Florin Lungu’s Post

GitHub Copilot just got smarter—and developers will feel the difference immediately.  We’ve rolled out a new embedding model that makes code search in VS Code faster, smarter, and lighter on memory.   Why does this matter for our customers? Because better code retrieval means:  💡 Developers find the right snippets faster—no more near misses.  💡2x higher throughput and 8x smaller index size—speed without the memory tradeoff.  💡Up to 113% lift in code acceptance for Java and C#—real impact, not just theory. This isn’t just a technical upgrade. It’s a strategic advantage for every engineering team building with GitHub Copilot. Whether you're navigating monorepos, debugging, or accelerating test coverage, Copilot now delivers sharper context and smarter suggestions. This means faster time to market, reduced dev friction, and more reliable AI-powered workflows. It’s how we turn AI innovation into customer value. Big kudos to the GitHub and Microsoft teams driving this innovation. Let’s keep pushing the boundaries of what AI can do for software development.

GitHub just shipped a new embedding model for Copilot that makes code search in VS Code 37.6% more accurate at retrieving the right snippets, with 2x faster throughput and an 8x smaller memory footprint. The secret here is training on "hard negatives" -code examples that look correct but aren't, which teaches the model to distinguish between "almost right" and "actually right." The update is already live in VS Code for all Copilot https://lnkd.in/dMP83Hhq

🚀 Ever feel like you're drowning in a sea of GitHub repos — wishing there was a magic wand to instantly create polished READMEs and licenses? 😩 I’ve been there! As developers, we love coding... but let’s be honest — writing the README is always the last thing on our to-do list. That pain sparked an idea during one frustrating weekend debugging session: “There HAS to be a better way!” 🤔 And so, I built Free Readme & License Generator — an AI-powered tool that automatically creates professional Readme and LICENSE files for your GitHub projects. 🎉 ⚙️ How It Works Just enter your: GitHub repository URL Username Preferred license (MIT, Apache 2.0, or GPL-3.0) 💡 It then analyzes your repo and generates clean, well-structured documentation — all in seconds. 🧠 Tech Stack • FastAPI — For a blazing-fast Python backend • OpenAI API — To generate intelligent, structured content • React + Tailwind CSS — For a smooth, responsive UI • Docker — For consistent deployments ✨ Key Features Automated README Creation — No more blank markdowns! License Selector — Choose from common open-source licenses. Smart File Analysis — AI scans your repo to tailor the README. Simple, Clean UI — Because dev tools should be delightful. 💪 Challenges & Learnings Parsing code intelligently was harder than expected — making AI understand project structure took multiple prompt iterations. Also learned how to optimize OpenAI API costs and manage rate limits effectively. This project helped me grow in: AI integration with real-world apps Backend + frontend synergy Building for developer experience (DX) 🔗 Try it out 🧩 GitHub Repo: https://lnkd.in/eeU8gp7g 🌐 Live Demo: https://lnkd.in/ekGJBn8e I built this to save hours of repetitive work — and it’s already made my projects smoother to publish. What’s one thing you wish was automated in your dev workflow? 👇 Would love to hear your thoughts! 💬 #AI #OpenAI #DeveloperTools #GitHub #Automation #Coding #Productivity #Python #React #FastAPI #DevTools #WebDevelopment #MachineLearning #Innovation #OpenSource #SoftwareEngineering #TechInnovation #BuildInPublic #LLM #CloudComputing #Developers

Two weeks ago, I caught myself spending 5 minutes trying to write a commit message for a simple bug fix. That's when it hit me: We have AI that can write code, why not commit messages? So I built gcomet. Here's what happened: Before: "fix: updated auth" (lazy, unhelpful) After: "fix(auth): handle null pointer in login validation" (clear, professional) What it does: - Analyzes your staged changes using AI (GitHub Models) - Generates professional, Conventional Commits-compliant messages in under 5 seconds - Detects sensitive data leak (API keys, tokens) before you commit - Integrates seamlessly with your existing Git workflow via hooks Why it matters: Developers spend an average of 2-3 minutes per commit crafting messages. With 10-20 commits per day, that's 30-60 minutes saved. Time better spent coding, not writing. Setup takes 30 seconds: 1. npm install -g gcomet 2. Run gcomet setup and paste your GitHub Personal Access Token 3. Start generating commits That's it. No complex configuration. No API keys to manage. Just your GitHub token and you're ready to go. It's free, open-source, and ready to use right now. If you have Node.js installed on your machine, you're 30 seconds away from never writing another commit message manually. GitHub: https://lnkd.in/dB9ruTBR NPM: https://lnkd.in/dAR2bm5z Have you wasted time struggling with commit message conventions? Try gcommit and reclaim your productivity. #OpenSource #DeveloperTools #AI #Git #GitHub #TypeScript #DevTools #Programming

🚀 Huge shift in AI-assisted software development! Forget "vibe coding." GitHub's new open-source Spec Kit is formalizing Spec-Driven Development (SDD), making your detailed specification the single source of truth for AI coding agents like Copilot, Gemini, and Claude. The Spec Kit introduces a structured, four-phase workflow that turns abstract ideas into high-quality, reliable code: /specify: Define the "what" and "why" (requirements, user stories). /plan: Define the "how" (architecture, technical constraints). /tasks: Break the plan into small, executable tasks. /implement: The AI agent generates the code, step-by-step, against the detailed spec. The result? Massive efficiency gains, early error prevention, and a shift where developers become the architects and validators, not just typists. This is how we regain control and ensure AI-generated code aligns perfectly with human intent. Time to ditch the guesswork and start building with precision! Check out the Spec Kit and dive into the future of development. #GitHub #SpecKit #AI #SoftwareDevelopment #SpecDrivenDevelopment #Coding https://lnkd.in/eKdZ7WKd

The article explores how to utilize GitHub Copilot directly within the terminal, making tasks like cloning repositories and managing pull requests more efficient. I found it interesting that integrating AI into our command-line workflow can significantly streamline operations. How do you see AI tools like Copilot influencing your daily coding tasks?

GitHub has rolled out its Copilot coding agent in general availability for paid users. Developers can now assign tasks—like bug fixes, feature work, or documentation updates—to the agent, which runs in the background via GitHub Actions, drafts pull requests, and waits for review.

GitHub Copilot vs Traditional Coding: A Developer’s New Reality There was a time when writing code meant googling for snippets, scanning Stack Overflow, and piecing together logic line by line. Now, with GitHub Copilot, the game has changed — not just in speed, but in how we think about coding. 💡 Traditional Coding: • You write every line manually • Debugging consumes 40–60% of your time • You spend hours switching between docs, forums, and IDE ⚙️ With Copilot: • It understands context and predicts your next line or function • Suggests test cases, regex, and edge cases on the fly • Acts like a 24x7 pair programmer — one that never gets tired But here’s the truth — Copilot doesn’t replace developers. It amplifies them. The real skill now is prompt-driven development — knowing how to communicate with your AI assistant effectively. As AI-driven tools evolve, developers who blend engineering expertise + prompt precision will stand out as the new generation of builders. 💬 What’s your take? Has Copilot changed your coding workflow yet? #GitHubCopilot #AIDevelopment #DeveloperProductivity #PromptEngineering #GitHub #AIForDevelopers

🤓 Using GitHub Copilot to implement a feature in DotVVM When playing intensively with AI-powered tools, I don't think we are close to AI fully replacing software engineers in general. However, for several classes of tasks, it already works. Yesterday, a new user to DotVVM, the open-source framework we build at RIGANTI, reported an issue. It wasn't anything new; it was reported many times in the past, but since it is a by-design behavior of DotVVM, it cannot be changed. Essentially, when you need to redirect to another page, DotVVM throws an exception to signal that, and the exception is subsequently caught by DotVVM and transformed into a redirect response. If you wrap the redirect call in a try/catch block and don't let this exception pass, the page will break. I wanted to write a Roslyn analyzer that would identify this pattern in the code and emit a warning years ago, because it is frustrating for new users. It would take an hour or two, but there were always more important tasks on the project. However, since we already have Roslyn analyzers in the project, with tests and a release process in place, it shouldn't be a significant problem for the AI to add just one more analyzer. So I tried it - I basically copied the question from the forum as a GitHub issue and asked Copilot to implement it. Since it was an isolated task that didn't require deep knowledge of the project itself, and there are thousands of analyzers on GitHub that were part of the training dataset for LLMs, it was a success. Copilot figured out exactly where to make code changes, created meaningful test cases, and even considered nested try/catch blocks that I hadn't mentioned. We did a code review with my colleague and proposed several changes we noticed, for example, extended the requirement to handle "when" clauses or "catch" blocks without exceptions. It is not a major feature, but there are surely hundreds of similar small improvements in DotVVM, and using AI for them can be extremely useful. #developer #dotnet #ai #copilot #github

What if your code could think with you? 👀 Meet GitHub Copilot, your AI pair programmer that turns comments into code and ideas into reality. Developed by GitHub + OpenAI, it understands context from your code editor and suggests complete functions, loops, and even tests — all in real time. No more Googling syntax or StackOverflow hunting. Just write a comment like: // fetch data from API and display user list and watch Copilot write the code for you. Why developers love it: ✅ Saves 40–50% of time writing boilerplate code ✅ Learns from your style and improves suggestions ✅ Integrates directly in VS Code, JetBrains, and Neovim ✅ Supports dozens of languages (JS, Python, Go, Java, etc.) Pro Tip: Use Copilot for repetitive patterns — like writing hooks, testing code, or CRUD APIs — but always review logic for business-critical apps. 🧩 Copilot isn’t replacing developers — it’s amplifying them. #AITools #GitHubCopilot #Developers #Productivity #TechChallenge #AIforDevelopers