AI-Powered Git Commit Messages with git-ai CLI

🧠 What a project's git history reveals — without reading a single line of code I took an open source repo I had never seen before: RAG-Anything (HKUDS, 1k+ stars on GitHub). 333 commits. Zero code reading. Just git metadata. Here's what emerged in seconds 👇 (see screenshot picture) A senior developer joining this project cold would spend hours mapping this coupling. This took 12 seconds. No LLM calls. Pure graph inference. Here's the insight behind this: The architecture of a project is not in the code. It's in the co-modification patterns — which files evolve together, in which order, how often. Every git commit is a signal. Accumulated over months, those signals reveal something no static analysis tool can see: how the project actually thinks. Current AI coding agents forget everything between sessions. They rediscover your project every single time. What if your agent remembered — not just your code, but the architectural soul of your project? That's what we're building. 🚧 👉 Repo used for this demo: https://lnkd.in/dfkVb_Kt #AI #DevTools #SoftwareEngineering #BuildInPublic #AgentAI

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

Ever wondered how to turn your codebase into a self‑editing partner? 🤔 I’ve been testing the new GitHub‑Claude Code plugin that lets you call Codex directly from Claude. After a quick setup, you can run a review or delegate a task with a single command, and the feedback comes back as sharp as a senior engineer’s pair‑programming session. What stands out is the blend of speed and depth: the review runs in the background, flags subtle bugs, and even suggests alternative designs. In my recent project, it cut review time by 30% while surfacing edge‑case risks that manual checks missed. 👉 Quick wins you can try today: - Run a baseline review on a feature branch - Use --adversarial-review to stress‑test a critical module - Resume a paused job with the session ID - Track progress with /codex:status - Delegate routine refactors to Codex and reclaim your focus Code reviews are more than finding bugs—they’re about building a culture where every line of code is examined, debated, and improved together. How will you reshape your workflow when AI becomes a permanent teammate? #AI #CodeReview #Leadership #DigitalTransformation #EthicalTech Reference: [https://lnkd.in/gxq36AWs] 🔄 Share 👍 React 🌐 Visit www.aravind-r.com #AravindRaghunathan

𝗬𝗼𝘂𝗿 𝗱𝗶𝘀𝗸 𝗱𝗼𝗲𝘀𝗻’𝘁 𝗵𝗮𝘃𝗲 𝘆𝗼𝘂𝗿 𝗿𝗲𝗽𝗼. 𝗝𝘂𝘀𝘁 𝗼𝗻𝗲 𝗯𝗿𝗮𝗻𝗰𝗵. I was trying out an AI coding tool. It asked me to upload files from my local project folder. Simple enough. But then I paused— “Wait… which branch is actually in this folder right now?” I knew I had multiple branches. I knew Git was tracking everything. But at this exact moment… what files are physically sitting on my disk? That question sent me down a rabbit hole. Here’s what I realized: 📁 𝗬𝗼𝘂𝗿 𝘄𝗼𝗿𝗸𝗶𝗻𝗴 𝗳𝗼𝗹𝗱𝗲𝗿 𝗼𝗻𝗹𝘆 𝘀𝗵𝗼𝘄𝘀 𝗢𝗡𝗘 𝗯𝗿𝗮𝗻𝗰𝗵 𝗮𝘁 𝗮 𝘁𝗶𝗺𝗲 Whatever branch you last checked out—that’s what exists in your folder right now. Other branches? Not there. 📁 𝗦𝗼 𝘄𝗵𝗮𝘁 𝗮𝗺 𝗜 𝘂𝗽𝗹𝗼𝗮𝗱𝗶𝗻𝗴 𝘁𝗼 𝘁𝗵𝗲 𝗔𝗜 𝘁𝗼𝗼𝗹? A snapshot of your current branch. Nothing more, nothing less. 📁 𝗪𝗵𝗲𝗿𝗲 𝗶𝘀 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 𝗲𝗹𝘀𝗲? Inside .git/—stored as compressed objects, commits, and references. Your full history lives there, just not in your working directory. 💡 𝗧𝗵𝗲 𝗶𝗻𝘀𝗶𝗴𝗵𝘁 𝘁𝗵𝗮𝘁 𝗰𝗹𝗶𝗰𝗸𝗲𝗱 𝗳𝗼𝗿 𝗺𝗲: When I upload my project folder to an AI tool, I’m not uploading “my repository.” I’m uploading one version of it. Other branches, past commits, stashes—they’re all still safe locally, hidden inside .git. And that’s actually the beauty of Git: It lets you work on one version at a time, while quietly preserving every version behind the scenes. #Git #AITools #CodingJourney #DevProductivity #VersionControl

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

AI agents don't have Ctrl+Z. When they break your code, they don't undo. They re-read every file, reason about what went wrong, rewrite from scratch, burning tokens on code they already had right. Sometimes the "fix" breaks something else. And the cycle repeats. I built ckpt. It watches in the background and auto-snapshots every step your agent takes. Something breaks? ckpt restore 3. Instant. Zero tokens. The agent can even call it itself. Not a new version control, just a thin layer on top of git. Works with Cursor, Kiro, Claude Code, Codex, Aider, anything. npm install -g @mohshomis/ckpt If you use AI agents for coding daily, try it on your next session and tell me what's missing. github.com/mohshomis/ckpt

I spent a few weekends playing around with Claude Code and Codex, and ended up with something I've actually kept using. The problem was simple enough: my commit messages are terrible. Not catastrophically so, but not the kind of clean, conventional history that's useful six months later. I kept telling myself I'd tidy them before opening a PR. I never did. So I built aic - a Rust CLI that reads your staged diff and generates a conventional commit message. I'm not a Rust developer by trade (infrastructure and DevOps is more my world), but tools like Claude Code makes it surprisingly practical to build and maintain something in a language you'd normally avoid. There's an obvious meta quality to using an AI coding assistant to build an AI-powered git tool too. It's grown a bit since the first working version - it'll now review your staged diff before you commit, draft PR descriptions, rewrite a scrappy local history before you push, split a mixed staged change set into separate commits, and generate SVG visualisations of your repo structure and commit history. Seven AI providers supported, including local ones via Ollama, Claude Code, and Codex. The binary is called aic. Yes, that's an Alice In Chains reference 🎸 Read more at https://lnkd.in/ectdATGC #Rust #Git #AI #DevTools #OpenSource #WeekendProject

GitHub just shipped an embedding model update that actually matters. Their new Copilot embedding model for VS Code isn't just incrementally better — it's 37.6% more accurate at finding the right code context, runs twice as fast, and uses 8x less memory for indexing. For C# and Java devs, acceptance rates for suggestions have doubled. That's not a feature update. That's a productivity shift. What's interesting: they used contrastive learning techniques (InfoNCE loss + Matryoshka Representation Learning) to train this. The model now powers chat, agent, edit, and ask modes — so the improvements cascade across every interaction you have with Copilot. But here's what I'm thinking about: we're moving from "AI suggests code" to "AI understands your codebase architecture." Better embeddings mean better context retrieval. Better context means suggestions that feel less like autocomplete and more like pair programming with someone who's read your entire repo. For QA folks and builders working in complex codebases, this changes test automation workflows and API integration work significantly. Question: Are you seeing improved Copilot suggestions in your stack after this update, or is acceptance rate still a coin flip for you? #GitHubCopilot #AICoding #DeveloperProductivity #CodeEmbeddings #DevTools

Stop sending 1,000-line Pull Requests. Start "Stacking" them. 🚀 We’ve all been there: You open a PR with 40 files changed. Your teammates see +1,240 / -300 and suddenly everyone is "too busy" to review it. Large PRs are where code quality goes to die. They are hard to review, slow to merge, and prone to "LGTM" rubber-stamping because the diff is just too overwhelming. I’ve been experimenting with Stacked Pull Requests—a workflow that turns a "Mega-PR" into a streamlined "Story." The Concept is Simple: Instead of one giant block of code, you break your feature into a chain of small, dependent layers: 1️⃣ Layer 1: Database Schema (The Foundation) 2️⃣ Layer 2: API Logic (Built on Layer 1) 3️⃣ Layer 3: AI Integration/UI (The Finished Product) Why this is a game-changer for engineering teams: ✅ Micro-Reviews: It’s easier to review 50 lines than 500. Feedback is faster and higher quality. ✅ Continuous Momentum: You don't have to stop working while waiting for a review; you just gh stack add and keep building. ✅ Atomic Merges: If there’s a bug in the UI, it doesn’t block the backend infrastructure from being approved. GitHub is now making this native with the gh-stack CLI, which handles the "cascading rebase"—automatically updating your entire chain if you make a change at the bottom. If you’re looking to boost your team's velocity and developer experience (DX), this is a must-try. Check out the official documentation here: 🔗 https://lnkd.in/giKcEuCh How do you handle large features? Do you prefer "The Big Bang" merge or are you already stacking? Let’s discuss in the comments! 👇 #SoftwareEngineering #GitHub #DevOps #BackendDevelopment #CleanCode #DeveloperExperience #Python #AIDevelopment

JSON is great for machines. YAML is great for humans. And developers are stuck in the middle. APIs give you JSON. But real systems often expect YAML: • Kubernetes configs • Docker Compose • CI/CD pipelines So you end up doing this: Copy JSON → find converter → fix formatting → debug weird issues. Because converting isn’t just about syntax. It’s about not breaking meaning. YAML does things differently: • indentation defines structure • some values auto-convert (yes, on, null) • multiline strings behave differently One small mistake — and your config breaks. Silently. So we added a JSON → YAML converter to JSONGate. Paste JSON → get clean, structured YAML: • preserves hierarchy • handles tricky values safely • ready for real-world configs And like everything in JSONGate: No uploads. No backend. Nothing leaves your browser. 👉 https://lnkd.in/dZ3b6bSe Because data conversion isn’t just formatting. It’s trusting that nothing breaks. Curious — have you ever had a YAML config fail because of something “invisible”? #BuildInPublic #JSONGate #DevTools #WebDevelopment #DevOps #Kubernetes #Programming #SoftwareEngineering #DeveloperExperience #IndieHacker