Priyadarshni Dubba’s Post

Exciting developments in code search capabilities with GitHub's new embedding model for Copilot! This update promises faster, more memory-efficient, and more accurate search results in VS Code. What stood out to me was how this could significantly enhance developer productivity by streamlining the coding process. How do you think improvements in tools like these will shape the future of software development?

⚔️ GitHub Copilot vs Tabnine — who’s better? AI is no longer just a trend — it’s a full-fledged assistant for developers. But when it comes to code?👇 🔹 GitHub Copilot 🔹 💡 Built in collaboration with OpenAI ⚙️ Deeply integrated into VS Code, JetBrains, and Neovim 📈 Suggests entire functions or code blocks based on context 🌐 Supports most languages (JS, Python, C#, Go, etc.) 🔒 Downside — requires a stable internet connection and a paid subscription 🔹 Tabnine 🔹 🤖 Powered by its own ML models 🧠 Offers code completions based on your personal codebase 💼 Can be trained on private repositories 🔐 Great for those who value privacy and need offline access ⚙️ A bit less “creative,” but more consistent Summary: 👉 Copilot — like a startup guy who always has bold ideas. 👉 Tabnine — a seasoned developer who never fails in production. #IT #programming #ITUkraine #development #webdevelopment

🚀 GitHub Copilot just got a major upgrade! The new embedding model makes code search in VS Code faster, smarter, and more memory efficient. With a 37.6% boost in retrieval quality, 2x higher throughput, and an 8x smaller index, developers can now find the right code faster and more accurately. Whether you're debugging, searching across monorepos, or just trying to locate that elusive helper method, Copilot’s improved context retrieval is a game changer. 🔍 Powered by contrastive learning and hard negatives, this model is built to distinguish “almost right” from “actually right.” 💡 Especially impressive gains for C# and Java developers with over 110% lift in code acceptance. Kudos to the GitHub and Microsoft teams for pushing the boundaries of AI-assisted development! 👏 👉 Dive into the details: https://lnkd.in/eZcZ6Tvc #AIforDevelopers #GitHubCopilot #VSCode #MachineLearning #SoftwareEngineering #DeveloperTools #GenerativeAI

Tired of git commit -m "fixes"? I created py-cogit to solve that. 🚀 It's an open-source CLI tool that uses Google's AI to act as your personal code review assistant. Right after git add and before you commit, py-cogit gives you: ✅ AI-powered code analysis and suggestions. ✅ Warnings about potential risks. ✅ A perfectly formatted, ready-to-use commit message. This project was my journey into automating a critical part of my workflow, powered by #Python and the Google #Gemini API, and distributed via #PyPI. Try it yourself! 🔧 Install: pip install py-cogit 📂 GitHub Repo + Docs: https://lnkd.in/dkbrGhWg As this is the first release, I'm really curious: How do you manage commit quality in your own projects, and do you think AI is the future for this kind of task? #OpenSource #DeveloperTools #AI #Git #Productivity

🚀 How I’m Getting the Most Out of GitHub Copilot as a Developer Recently, I started using GitHub Copilot more actively — and honestly, it feels like coding with a smart assistant sitting next to me. Here’s how I’m using it to boost my productivity 👇 💡 1. Write clear intent in comments Before writing code, I just describe what I want. Example: # Create API to fetch all active users with pagination Copilot instantly generates the full Django logic — serializer, view, and query! ⚙️ 2. Use Copilot Chat inside VS Code I can now ask things like: > “Explain this function” or “Write test cases for this API.” It’s like ChatGPT but directly inside my editor. 🧠 3. Let Copilot handle repetitive work Whether it’s Django serializers, CRUD views, or GraphQL resolvers — I just write one line and it completes the rest. 🚀 4. Combine it with GitHub Actions Copilot even helps create CI/CD YAMLs, Dockerfiles, and documentation. ✅ 5. Always review and refine Copilot gives a great starting point — but your developer instincts make it production-ready. --- Using Copilot smartly isn’t about letting AI code for you — it’s about using AI to code faster, cleaner, and with more focus. ⚡ Are you using GitHub Copilot yet? Share your favorite tip or experience below 👇 #GitHubCopilot #AItools #Developers #Python #Django #Productivity #Coding

I'm a fan of Github Copilot to assist in writing tools but using AI for coding tasks shouldn't absolve you of writing tools and frameworks the right way if you're gonna open-source them to the public. Modularity and reusable code aren't less important just because AI didn't tell you about it. Users and dev still need to be able to read through code. 🫠🫠🫠

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

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?

Today I experimented with Grok Code Fast 1 (Preview) inside GitHub Copilot, and I was pleasantly surprised. ✨ First impressions: - It is fast. - It handled tool calling well. - Executed git commands + GitHub CLI properly, creating a PR without issues. - For a free model, it did the job without any glaring problems. That said, it’s not at the same level as Claude Sonnet or OpenAI’s GPT 5 Codex in terms of reasoning depth, code quality, and overall refinement. Those models are still far ahead for more complex or production-level coding tasks. 👉 But for developers who are looking for a free coding assistant option, at least for some time, Grok Code Fast 1 Preview is worth trying out.