings across your entire project 👉🏽 Uses Type::Var placeholders for recursive and unknown types that resolve later 👉🏽 Module-centric design prioritizing raw speed over fine-grained identifier solving 𝗪𝗵𝗮𝘁 𝘆𝗼𝘂 𝗴𝗲𝘁 👉🏽 Type inference that auto-detects variables and return types without annotations 👉🏽 Flow-sensitive types that track control flow (if/else, isinstance checks) 👉🏽 Incremental checking and parallel processing for large codebases 👉🏽 IDE support for VSCode, Neovim, and Zed out of the box 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 👉🏽 Modular Rust crate architecture (pyrefly_util, pyrefly_types, pyrefly_config) 👉🏽 MIT licensed, 153 contributors, 11k+ commits 👉🏽 Browser sandbox so you can try it without installing anything 👉🏽 Biweekly office hours and an active Discord community Python type checking has been an afterthought for too long. A Rust-powered checker that doubles as your language server could finally make it a first-class experience. 𝘈𝘳𝘦 𝘺𝘰𝘶 𝘴𝘵𝘪𝘭𝘭 𝘶𝘴𝘪𝘯𝘨 𝘮𝘺𝘱𝘺, 𝘰𝘳 𝘩𝘢𝘷𝘦 𝘺𝘰𝘶 𝘴𝘸𝘪𝘵𝘤𝘩𝘦𝘥 𝘵𝘰 𝘴𝘰𝘮𝘦𝘵𝘩𝘪𝘯𝘨 𝘧𝘢𝘴𝘵𝘦𝘳? 🔗 Link in comments #python #rust #opensource #meta

Day 91 of my Python Journey 👨🏾💻🔥: Progress Update: Implemented Light/Dark Mode Toggle 🔥 I upgraded my GPA Calculator project by adding a theme toggle feature that allows users to switch between Light Mode and Dark Mode dynamically. Adding a theme switch makes the app feel more polished, modern, and user-friendly. ✨️ How It was Implemented: To implement this feature, A function called toggle_theme(e) handles the switching logic. Inside this defined function: • page.theme_mode property allows to check whether the app is currently in dark (ft.ThemeMode.DARK) or light (ft.ThemeMode.LIGHT) • To switch between themes if current theme is dark, page.theme_mode is the property that does the main switching ✨️ if current themes is dark it switches to light and vice-versa • ft.IconButton() was the flet tool used to create the icon button this on-click parameter connects the button to the toggle theme function, making it interactive • theme_btn.icon updates the button icon based on the current theme state, making the UI intuitive. • Finally, After changing the theme and button properties, page.update() forces the UI to re-render and reflect the new changes immediately. Implementing this feature helped understand: ~ How to manipulate global UI properties in Flet. ~ How dynamic UI updates work. ~ How small UX improvements elevate a project significantly ✨️ Feels good to add another functional UI feature ✨️👏🏾 The project keeps getting better 🚀 #Python #Flet #SoftwareDevelopment #GUI #LearningInPublic #UIUX #DarkMode #ProjectBasedLearning #100DaysOfCode

When someone new joins a Python codebase, their first PR tells you everything: single-letter variables, inconsistent imports, functions that return str | None | dict depending on the mood of the day. We have onboarding docs for this, but tooling is what actually enforces it. My default move is three things: Ruff, Pyright, and pre-commit. Ruff with format + lint, fix-on-save, and import sorting. They hit save and the file snaps into shape, which alone kills most style discussions before they ever reach a PR. Then Pyright via Pylance in standard mode. Not strict, because strict in Python is counterproductive unless the whole codebase is designed for it. Standard catches wrong argument types, None-access, and missing returns, the stuff that otherwise shows up as runtime bugs or reviewer nitpicks. Once people see type issues flagged while they're still typing, something clicks and they stop shipping avoidable bugs. That peace of mind is something I never want to go back from. Pre-commit makes all of it non-optional. The same checks run for everyone, every time, before the code even gets pushed. It turns "we should all follow the coding guides" into "you literally can't commit without following them." That's the difference between a convention and a guarantee. With this, reviews actually focus on correctness, design, and edge cases instead of formatting and obvious type errors. You're moving the stuff humans shouldn't be wasting time on to machines that do it better and faster, and you're doing it at the cheapest possible point, before it ever hits a PR. I remember when I first set this up for myself and it felt like someone turned the lights on. I want that for everyone joining the team. What's the first thing you set up when onboarding someone new? #Python #SoftwareEngineering #CodeQuality #DeveloperExperience #DevTools

We just released a big update to Rowan's Python API (v3.0.0), with the goal of making it easier for (1) regular programmers, (2) people using Claude Code/Codex, and (3) "AI scientists" to build atop Rowan. Every workflow now returns a typed output result that mirrors the web interface, so you don't have to guess what arguments you're going to get back! We've also conformed the workflow API to the Python futures pattern, which enables e.g. streaming results without extensive scripting logic. We're planning to add usage predictions, webhooks, guides/skills, better errors, and a CLI in future. It should be easy to vibe-code with Rowan! If you're a current API user or interested in integrating Rowan into an agentic system, we'd love to learn what we can be doing better: please reach out! Full release announcement here: https://lnkd.in/gPzNnsnE Updated API documentation: https://lnkd.in/gsaB7wuZ GitHub repo: https://lnkd.in/gz5c-HvH

Book Recommender System | Python, Flask, Machine Learning, Pandas • Redeveloped a machine learning–based Book Recommender System and deployed it as a web application using Flask • Integrated trained ML model and similarity matrix to provide real-time book recommendations • Converted the standalone ML project into a full-stack web application with interactive frontend using HTML, CSS, Bootstrap, and JavaScript • Displayed book details such as title, author, ratings, votes, and cover images dynamically • Used Pandas and Pickle for data processing and model integration