GitHub updates Copilot with 37.6% more accurate code context

Recently, I accidentally fell into a rabbit hole… and instead of climbing out, I opened the source code of an AI coding agent 👀 I’ve been curious about agentic coding for a while, so I gave Opencode a spin. And yes, my selection criteria were extremely rigorous… mainly “ooh nice UI” and “that name sounds cool” 😄 While using it, I noticed something odd (atleast to me): It only listed my sessions when I was inside the same folder where they were created. Switching folders? No sessions. Naturally, curiosity kicked in — so I dug into the source code 🔍 Here’s what I found: 👉 Project Identification Opencode generates a unique project ID to track sessions. It does this in two ways:  - If the folder is a Git repo:      It runs   $ git rev-list --max-parents=0 HEAD       to get the Git directory, then derives the project identity from it.       And here’s the fun part — it caches this inside .git/opencode.   Yes, it casually writes into your .git folder. Pretty clever.  - If Git is not initialized:       It falls back to a constant global value (so basically, all such folders look the same to it, no because there are some other conditions too). 👉 Session Tracking Each session gets its own unique ID. 👉 How it links everything It stores sessions in a SQLite database and connects them using the project ID as a foreign key. You can even find where this DB lives with: opencode db path 💡 Why sessions don’t show across folders? Because each folder = different project ID (especially if Git is initialized). No shared project ID → no shared session list. Honestly, I love these small design decisions. Simple idea, clean implementation, and very “developer-minded.” Diving into source code like this always feels like uncovering tiny engineering stories hidden beneath the UI 🚀 #OpenSource #AI #AgenticAI #CodingAgent #DeveloperLife #SoftwareEngineering #TechDeepDive #CodeReading #LearnInPublic #Git #SQLite #Programming #Developers #TechCuriosity #BuildInPublic #EngineeringInsights #Debugging #DevTools #BackendEngineering #CleanDesign

Introducing git-ai — Your complete AI-powered Git workflow solution! This visual breakdown highlights how git-ai lives inside your terminal to automate personalized commit messages, create PR descriptions, generate changelogs, and even detect ticket IDs, all while learning your unique style. Just run: npm install -g @malikasadjaved/git-ai git-ai setup git-ai commit Three commands. Zero friction. Never write a commit message manually again.  🌐 Website: https://gitai.vercel.app   💻 GitHub: https://lnkd.in/dfEJGaBX   🔗 Connect: https://lnkd.in/dv_dkDsd #OpenSource #DeveloperTools #AI #Git #CLI #NodeJS #TypeScript #Programming #SoftwareEngineering

I realized most people completely misunderstood what I’ve been building. They think it’s just a commit message summarizer. Honestly, I’d think the same too. But it doesn’t just jump into commits or diff summaries. It tries to understand the project first, then looks at what actually changed. And it’s not even reading commit titles. It reads the actual diffs and patches - up to 100k lines of code - across thousands of files - from up to 100 commits at a time So it’s not just scanning a project snapshot it’s understanding what changed across commits. The problem is commit messages are messy. Sometimes they’re great. Most of the time they’re vague, incomplete, or just “fix stuff” So summarizing them doesn’t really solve anything. What I’m trying to do instead Go straight to the source of truth and then the code changes themselves And turn that into something readable structured release notes real technical breakdowns clear changelogs without manual work Example 👇 https://lnkd.in/g-bJCqqc Also made it flexible depending on who you’re writing for Format: Release Notes / GitHub Releases / Keep a Changelog / Simple List Tone: Friendly / Technical / Marketing Still early, but this shift (from “summarizing commits” → “understanding code changes across commits”) feels like the right direction. also, try now for free at https://noteshipai.com/try. No sign up required. #Python #GitHub #LeetCode #DSA #BuildInPublic #Automation #Developers #TechCommunity #CodingJourney #noteshipAI #AITools #AI

Anthropic hackathon winners’ setup A fully systematized Claude Code stack—agents, workflows, rules, and automation—turning LLMs from tools into an integrated engineering OS.

Tired of writing commit messages? I built a tool that does it for you.   Introducing git-ai — an AI-powered CLI that lives inside your Git workflow.   What it does:   - Generates commit messages that match YOUR style (learns from your last 20 commits)   - Creates PR descriptions with one command   - AI code review with severity ratings   - Auto-generates changelogs in Keep a Changelog format   - Detects ticket IDs (JIRA, Linear, GitHub) from branch names   - Works with Claude, GPT, Gemini, or fully local with Ollama   Just run:   npm install -g @malikasadjaved/git-ai   git-ai setup   git-ai commit   Three commands. Zero friction. Never write a commit message again.   🌐 Website: https://lnkd.in/dzydYtAj   💻 GitHub: https://lnkd.in/dfEJGaBX   🔗 Connect: https://lnkd.in/dv_dkDsd   Built with frustration from writing commit messages manually. Open source & MIT licensed.   #OpenSource #DeveloperTools #AI #Git #CLI #NodeJS #TypeScript #Programming #SoftwareEngineering

I didn’t build my first CI/CD pipeline in one go. I broke it… multiple times. ❌ Docker build failed   ❌ YAML errors   ❌ GitHub Actions failing again and again  At one point, nothing was working. But I kept debugging. Step by step: → Fixed Dockerfile issues   → Understood GitHub Actions workflow   → Added testing using pytest   → Rebuilt the pipeline  And finally… ✅ CI/CD pipeline running successfully   ✅ Docker image built via GitHub Actions   ✅ Pulled and ran the container locally  This wasn’t just about tools. It was about learning how real engineering works: fail → debug → fix → repeat → succeed 💡 Built using: - Flask   - Pytest   - Docker   - GitHub Actions  This is my first step into DevOps — and definitely not the last. #CI_CD #DevOps #Docker #GitHubActions #Python #Flask #LearningInPublic #SoftwareEngineering

𝗦𝗽𝗲𝗻𝘁 𝘁𝗵𝗲 𝗹𝗮𝘀𝘁 𝘄𝗲𝗲𝗸 𝗱𝗲𝗯𝘂𝗴𝗴𝗶𝗻𝗴 𝗮 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗶𝘀𝘀𝘂𝗲 𝘁𝗵𝗮𝘁 𝗰𝗮𝗺𝗲 𝗱𝗼𝘄𝗻 𝘁𝗼 𝗼𝗻𝗲 𝘁𝗵𝗶𝗻𝗴 — 𝗮 𝗽𝗼𝗼𝗿𝗹𝘆 𝘄𝗿𝗶𝘁𝘁𝗲𝗻 𝗗𝗼𝗰𝗸𝗲𝗿𝗳𝗶𝗹𝗲. 𝗦𝗼 𝗵𝗲𝗿𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗗𝗼𝗰𝗸𝗲𝗿 𝗯𝗲𝘀𝘁 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀 𝗜 𝘄𝗶𝘀𝗵 𝘀𝗼𝗺𝗲𝗼𝗻𝗲 𝗵𝗮𝗱 𝘁𝗼𝗹𝗱 𝗺𝗲 𝗲𝗮𝗿𝗹𝗶𝗲𝗿: 𝟭. 𝗨𝘀𝗲 𝗮 𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗯𝗮𝘀𝗲 𝗶𝗺𝗮𝗴𝗲 𝘁𝗮𝗴, 𝗻𝗲𝘃𝗲𝗿 :𝗹𝗮𝘁𝗲𝘀𝘁  latest can silently change between builds. Pin your version — node:20.12-alpine, python:3.12-slim. Your future self will thank you. 𝟮. 𝗢𝗿𝗱𝗲𝗿 𝘆𝗼𝘂𝗿 𝗹𝗮𝘆𝗲𝗿𝘀 𝗳𝗿𝗼𝗺 𝗹𝗲𝗮𝘀𝘁 𝘁𝗼 𝗺𝗼𝘀𝘁 𝗳𝗿𝗲𝗾𝘂𝗲𝗻𝘁𝗹𝘆 𝗰𝗵𝗮𝗻𝗴𝗲𝗱  COPY package.json first → RUN npm install → then copy the rest of your code. This keeps your dependency layer cached and builds stay fast. 𝟯. 𝗥𝘂𝗻 𝗮𝘀 𝗮 𝗻𝗼𝗻-𝗿𝗼𝗼𝘁 𝘂𝘀𝗲𝗿  By default, containers run as root. That's a security risk. Add: RUN adduser --disabled-password appuser USER appuser 𝟰. 𝗨𝘀𝗲 .𝗱𝗼𝗰𝗸𝗲𝗿𝗶𝗴𝗻𝗼𝗿𝗲  Stop shipping node_modules, .git, test files, and .env into your images. A bloated image is a slow image — and a leaky one. 𝟱. 𝗠𝘂𝗹𝘁𝗶-𝘀𝘁𝗮𝗴𝗲 𝗯𝘂𝗶𝗹𝗱𝘀 𝗮𝗿𝗲 𝗮 𝗴𝗮𝗺𝗲 𝗰𝗵𝗮𝗻𝗴𝗲𝗿  Build your code in one stage, copy only the final artifact to a clean runtime image. I've seen image sizes go from 1.2GB → 80MB just from this one change. 𝟲. 𝗢𝗻𝗲 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝗽𝗲𝗿 𝗰𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿  Don't run your app + cron + nginx in a single container. Separate concerns. Use docker-compose or orchestration for that. 𝟳. 𝗛𝗘𝗔𝗟𝗧𝗛𝗖𝗛𝗘𝗖𝗞 𝗶𝘀 𝗻𝗼𝘁 𝗼𝗽𝘁𝗶𝗼𝗻𝗮𝗹 𝗶𝗻 𝗽𝗿𝗼𝗱  HEALTHCHECK --interval=30s --timeout=5s CMD curl -f http://localhost:8080/health || exit 1 If you're not doing this, your orchestrator doesn't know if your app is actually alive.  • 𝗦𝗺𝗮𝗹𝗹 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁𝘀 𝗶𝗻 𝗗𝗼𝗰𝗸𝗲𝗿𝗳𝗶𝗹𝗲𝘀 𝗰𝗮𝗻 𝘀𝗮𝘃𝗲 𝗵𝗼𝘂𝗿𝘀 𝗶𝗻 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻. #Docker #DevOps #SoftwareEngineering #Backend #CloudNative

VS Code showed I was on a feature branch. But Git pushed to main. Why? Ever faced this? You’re on the “right” branch Everything looks clean You push… …and it lands in main/staging 😐 Here’s what’s actually happening: 1. You’re not on the wrong branch   You’re in the wrong "folder" 2. Tools like Claude Code / Copilot CLI now use "Git worktrees" 3. That means:   • Same repo   • Multiple folders   • Each folder = different branch 4. VS Code shows branch state   Git uses your "current directory" 💥 Result: Right command. Wrong place. 🔒 Fix (this alone saves you): Before every push: `git status` Optional but powerful: • `pwd` • `git branch -vv` If it doesn’t match your expectation → don’t push. Most Git mistakes aren’t about Git. They’re about context. If you're using AI tools and haven't thought about worktrees yet… you will. Usually right after your first “push to main” moment. 👇 Full breakdown in comments #Git #SoftwareEngineering #WebDevelopment #AItools #DeveloperTips #Git #DevTools #SoftwareEngineering #Programming #DeveloperLife

We’ve trusted Git for everything — clean versioning, easy collaboration, and quick rollbacks. But when I started building real ML projects, I realized Git alone wasn’t enough. Git works great for software development, but in ML, data broke everything. Massive datasets, model weights, constantly changing labels, and scattered experiments made versioning a nightmare. Git LFS was expensive, S3 buckets felt disconnected, and reproducibility became painful. That’s when I discovered DagsHub — GitHub for Data Science. It neatly combines Git + DVC + MLflow in one platform. I finally got: - Reliable versioning for large datasets (no more LFS headaches) - Built-in experiment tracking - Free remote storage + model registry I tested it on a project containing audio, images, and tabular data. I ended up tracking 3GB+ of data while keeping my Git repository under 50KB. Clean, reproducible, and actually enjoyable. Want the full story — setup steps, DVC commands, MLflow integration, and key learnings? 👉 Read the complete post here: https://lnkd.in/gdM-ERPk #MLOps #AIOps #DevOps #MachineLearning #ProductionAI #AI