Artificial Intelligence

The new feature for custom script

Not sure if this "PAT" part will be the final solution, or i can make i better But the goal is to have a community to share custom scripts

You asked for it and you got the answer, very helpfull information / Tips you are getting, beside the /diff, but it good to be reminded on what you can do better, learning is the key here

That's a very strong feature, that can improve you agent in the repo you are working in, and also for you going forward..... and a reminder: Planning is everything, get the information out of your head and in to the agent knowlegede

## The Problem Every CISO Knows Your Azure Entra ID tenant is the front door to your entire cloud estate. Behind that door: hundreds of app registrations with over-permissioned secrets, service principals with stale credentials, conditional access policies with gaps you don't know about, and attack paths that chain these misconfigurations into real breaches. Microsoft provides tools. They're scattered across Defender, Entra admin center, Azure Portal, and Graph API queries you run manually. None of them give you a unified, scored, actionable view of your Entra security posture — and none of them let your team extend the scanning with organization-specific checks.

I am working on a Azure security scanner tool, and it gets new feature on as we go. My last idea i that i would make it possible to upload/make phyton script inside the apps dashboard, and make it run the scan, its must be with some templates etc, what you see below is a part of the Roadmap that is sent to Copilot that uses my Agent flow to build apps

Here is a structured, high‑quality set of prompt samples specifically designed for testing AI‑coder apps like Copilot, Cursor, Claude Code, Windsurf, Kiro, Gemini Code Assist, etc. These prompts are crafted to expose differences in: - multi‑file reasoning - refactoring quality - architecture planning - debugging - agentic workflows - spec‑driven development - UI generation - API design - database modeling They are ideal for benchmarking how well each tool performs in real development scenarios.

GitHub Copilot sits inside the largest developer ecosystem on the planet, but the **AI coding‑assistant market in 2026 is no longer dominated by a single tool**. Cursor, Claude Code, Windsurf, and Kiro have all carved out strong positions, each optimized for different workflows. The landscape is now a mix of IDEs, agents, and terminal‑native tools competing for developer attention. The sections below synthesize the most recent market‑relevant data and comparisons from 2025–2026, including usage numbers, valuations, and positioning. Citations appear throughout. I will make a Blogpost on Google, the one that i think can end up being a hard competitor to Github Copilot

https://www.youtube.com/watch?v=6K5UW594BUc&list=LL&index=2&t=89s&pp=iAQBsAgC

The video compares Ralph loops with GitHub Copilot CLI’s Autopilot + Fleet mode to see which approach does a better job porting a real application. The creator walks through how each method works, runs both on the same PRD, and then compares the final results. https://www.youtube.com/watch?v=6K5UW594BUc&list=LL&index=2&t=89s&pp=iAQBsAgC

The new **shadcn/ui Theme Generator** makes it easier than ever to create a polished, consistent design system for your project. Once you’ve generated your theme on the website, the next step is bringing it into your codebase. These are the core commands you’ll use to set everything up. ---

Most developers love the flexibility of shadcn/ui, but let’s be honest—many projects end up looking the same. Or worse: mismatched colors, awkward font pairings, and dark modes that feel like an afterthought. The components are great, but the theming often isn’t. The new shadcn/ui Theme Generator changes that completely. It gives you a professional, consistent design system in seconds—no design degree required. This post breaks down what the tool does, why it matters, and how it solves one of the biggest problems in modern UI development. good Youtube on this subject https://www.youtube.com/watch?v=m-gIqQTHcAY&list=WL&index=1

In 2026, GitHub Copilot stands as the most widely adopted AI coding assistant, powering the workflows of millions of developers and thousands of organizations worldwide. What began as an autocomplete tool has evolved into a sophisticated, agentic platform—capable of understanding entire codebases, executing multi-step tasks, integrating with a vast ecosystem of tools, and delivering productivity gains that are reshaping the software development landscape. This comprehensive guide explores the latest updates, features, and strategic shifts in GitHub Copilot, drawing on official GitHub sources, hands-on reviews, and community insights. Whether you’re an individual developer, a team lead, or an enterprise architect, this post will help you navigate Copilot’s new capabilities, understand its pricing and plans, and make informed decisions about integrating AI into your development workflow.

Modern AI applications demand fast rendering, scalable APIs, and efficient data pipelines. With Next.js 15, React 19, and today’s AI‑native tooling, developers can build production‑grade AI systems faster than ever. This guide walks through the core technologies, architectural patterns, and updated Next.js 15 features that matter when building real AI applications.