Navigating Large Codebases with These 4 Tools

My workflow: From GitHub Projects to PR. It all starts with a GitHub Project issue. If the requirements don't align with the business logic or lack clarity, I don't start. I ask, find solutions, and align expectations first. Once the path is clear, I move to planning: Impact Analysis: How does this affect the current stack and future features? Do we need new models? Do we need changes in other modules? Implementation Roadmap: A technical step-by-step before touching the IDE. Then comes the execution. I’m not about delegating everything to AI—I like to get my hands dirty and stay on top of the code. I use AI to speed things up, but it always follows my architecture and my technical criteria. Coding is just the final step of a solution that’s already been engineered. #SoftwareEngineering #WebDev #GitHub #Programming #CleanCode #FullStack

GitHub Copilot for System-Level Development 🚀 GitHub Copilot can go beyond basic code suggestions and actually assist in system-level development and from test-driven development to large-scale refactoring and infrastructure automation. Key takeaways👇 🧠 AI-assisted TDD → writing failing tests first, then generating code to pass them 🧪 Generating complex test suites with Copilot for better coverage 🧩 Mocking dependencies using unittest.mock to isolate system-level tests 📊 Improving test coverage using pytest + coverage tools 🔍 Using AI to identify uncovered, unused, or risky code sections 🗂️ Working efficiently with large codebases using workspace context 🔗 Analyzing cross-file dependencies for better debugging & refactoring ♻️ Iterative refactoring → run tests → fix issues → maintain stability 📏 Enforcing coding styles using Copilot instruction files 📝 Defining structured TDD workflows and guidelines for consistent development ⚙️ Infrastructure as Code (IaC) → automating deployments using Ansible 🐳 Generating optimized Dockerfiles (multi-stage builds, distroless containers) ☁️ Terraform configurations for container deployment (e.g., Azure Container Registry) 🔐 Handling environment-specific configurations and deployment workflows 🚀 Using AI to speed up debugging, modernization, and system design #GitHubCopilot #AI #DevOps #TDD #InfrastructureAsCode #Terraform #Docker #Automation

**3 PRs merged into GitHub's spec-kit here's my open-source journey so far** Over the past week, I've had 3 pull requests merged into github/spec-kit an open-source specification framework. --- 🔹 PR #1: Documentation fix (Mar 31) Found that AGENTS.md was out of sync with the actual agent configuration. Several agents were missing from the docs. Synced everything up so developers aren't confused when onboarding. --- 🔹 PR #2: Community catalog extension (Apr 1) Added the fix-findings extension to spec-kit's community catalog, making it discoverable and installable for all users. --- 🔹 PR #3: argument-hint frontmatter for Claude Code (Apr 3) This was the big one. When users typed slash commands like /speckit-plan in Claude Code, there was no hint about what input the command expects. I built a post-processing pipeline that injects argument-hint into YAML frontmatter for all 9 Claude Code skill commands. Now users instantly see prompts like "Describe the feature you want to specify" right inline. This PR went through: ✅ Multiple rounds of code review with the maintainer ✅ A rebase when upstream merged a major architecture change ✅ 6 targeted tests The maintainer's feedback genuinely improved the final code. --- 📌 What this journey taught me: 💡 Start small (docs fix), build trust, then take on bigger features 💡 Code review isn't criticism it's collaboration 💡 Open source rewards consistency and quality over speed --- 🚀 Currently working on PR #4 adding Table of Contents to generated markdown documents. --- #OpenSource #GitHub #Python #AI #ClaudeCode #SoftwareEngineering #SpecKit #WomenInTech

Episode 4 of Deploy or Die is live. Topic: How to build a GitHub Actions workflow that does the work you hate. What the workflow does: → Triggers on any version tag push → Generates release notes automatically using Claude (EP001 pattern) → Creates the GitHub release in one step → Posts a Slack notification with the release name Total runtime: under 2 minutes. Zero manual steps. Two bonus patterns covered: → Scheduled workflows — dependency PRs waiting every Monday morning → Reusable workflows — define once, used by every repo in your org automatically The full YAML is on GitHub. Drop it in your repo and adapt it. The hard part is done. 🎥 Watch: https://lnkd.in/gHvx2uES 📩 Read: https://lnkd.in/gC373ECt #DevOps #GitHubActions #CICD #ReleaseEngineering #DeployOrDie

In my previous articles, I analyzed how to practice SDD (Spec-Driven Development) using GitHub Copilot and SpecKit. Recently, I revisited this topic and gained a deeper understanding of the broader narrative that SDD aims to convey. Today, I want to share this with you! https://lnkd.in/gxbBAyM8

came across gitreverse.com lately and my first reaction was "wait... doesn't the README already do this?" turns out that's the right question to ask. the tool takes any public GitHub repo and generates a single prompt you can paste into Cursor or Claude Code to rebuild the project from scratch. cool concept. but yeah, if the README is solid, you're not getting much extra. where it actually clicks is when: - the repo has a terrible or no README (which is like... most repos) - you want to rebuild something to learn it, not just read about it - you're trying to feed context into an AI tool and don't want to manually copy 40 files a README is written for users. this output is written for AI tools. different format, different purpose. still think it's a clever idea even if the use case is narrow. the trick is just swapping "github" with "gitreverse" in any repo URL and it does the rest. not a game changer but genuinely useful if you learn by building. #DevTools #AITools #GitHub #LearnByBuilding #VibeCoding #PromptEngineering #CursorAI #ClaudeCode #OpenSource #CodeSmarter #SoftwareEngineering #100DaysOfCode #Programming #MachineLearning #ArtificialIntelligence #TechTwitter #Developers #WebDevelopment #BuildInPublic #AIAssistant

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

🚀 55k GitHub stars. One file. Zero overhead. While frameworks like Superpowers and Spec Kit build out massive methodologies, multi-step workflows, and rigid TDD enforcement gates... andrej-karpathy-skills takes the path of "Surgical Minimalism." 🔪 Instead of adding more tools, it adds more discipline. It’s a single CLAUDE.md file that forces a Senior Engineer mindset onto the agent. 🧠 The "Karpathy Stack" in 4 lines: 1️⃣ Think First: Stop the agent from "vibe-coding." Force it to surface assumptions and ask questions before writing a single line. ❓ 2️⃣ Simplify: No abstractions. No speculative code. No bloat. Keep the logic flat and readable. 📉 3️⃣ Surgical: Touch only what’s needed. No more annoying "drive-by" refactors or accidental style changes. 🎯 4️⃣ Goal-Loop: Define success early and work until the specific criteria is met. 🔄 Frameworks are great for teams that need rigid rails. 🛤️ But if you want "senior-level" output without the heavy configuration tax? This is the ultimate shortcut. ⚡ Repo: https://lnkd.in/d_tMhV6D #AI #SoftwareEngineering #ClaudeCode #Programming #Minimalism #CleanCode

I recently found myself repeatedly writing similar GitHub Actions YAML while working with GitHub Actions. After a point, it felt more like rewriting patterns than actually defining behavior. So I tried a small experiment — what if I described the pipeline instead of writing it? I built a simple approach where the pipeline is defined in Markdown, and the workflow is generated from it. Instead of focusing on how things run, it shifts the focus to: what needs to be built what needs to be tested when things should run It’s a small change in implementation, but it changes how you think about pipelines — from execution details to intent-driven design. I wrote a deeper breakdown here: https://lnkd.in/ghzghHfm And shared the project here: https://lnkd.in/gP9pWNMg Open to suggestions, feedback, and ideas — would love to hear how others are thinking about this space.

𝐃𝐚𝐲 2: 𝐓𝐡𝐞 𝐒𝐞𝐜𝐫𝐞𝐭 𝐁𝐞𝐡𝐢𝐧𝐝 𝐃𝐨𝐜𝐤𝐞𝐫. 𝐈𝐭 𝐢𝐬 𝐀𝐥𝐥 𝐀𝐛𝐨𝐮𝐭 𝐈𝐦𝐚𝐠𝐞𝐬 𝒀𝒆𝒔𝒕𝒆𝒓𝒅𝒂𝒚, we ran our first container. But today, a bigger question comes up: 𝐖𝐡𝐞𝐫𝐞 𝐝𝐨 𝐜𝐨𝐧𝐭𝐚𝐢𝐧𝐞𝐫𝐬 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐜𝐨𝐦𝐞 𝐟𝐫𝐨𝐦? 𝐓𝐡𝐞 𝐚𝐧𝐬𝐰𝐞𝐫: Docker Images. And honestly, this is where Docker really starts to make sense. In Day 2 of #20DaysOfDocker, we break down the concept that powers everything in Docker. No fluff. Just clarity. 𝐖𝐡𝐚𝐭 𝐲𝐨𝐮 𝐰𝐢𝐥𝐥 𝐥𝐞𝐚𝐫𝐧: Why Docker images are read-only blueprints How images are built using layers (this is a game-changer) How versioning works (and why tags matter more than you think) Where images live (Docker Hub & registries) 𝐓𝐡𝐞 “𝐚𝐡𝐚” 𝐦𝐨𝐦𝐞𝐧𝐭: Every image is made of layers. Each layer = a small change. Each change = cached, reusable, efficient. That’s why Docker is fast. That’s why it scales. 1.) 𝐇𝐚𝐧𝐝𝐬-𝐨𝐧 (𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐭𝐡𝐞𝐨𝐫𝐲 𝐢𝐬𝐧’𝐭 𝐞𝐧𝐨𝐮𝐠𝐡): Pull real images (ubuntu, nginx, python) Explore sizes and layers Remove images and clean your system Set up your Docker Hub account 2.) 𝐐𝐮𝐢𝐜𝐤 𝐢𝐧𝐬𝐢𝐠𝐡𝐭𝐬 𝐲𝐨𝐮 𝐝𝐨𝐧’𝐭 𝐰𝐚𝐧𝐭 𝐭𝐨 𝐦𝐢𝐬𝐬: Images are immutable (they never change) Containers add a writable layer on top Every image has a unique SHA256 ID Everything is optimized for speed and reuse 3.)  𝐁𝐲 𝐭𝐡𝐞 𝐞𝐧𝐝 𝐨𝐟 𝐃𝐚𝐲 2, 𝐲𝐨𝐮’𝐥𝐥 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝: What Docker images really are How layers work behind the scenes How to pull, inspect, and manage images How registries and repositories fit together How to choose the right images (like a pro) If 𝐃𝐚𝐲 1 𝐰𝐚𝐬 “𝐫𝐮𝐧 𝐚 𝐜𝐨𝐧𝐭𝐚𝐢𝐧𝐞𝐫”… 𝐃𝐚𝐲 2 𝐢𝐬 “𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐰𝐡𝐚𝐭’𝐬 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐡𝐚𝐩𝐩𝐞𝐧𝐢𝐧𝐠.” And that's when beginners become real Docker users. 𝐒𝐭𝐚𝐫𝐭 𝐃𝐚𝐲 2 𝐡𝐞𝐫𝐞: https://lnkd.in/dtVn3ieP 𝐋𝐞𝐭’𝐬 𝐤𝐞𝐞𝐩 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠. 𝐎𝐧𝐞 𝐥𝐚𝐲𝐞𝐫 𝐚𝐭 𝐚 𝐭𝐢𝐦𝐞. 🐳 𝑫𝒐 𝒏𝒐𝒕 𝒇𝒐𝒓𝒈𝒆𝒕 𝒕𝒐 𝒔𝒕𝒂𝒓𝒕 𝒕𝒉𝒆 𝒓𝒆𝒑𝒐. #Docker #DevOps #LearningInPublic #OpenSource #BackendDevelopment #CloudComputing #TechCommunity