🚀 Project Showcase | Finance AI – Flask-Based Python Web Application 💹 Built and deployed Finance AI, a Python Flask web application that demonstrates backend routing, Python module integration, and interactive web workflows. As part of the project, I integrated Python’s built-in antigravity module to showcase creative use of Python features within a real web application. 💡 Project Overview: Finance AI exposes Python functionality through Flask routes, enabling user interactions to trigger backend logic and demonstrate Python behavior via a web interface. The project emphasizes clean architecture, modular design, and deployment readiness. 🔍 Key Highlights: ✅ Flask routing & request handling ✅ Python standard library integration (antigravity) ✅ Dynamic backend–frontend interaction ✅ Deployment-ready Flask application structure 🛠 Tech Stack: 🔹 Python | Flask 🔹 HTML | CSS 🔹 Git | Production-oriented setup 📌 Project Flow (Quick Walkthrough): 1️⃣ Flask-based Python web application 2️⃣ Backend in Flask, frontend with HTML & CSS 3️⃣ Flask server handles incoming requests 4️⃣ UI actions mapped to backend routes 5️⃣ Routes execute server-side Python logic 6️⃣ antigravity module triggered via Flask 7️⃣ Backend processes and responds to client 8️⃣ Clean structure ensures smooth execution 9️⃣ Designed for real-world deployment 🔟 Demonstrates Flask routing & backend fundamentals This project strengthened my backend development skills and allowed me to explore creative Python features in a real web application. #Python #Flask #BackendDevelopment #WebApplications #ProjectShowcase #StudentDeveloper #FinanceAI #LearningByDoing

🏡 House Price Prediction App! I developed a Machine Learning-based web app using Python & Streamlit that estimates house prices based on important factors such as: ✔️ Area (in square feet) ✔️ Number of bedrooms ✔️ Property age ✔️ Distance from the city center ✨ Highlights of the project: Interactive and clean UI built with Streamlit Easy-to-use input fields for users Integrated ML model for accurate predictions Instant results display 🎯 What I learned while building this: Data cleaning & preprocessing techniques Working with regression algorithms Deploying ML models into web apps Designing simple and effective user interfaces 🛠️ Tech Stack: Python | Pandas | NumPy | Scikit-learn | Streamlit 🔗 Check out the project here: https://lnkd.in/ghhUHEGM 💡 Next improvements I’m working on: 🔹 Enhancing model performance 🔹 Adding visual analytics & charts 🔹 Improving UI with advanced features #MachineLearning #Python #AI #DataScience #Streamlit #Projects #BuildInPublic #Learning

I've been building web apps with Next JS and have used React Native as a student for a while Recently I decided to explore AI/ML, I wanted to begin from the fundamentals So instead of jumping straight into machine learning models, I started with Python and built a small project: a CLI Task Manager. GitHub Repo - https://lnkd.in/gej-ZwFG Features: • Add tasks • Remove tasks • Edit tasks • Mark tasks complete • Filter tasks • Persistent storage using JSON This helped me refresh my python concepts like dictionaries, file handling, functions, and error handling. Next step: learning web scraping with Python. Trying to learn AI/ML the right way by building things step by step. #Python #AI #MachineLearning #WebDevelopment #BuildInPublic

🚀 𝐃𝐚𝐲 𝟐: 𝐌𝐚𝐬𝐭𝐞𝐫𝐞𝐝 𝐑𝐨𝐮𝐭𝐢𝐧𝐠 & 𝐏𝐚𝐭𝐡 𝐏𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫𝐬 𝐢𝐧 𝐅𝐚𝐬𝐭𝐀𝐏𝐈! ⚡ The journey into FastAPI continues! Today was all about how we handle data directly within the URL. Coming from a Django background, I’m loving how clean and intuitive the routing feels here. 𝙃𝙚𝙧𝙚’𝙨 𝙬𝙝𝙖𝙩 𝙄 𝙩𝙖𝙘𝙠𝙡𝙚𝙙 𝙩𝙤𝙙𝙖𝙮 : 📍 𝙋𝙖𝙩𝙝 𝙋𝙖𝙧𝙖𝙢𝙚𝙩𝙚𝙧𝙨 & 𝙃𝙏𝙏𝙋 𝙈𝙚𝙩𝙝𝙤𝙙𝙨 : I explored how to capture dynamic values from the URL using {curly_brackets} and how they interact with standard HTTP methods like GET and POST. 🔢 𝙋𝙖𝙩𝙝 𝙋𝙖𝙧𝙖𝙢𝙚𝙩𝙚𝙧𝙨 𝙬𝙞𝙩𝙝 𝙏𝙮𝙥𝙚𝙨 : This is a game-changer! By using Python type hints (like : int or : str), FastAPI automatically handles: 𝗗𝗮𝘁𝗮 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻:It returns a clear error if the wrong type is sent. 𝐃𝐚𝐭𝐚 𝐂𝐨𝐧𝐯𝐞𝐫𝐬𝐢𝐨𝐧: It automatically converts the URL string into the correct Python type. 🔄 𝘿𝙤𝙚𝙨 𝙊𝙧𝙙𝙚𝙧 𝙈𝙖𝙩𝙩𝙚𝙧? (𝙋𝙖𝙩𝙝 𝙋𝙖𝙧𝙖𝙢𝙚𝙩𝙚𝙧 𝙊𝙧𝙙𝙚𝙧𝙨) : I learned that in FastAPI, the order of your route functions matters. If you have a static path like /users/me and a dynamic path like /users/{user_id}, the static one must come first to avoid being "caught" by the dynamic parameter! 📋 𝙋𝙧𝙚𝙙𝙚𝙛𝙞𝙣𝙚𝙙 𝙑𝙖𝙡𝙪𝙚𝙨 : Using Python’s Enum, I learned how to restrict a path parameter to a specific set of valid options. This makes APIs incredibly robust and self-documenting. 🛠️ 𝙋𝙖𝙩𝙝 𝘾𝙤𝙣𝙫𝙚𝙧𝙩𝙚𝙧𝙨 : I dived into using :𝗽𝗮𝘁𝗵 to capture entire file paths (like files/images/photo.jpg) within a single parameter. 𝐂𝐮𝐫𝐫𝐞𝐧𝐭 𝐒𝐭𝐚𝐭𝐮𝐬:Feeling more confident with every line of code. The way FastAPI handles documentation and validation simultaneously is a massive productivity boost! 🛠️💻 #FastAPI #Python #BackendDevelopment #WebAPI #LearningJourney #Coding #SoftwareEngineering #PythonDeveloper #Day2

Typed-FFMpeg 4.0 Release I built typed-ffmpeg, a Python package that lets you build FFmpeg filter graphs with full type safety, autocomplete, and validation. It’s inspired by ffmpeg-python, but addresses long-standing issues like lack of IDE support and fragile CLI strings. What’s New in v4.0: TypeScript support — Full TypeScript bindings with the same API as Python. Works in Node.js and the browser. Code-generated from FFmpeg source, so every filter and option is typed. parse() — reverse-engineer any FFmpeg command — Paste an FFmpeg CLI string and get back a typed filter graph object, in both Python and TypeScript. Useful for learning, migrating legacy scripts, or building tools on top of FFmpeg. Per-version packages — Instead of one 10 MB bundle with all FFmpeg versions, you now install only what you need: pip install typed-ffmpeg-v7 (~300kb). Packages exist for FFmpeg 5 through 8. FFmpeg 8.0 support — Full compatibility with the latest FFmpeg release. GitHub: https://lnkd.in/gHZAV7QG I’d love feedback, bug reports, or ideas. Thanks! — David (maintainer)

🚀 From Static Data to an Interactive Web App! 🚀 I recently decided to take my programming skills to the next level by heavily upgrading one of my data analysis projects. What started as a simple Python script to process the famous Superstore dataset is now a fully interactive, live web dashboard! 📊 Here is what I accomplished:  🧠 Learned how to structure data using Python and Pandas (specifically using .groupby() to manipulate data dynamically). 🛠️ Rebuilt static charts into interactive ones using Streamlit. ⚙️ Mastered version control by resolving conflicts via Git terminal commands. 🌐 Deployed the final application to the cloud so anyone in the world can interact with the data! I am incredibly proud of how much I learned about environments, dependencies, and taking a project from code to the cloud. 🔗 Try the live dashboard here: https://lnkd.in/dapceAK5  💻 Check out the code on my GitHub: https://lnkd.in/det-RQjc #Python #DataAnalysis #DataScience #Streamlit #Pandas #CodingJourney #BuildInPublic

Surface syntax for Clojure — try it live in your browser (I don't feel funny today, so this is real, not a joke ;) ) During my PhD in machine learning I worked in Clojure while everyone around me used Python. I couldn't show my code to colleagues or supervisors without first explaining parentheses. That initial barrier only takes a few days to overcome. But a few days is infinity in a meeting, a talk, or a code review. So I built Superficie: a bidirectional renderer that translates Clojure S-expressions into familiar syntax and back. ;; Clojure (defn process-users [users]  (->> users (filter :active) (map :name) (sort) (take 10))) ;; Superficie defn process-users [users]:  users |> filter(:active)     |> map(:name)     |> sort()     |> take(10) What makes this interesting technically: - Roundtrips. Parse superficie → Clojure forms → render back. 85% of 699 files across 14 real-world projects (core.async, Datahike, Malli, Babashka, …) roundtrip to identical forms. - Resilient parser. Four-stage pipeline inspired by Racket's shrubbery notation — the grouper never throws; mismatched brackets become error nodes embedded in a valid tree. You get precise error messages with source location and underlines, not "unexpected EOF." - Extensible operators. Infix arithmetic, threading (|>), comparisons — all first-class and user-extensible. Define your own with defsupoperator. - Adaptive blocks. Define a macro with :superficie/role metadata and it is immediately available as block syntax — no config, no registration step. The parser discovers it through namespace resolution at parse time. The block syntax extension over M-expressions is inspired by Julia. - Runs everywhere. JVM, Babashka (instant startup), browser via SCI. The npm package ships Node, browser, and REPL bundles. Try it yourself — paste any Clojure code and see it rendered live, or evaluate superficie syntax directly in the browser REPL: https://lnkd.in/gnpmaJy3 Source: https://lnkd.in/gP6QJ7ey #clojure #opensource #programming #syntax #racket #julia #functionalprogramming

So cool! I have been experimenting a bit to see how this can be built in to Calva. So, say you want to reason about some code with a colleague and they find the prefix notation hard to parse. You just split open it in a more familiar syntax for them. And in my experiments I have had two-way editing working too. You think I should spend some more time on this and actually make it a thing in Calva?

Most FastAPI tutorials stop at: -- pip install fastapi But real-world projects don’t. While working on backend projects, I kept running into: - dependency chaos - environment configuration issues - messy secret management So I explored a better approach using uv and put together a write-up: FastAPI Beyond pip - Master Secrets & Envs with uv In this article, I cover: • a cleaner way to manage dependencies • handling environment variables properly • structuring FastAPI projects more like production systems Here’s the full breakdown: https://lnkd.in/eaU8SFCV I’m currently exploring opportunities and always open to connecting with others working in backend and Python. How are you handling environments and secrets in your projects? #FastAPI #Python #BackendDevelopment #DevTools #OpenToWork #SoftwareEngineering