Python AI Coding Agent with Gemini for Predictable Code Updates

Built a GitHub Code-Review-Fixing Python Agent using AgentApps 🔗 Library: https://lnkd.in/gVgbt5xi 🔗 Source code: https://lnkd.in/gNqvx6TK This agent automatically: ✅ Reviews a GitHub repository ✅ Identifies logical and runtime issues in code ✅ Creates a new branch for fixes ✅ Generates corrected code safely ✅ Applies the fixes along with code review summary In my demo video, the agent detected a division-by-zero issue in a Python file and automatically generated a fixed implementation along with a detailed review log — without any manual debugging. Learning how AI agents + GitHub automation can significantly improve developer productivity 🚀 More improvements coming soon! #Python #AI #GitHub #Automation #AgentApps #Learning Developed using the AgentApps library by Abdul Mannan K.

Built a Python CLI To-Do List Manager that lets users add, view, delete, and complete tasks — with data saved to a file so tasks persist between sessions. This project helped me practice: ✔ File handling (read/write JSON) ✔ Error handling ✔ Functions & clean code structure ✔ Menu-driven user interaction Syntecxhub

I built CodeSurgeon — a Python CLI tool that performs deep architectural analysis of Python projects and generates: 📊 Cyclomatic complexity metrics 🔁 Circular dependency detection ♻️ Duplicate function detection 📁 Large file detection 🧠 Architecture health score (0–100) 🚨 CI mode that fails pipelines when quality drops 📦 Full structured JSON output

Did you know? Docker Model Runner (DMR) now supports the Anthropic Messages API format. This means you can use the Anthropic Python/TypeScript SDK against local open-source models during development, then seamlessly switch to Claude in production.

So I put together a python library that handles the core of any agent harness--Managing the context window. It's called, because naming is hard, harness-utils. It's but around 3 fundamental operations that impact how you manage the context window: - Truncation - Pruning - Summarization Basically, because when you're in a session with an LLM on some task, not just coding, you provide the LLM with context and it responds back on its own. These messages can fill up your context window, so clever care is needed to make sure you don't blow your context window in short order...this is where harness-utils comes into play. Anyway, if you're building such an agent and want to plug it in and see how it manages, go for it. Let me know what you think about it too. https://lnkd.in/eZRre5JD

Still spending your weekends dragging files into folders? This 5-line Python automation script cleans your Downloads folder instantly. Whether you're a beginner or a pro, using the pathlib library is a game-changer for desktop organization. Save this script for your next digital cleanup and follow for more daily coding hacks! #PythonProgramming #CodingHacks #AutomationTools #LearnToCode2026 #ProductivityHacks

One nice improvement in the latest DBOS Python release–you can now call async steps concurrently with native syntax from a workflow, and it all just works. To make that possible, we added code to set a unique ID for each step before it begins async execution, so even though the step executions overlap they’re checkpointed deterministically.

🚀 Python Automation Project | Real-World Workflow I recently built an end-to-end Python automation tool that streamlines everyday file management tasks. 🔹 Organizes files automatically by category 🔹 Generates Excel reports with summaries & charts 🔹 Creates ZIP backups with retention policy 🔹 Sends reports & error alerts via SMTP email 🔹 Fully controlled using CLI arguments This project helped me understand how real automation systems are designed — from logging and error handling to secure email notifications using environment variables. 💡 Technologies: Python, Pandas, OpenPyXL, SMTP, CLI (argparse), Logging Always learning, always building. 💻✨ #Python #Automation #SoftwareEngineering #LearningByDoing #BackendDevelopment #CLI #GitHub

Just launched: Python SDK to turn real data into labeled training datasets 🛠️ Go from messy raw data → thousands of questions + labels in minutes. SDK Features: - Generate training data directly from real public data sources or your own documents - Fully customizable outputs (instructions, examples, requirements) - Provenance with every row - Run quality checks, scoring, and filtering automatically This is an early beta, so we’d love feedback on what works well, what’s missing, and what you’d like to build with it. GitHub repo linked below👇

🚀 Project Showcase: Building a Python Virtual Assistant I’m excited to share Jarvis, a custom automation tool I built to explore the mechanics of voice-controlled systems and Python scripting. The goal was to create a lightweight, modular assistant capable of handling system tasks and retrieving information hands-free. ⚙️ Technical Highlights: Voice-to-Action Pipeline: Integrated SpeechRecognition for input and pyttsx3 for real-time audio feedback, creating a seamless conversational loop. System Automation: utilized os and requests libraries to programmatically control applications and fetch web data. Modular Architecture: Recently refactored the codebase to separate concerns, including a dedicated musicLibrary module for dynamic media handling. Version Control: Managed the development lifecycle using Git/GitHub to handle feature branches and code updates efficiently. 💻 View the Source Code: The repository is open-source and includes setup instructions. 🔗 https://lnkd.in/gzw3nfdF I’d appreciate any feedback on the code structure or suggestions for new automation features to add! #Python #SoftwareDevelopment #Automation #OpenSource #GitHub #Coding #ComputerScience