AI as GPS for Developers: Augmenting Human Expertise

💡 𝗦𝘁𝗼𝗽 𝗿𝗲𝗮𝗱𝗶𝗻𝗴 𝗰𝗼𝗱𝗲. 𝗦𝘁𝗮𝗿𝘁 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗶𝘁 — 𝗶𝗻𝘀𝘁𝗮𝗻𝘁𝗹𝘆. One of the most underrated features in GitHub Copilot is the /explain command. As developers, we don’t always get shiny new projects. More often than not, we step into existing or legacy systems. And that’s where the real challenge begins: 👉 “𝘞𝘩𝘢𝘵 𝘪𝘴 𝘵𝘩𝘪𝘴 𝘤𝘰𝘥𝘦 𝘥𝘰𝘪𝘯𝘨?” 👉 “𝘞𝘩𝘺 𝘸𝘢𝘴 𝘵𝘩𝘪𝘴 𝘸𝘳𝘪𝘵𝘵𝘦𝘯 𝘭𝘪𝘬𝘦 𝘵𝘩𝘪𝘴?” 🔍 𝗘𝗻𝘁𝗲𝗿 /𝗲𝘅𝗽𝗹𝗮𝗶𝗻 Just highlight the code and type: 👉 /𝘦𝘹𝘱𝘭𝘢𝘪𝘯 And Copilot will: ✔ Break down logic in simple terms ✔ Explain complex conditions and flows ✔ Decode unfamiliar code instantly ✔ Help you ramp up faster on existing projects 🚀 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝗲𝘃𝗲𝗿: • Not every assignment is greenfield • Legacy code is everywhere • Faster understanding = faster delivery • Better understanding = fewer production bugs ⚡ 𝗥𝗲𝗮𝗹 𝗶𝗺𝗽𝗮𝗰𝘁: Instead of spending hours reverse-engineering code, you get a clear explanation in seconds. Have you used /explain on legacy code yet? What was your experience? #GitHubCopilot #AI #DeveloperProductivity #LegacyCode #SoftwareDevelopment

When I said we lived in an AI 𝗯𝘂𝗯𝗯𝗹𝗲, nobody believed me. The 𝗱𝗲𝗺𝗼𝗰𝗿𝗮𝘁𝗶𝗰 manifesto was saying: “everybody can code, everybody should code.” Yesterday, GitHub paused new sign-ups for GitHub Copilot Pro, Pro+, and Student plans.  Also planned to increase the fee on AI consumption. We’re using resources out of a marketing program and an 𝗶𝗱𝗲𝗼𝗹𝗼𝗴𝗶𝗰𝗮𝗹 one. It’s a subsidized marketing with 𝗰𝗵𝗲𝗮𝗽 tokens to 𝗵𝗼𝗼𝗸 users moving to a sustainable infrastructure or real-world 𝗽𝗿𝗶𝗰𝗶𝗻𝗴. The only way to survive is using local models as a "bridge" or a "pre-processor" for giants like Gemini and Claude, and the only 𝗹𝗼𝗴𝗶𝗰𝗮𝗹 move to avoid going 𝗯𝗿𝗼𝗸𝗲 while staying productive. For instance, models like Context7, an open-source Model Context Protocol (MCP) server developed by Upstash, provide AI coding 𝗮𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁𝘀 with real-time, up-to-date documentation for programming libraries and frameworks. It addresses a critical problem: AI models often have outdated knowledge about software libraries or hallucinate deprecated APIs, leading to incorrect code suggestions. See how to wire into 𝗚𝗶𝘁𝗛𝘂𝗯 𝗖𝗼𝗽𝗶𝗹𝗼𝘁 𝗖𝗟I, with the container booting autonomously every time the terminal opens. https://lnkd.in/dEGvAauq #AICoding #MCP #GitHubCopilot #Upstash #Context7 #LocalAI #DevTools #OpenSource #SoftwareArchitecture #CodingBubble #LLM #TechStrategy

Claw Code just shattered all GitHub records, hitting 50K stars in a mind-blowing 2 hours ⭐ It's not just an archive of leaked Claude Code. It's about harnessing tools and making real things done for AI agents. Now, it's getting even faster ⚡ Free and open-source! → Record-breaking 50K stars in 2 hours ⭐ → Better Harness Tools, not merely storing code → Rewriting in Rust for a faster, memory-safe harness runtime ⚡ → Built with oh-my-codex (OmX) AI orchestration Some great use cases: #1 - Need to build production-ready AI agents? → Claw Code provides architectural insights into robust harness engineering, forged with AI-driven workflows. #2 - Want cutting-edge performance for your agent tools? → The ongoing Rust port promises unparalleled speed and memory safety. #3 - Curious about AI-powered software development? → See how oh-my-codex ($team, $ralph modes) orchestrated this rapid rewrite from scratch. Check out the free, open-source repo: → https://lnkd.in/gga7KiWD ---- ♻️ If this was useful, repost it so others can benefit too. Follow me for more post like this

𝗦𝗮𝗺𝗲 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆. 𝗧𝘄𝗼 𝘃𝗲𝗿𝘆 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀. 𝗩𝗲𝗿𝘆 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗼𝘂𝘁𝗽𝘂𝘁𝘀. Here's what most people miss 👇 Both GitHub Copilot and Claude Code can plan, execute, and iterate across your full codebase autonomously. Both are genuinely agentic in 2026. So why do outputs feel so different? It's not just the model. It's 𝘸𝘩𝘢𝘵 𝘵𝘩𝘦 𝘱𝘳𝘰𝘥𝘶𝘤𝘵 𝘪𝘴 𝘣𝘶𝘪𝘭𝘵 𝘢𝘳𝘰𝘶𝘯𝘥. 𝗚𝗶𝘁𝗛𝘂𝗯 𝗖𝗼𝗽𝗶𝗹𝗼𝘁 𝗶𝘀 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺-𝗳𝗶𝗿𝘀𝘁. It's deeply integrated into the GitHub ecosystem — issues, PRs, CI/CD pipelines, GitHub Actions. It supports multiple models (GPT, Claude, Gemini) and lets you assign entire issues to a coding agent that works in the background and opens a PR for review. The power is in the workflow integration, not any single model. 𝗖𝗹𝗮𝘂𝗱𝗲 𝗖𝗼𝗱𝗲 𝗶𝘀 𝗮𝗴𝗲𝗻𝘁-𝗹𝗼𝗼𝗽-𝗳𝗶𝗿𝘀𝘁. The entire execution cycle — reading your codebase, planning, running tests, iterating on failures — is built specifically around how Claude reasons. No IDE required. Works in your terminal, browser, or desktop. You can even spawn parallel agents coordinating on the same task. The power is in the depth of the reasoning loop. Same underlying model capability. Very different product design philosophies. The real insight: 𝗔𝗜 𝗺𝗼𝗱𝗲𝗹 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆 = 𝗔𝗜 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆. Don't just ask which model a tool uses. Ask what 𝘵𝘩𝘦 𝘱𝘳𝘰𝘥𝘶𝘤𝘵 𝘪𝘴 𝘣𝘶𝘪𝘭𝘵 𝘵𝘰 𝘥𝘰 𝘸𝘦𝘭𝘭 — and whether that matches your workflow. 💬 Which one fits your workflow better — or are you running both? Drop your take below. #AI #ClaudeCode #GitHubCopilot #AIEngineering #DeveloperTools #AgenticAI

Tried GitHub's spec-kit for spec-driven development with an AI agent. Love the idea — let the agent drive development from living specs, not vibes. But in practice it's heavy: lots of commands, templates, mandatory artifacts. Overkill for small projects and lean teams. So I built a lightweight fork: spec-kit-lite. Same idea, stripped to the bone. 3 docs per feature: spec.md — WHAT & WHY tech.md — HOW IT'S WIRED plan.md — HOW TO BUILD IT NOW 4 commands: /specl-sync — sync docs with code, or bootstrap from an existing codebase /specl-take — turn a ticket, description or URL into a feature /specl-plan — draft an implementation plan /specl-go — execute it The killer feature for me: point it at an existing repo and it generates spec.md + tech.md from the actual code. No retroactive documentation theater — docs are born from code and live alongside it. This is an init version — I'm dogfooding it on my own projects. Would really appreciate feedback: what works, what's annoying, what's missing. Issues and stars welcome 🙌 👉 https://lnkd.in/dKkNNkMz #SpecDrivenDevelopment #ClaudeCode #AI

Day 67 Today, I developed a small mock test website using AI tools for practice. Initially, I prepared PDFs containing the questions. Then, using AI tools like Claude, I converted those PDFs into JSON format and stored the data in a "data.js" file. For development, I used GitHub Copilot by giving structured prompts, which helped me build both the functionality and the UI design efficiently. The final output was successful, and I achieved the expected result: 🔗 https://lnkd.in/gKEcQvuq What surprised me the most was that I was able to build the entire project within 3 hours using AI assistance. After development, I decided to deploy the project on Vercel. Since it was my first time deploying, I had no prior knowledge. I relied on AI guidance throughout the process. During deployment, I faced several bugs, and resolving them took more than 1 hour. Through this experience, I also learned and practiced important Git commands required to push code to GitHub. Key Learnings: - Development is important, but debugging is even more critical - Patience plays a major role when solving bugs - Even small issues require checking code carefully, sometimes line by line - AI tools can significantly speed up development, but understanding the process is essential - Deployment is not just a final step — it’s a learning phase on its own #Day67 #WebDevelopment #AI #LearningJourney #Vercel #GitHub #FrontendDevelopment

A couple of years ago, I built a personal portfolio site and hosted it on GitHub Pages. It was static with manual updates, the kind of setup you create once and then forget about. Then I began to wonder: what if an AI agent managed it for me? So, I rebuilt the backend around Claude Code — a scheduled agent that wakes up every morning at 2 AM EST, searches the web for the latest in AI, Robotics, and Innovation, curates the most relevant stories for a technical audience, writes a structured JSON file, and pushes a commit to GitHub. That single commit triggers a GitHub Actions pipeline — installing, building, and deploying to Firebase Hosting — making the site live with fresh content in under 3 minutes, all without human intervention. The old GitHub Pages setup served its purpose, but this new version showcases my approach to systems, where every component has its role: → Claude Code handles the intelligence layer (curation, writing, scheduling) → GitHub Actions is the reliability layer (CI/CD, build integrity) → Firebase is the delivery layer (fast, global, cost-effective) → GoatCounter manages analytics — privacy-first, no cookies, no Google dependency If you're exploring something similar or are curious about how any piece of this fits together, I welcome the opportunity to connect and exchange ideas. The agentic era is already running on a cron schedule. Interested in the technical details — the Claude Code setup, the GitHub Actions config, or how the news feed works? Feel free to reach out. You can see it live here 👉 https://lnkd.in/ePEim8xU #AIEngineering #SoftwareArchitecture #EngineeringLeadership #ClaudeAI #Automation

GitHub just reported that 51% of all code committed to its platform in early 2026 was generated or substantially assisted by AI. Think about that for a moment. A majority of commits to the world’s largest code host now have an AI somewhere in the loop. And we’re only 3 years out from GitHub Copilot’s general availability. The supporting data tells the same story: → McKinsey: AI coding tools cut routine coding time by 46% (4,500+ developers, 150 enterprises) → Stack Overflow: 84% of developers are using or planning to adopt AI coding tools → 20M+ GitHub Copilot users, with agent mode now standard But here’s what the headline misses: the developers seeing the biggest gains aren’t the ones who replaced their workflow with AI. They’re the ones who redesigned their workflow around AI. The 2026 developer stack isn’t one AI tool. It’s a combination: • Claude Code or Cursor for complex reasoning and multi-file edits • Copilot for line-level autocomplete • Local models (Ollama, Tabby) for sensitive or proprietary code The developers who treat AI as a single tool will plateau. The ones treating it as a new layer in their stack are the ones compounding. 51% of code is already there. The other 49% won’t wait long. #DeveloperProductivity #AITools #SoftwareEngineering #GitHub #FutureOfWork

🚀 Just open sourced backend-integrate — a GitHub Copilot plugin that gives AI full context when integrating downstream services. If you've ever asked Copilot (or any AI) to "integrate the Payments service" and gotten back generic boilerplate that doesn't match your actual proto definitions, client patterns, or repo conventions — you know the pain. The real problem isn't AI capability. It's context. Copilot doesn't know your downstream service's API surface, your team's client wrapper patterns, or which repository layer needs refreshing. So you end up copy-pasting files, explaining the same conventions over and over, and manually stitching together a plan. backend-integrate fixes this. It's a zero-dependency Copilot CLI plugin that:  - Fetches relevant files (OpenAPI specs, proto files, client stubs, READMEs) directly from the downstream GitHub repo via gh CLI  - Asks the right clarifying questions before generating any plan  - Executes the integration in parallel fleet mode across your codebase No MCP server. No Python. No setup beyond gh auth login. Just drop it in and go. 📦 copilot plugin install sagar-rai/backend-integrate Would love feedback from anyone integrating microservices day-to-day. What context does your AI assistant always get wrong? #GitHub #Copilot #OpenSource #BackendEngineering #DeveloperTools #AI

GitHub just changed the economics of AI-assisted development. Yesterday, GitHub announced Copilot is moving to usage-based, token-driven pricing starting June 1. For teams running agentic workflows, this isn’t a pricing tweak — it changes how AI costs compound per feature. We published a deep dive on: - Why dashboard AI metrics no longer satisfy finance or boards - How per-feature ROI and per-feature cost tracking actually works - What teams should do before June 1 to avoid surprise spend If you’re scaling Copilot or agentic workflows, this is required reading. 👉 Read the full post: https://loom.ly/GjBuThc #GitHub