Optimize GitHub Copilot for Agentic AI Projects with Context

🚀 AI CHEAT CODE #026 🚀 Most devs use GitHub Copilot wrong. Here's the trick that 10x'd my output 👇 Stop accepting single-line suggestions. Use Copilot Chat with THIS prompt pattern: Step 1: Highlight your entire function Step 2: Open Copilot Chat (Ctrl+I) Step 3: Type: "Refactor this for readability, add error handling, and write unit tests" You just got 3 tasks done in 10 seconds. 🤯 Step 4: Ask: "What edge cases am I missing?" Step 5: Paste those edge cases directly into your test file ⚡ Pro Tip: Add your tech stack to the prompt: "Refactor for Python 3.11 with FastAPI best practices" — Copilot tailors everything perfectly. Drop a 🔥 if this saved you time today! Save this post for your next code review session. #AI #GitHubCopilot #Coding #DevProductivity #SoftwareEngineering #Python #CloudComputing #DevOps

I've been stress-testing the new Codex desktop app for three weeks and honestly? It's the first AI coding tool that doesn't make me want to throw my laptop out the window. The context awareness is scary good - it actually remembers what I was working on yesterday and picks up conversations mid-thread. But here's what nobody's talking about: the real magic isn't the code generation, it's how it handles debugging existing codebases. I fed it a gnarly legacy Python script with zero documentation and it mapped out the entire data flow in minutes. Sure, GitHub Copilot writes decent boilerplate, but Codex actually understands architecture. Still has weird hallucinations with newer frameworks though. #OpenAICodex #DeveloperTools #AIcoding

I've been stress-testing the new Codex desktop app for three weeks and honestly? It's the first AI coding tool that doesn't make me want to throw my laptop out the window. The context awareness is scary good - it actually remembers what I was working on yesterday and picks up conversations mid-thread. But here's what nobody's talking about: the real magic isn't the code generation, it's how it handles debugging existing codebases. I fed it a gnarly legacy Python script with zero documentation and it mapped out the entire data flow in minutes. Sure, GitHub Copilot writes decent boilerplate, but Codex actually understands architecture. Still has weird hallucinations with newer frameworks though. #OpenAICodex #DeveloperTools #AIcoding

Excited to share my latest open-source project — CodeLedger AI-assisted coding moves fast. You iterate rapidly with LLMs, ship features in hours, and six months later, no one remembers why anything was built the way it was. CodeLedger solves this by auto-generating structured documentation during development, capturing architecture decisions, component logic, and integration patterns as your project evolves, not after. Who it's for: → Solo devs who vibe-code with AI and want to remember what they built → Teams onboarding new members to AI-iterated codebases → Anyone tired of writing docs as an afterthought Built with Python · Available on PyPI · MIT Licensed pip install codeledger Check it out → https://lnkd.in/dG8B6Bch Feedback, stars, and contributions are welcome! #OpenSource #Python #AI #DeveloperTools #Documentation #CodeLedger #VibeCoding

Developers are flocking to luongnv89/claude-howto, a visual guide to Claude Code that's making fast-moving AI workflows easier to steer and reuse in real projects. This project is more than just a tutorial – it's a practical solution to the complexity of LLM and agent workflows. By providing a clear learning path and example-driven templates, Claude How To is helping teams overcome the common pitfalls of mastering Claude Code. At its core, Claude How To is a collection of 10 tutorial modules covering every Claude Code feature, from slash commands to custom agent teams. This comprehensive approach is a breath of fresh air in a landscape where most resources leave developers scratching their heads. By focusing on the practical application of Claude Code, this project is changing the way developers work with LLM and agent workflows. Key benefits of Claude How To include: - A clear learning path that helps developers master Claude Code features - Example-driven templates that bring immediate value to real projects - A comprehensive approach that covers every aspect of Claude Code - Built with Python, making it accessible to a wide range of developers The traction makes sense: a repository sitting at #3 with around 27,548 new stars is usually solving a problem people can feel immediately. With its recent commits and active development, it's clear that Claude How To is here to stay. Repo: https://lnkd.in/gV8nN-6t #GitHub #OpenSource #GitHubTrending #LinkedInForDevelopers #Python #ClaudeHowto #ClaudeCode #Guide

Turned recently exposed Claude Code artifacts into a practical #cookbook for building production-grade coding agents. It does NOT include "Claude Code" Code itself. It gathers key patterns and architectures into clear documentation for learning with practical Python examples. It also includes an Agents.md file, so you can plug the repo into your own agent and get guided help in building a production-ready coding agent. https://lnkd.in/gHJgfXFd #CodingAgents #AI #playbook

"Did you know that many developers spend up to 50% of their time figuring out how to explain a single concept in OpenClaw? That's where short-form video scripts come into play—they're a game-changer. Here are three tips to create 60-second video demos that effectively teach one OpenClaw concept: 1. **Focus on One Key Idea:** Narrow down your topic to a single concept. If you’re demoing OpenClaw's auto-scaling feature, start by showing the problem it solves, then walk through configuring it step-by-step. Clarity is king in short form content. 2. **Craft a Storyboard:** Outline your narrative before hitting record. This helps you stay on track and not miss any critical points. Break it down into intro, concept explanation, and conclusion. Think of it as vibe coding your video—iterative and responsive to your insights. 3. **Use Python Snippets for Clarity:** Annotate your code to guide viewers through your logic. For example, use comments to explain why you're setting specific parameters. Here’s a quick look: ```python # OpenClaw auto-scaling config def setup_auto_scale(threshold): # Define metrics that trigger scaling print("Setting up with threshold:", threshold) # Call scaling API scale_cluster(threshold) ``` Which of these was new to you? How do you ensure your video scripts are engaging and informative while staying within 60 seconds?" #AIConsultant #BusinessAutomation #AIAgents #OpenClaw #RPA

Kedro vs non-kedro framework Kedro has a clear advantage over a plain notebook-and-script workflow when a project starts to grow. It helps organize pipelines, makes dependencies easier to follow, and keeps data projects more reproducible and maintainable. In practice, Kedro is not ideal for every case. For small prototypes, quick proofs of concept, or very lightweight workflows, its project structure can add extra overhead. It also requires some upfront discipline and a learning curve, which may not be ideal for teams that need to move very fast or already have an established orchestration stack. For example, in a churn prediction project, a non-Kedro setup might rely on separate notebooks for each step, while Kedro would define each step as a reusable node in a pipeline. The tradeoff is clear: more structure and consistency, but less flexibility for rapid experimentation. What are other disadvantages you have detected while using the Kedro framework? #Kedro #DataEngineering #MLOps #Python #DataScience #DataPipelines #MachineLearning

After several days of building, debugging, and refining, I successfully structured and shipped a reproducible Machine Learning project setup using UV + Git 🥰 I simply wanted to build a clean and scalable foundation for ML workflows following my recent class on Git and GitHub Workflow. What I implemented: • Initialized a structured Python ML project using UV • Managed dependencies with uv.lock for reproducibility • Set up a clean virtual environment workflow • Organized project structure for real-world ML development • Integrated Git for proper version control • Successfully pushed the complete workflow to GitHub This is not just setup, it is a production-style ML foundation that ensures consistency, reproducibility, and scalability across environments. What I learned: • How modern Python tooling (UV) simplifies dependency management • Why reproducible environments matter in ML engineering • The importance of clean project architecture before model building • How Git integrates into real ML workflows This is the foundation I will continue building on as I move into full machine learning projects. Mentorship for Acceleration 🔗 GitHub Repository: https://lnkd.in/eqRmyY5p #MachineLearning #Python #DataScience #Git #MLEngineering #UV #BuildInPublic

GitHub moves faster than we can "git pull." I spent the morning auditing this week's Top 12 trending repositories so you don't have to drown in your "Star" list. The Weekly Signal: Dominant Language: Python is leading the pack with 6 out of 12 top repos. AI agents, foundation models, and dev tooling are all written in Python this week. Top Pick: hermes-agent by NousResearch gained 14,811 stars this week. A modular AI agent framework that supports multi-LLM workflows out of the box. Rising Star: openscreen by siddharthvaddem pulled 13,938 stars. An open-source Screen Studio alternative. Free product demos with no watermarks. I've mapped out the technical specs, practical use cases, and star growth for all 12 repos into a clean dashboard. Which of these are you cloning today? Let's talk architecture in the comments. #OpenSource #GitHub #AI #Python #DeveloperTools #TechTrends