Claude Models vs GitHub Copilot: Developer Experience

If you’re using Claude Code to build anything bigger than a quick script, you’ve probably hit the wall where the AI starts confidently building things you never asked for. I’m a TPM at LinkedIn, not a developer, and I spent months assuming that was just how it worked. Then somebody at an engineering tech talk showed us GSD, an open source context engineering system that gives your Claude Code sessions actual structure. Task specs, acceptance criteria, progress tracking. Since I started using it my bigger builds actually finish without going off the rails. One heads up though: GSD is a token monster, so budget accordingly. https://lnkd.in/gNJ5U__h

We've standardized this across our entire development team — and it changed how we build with AI. Most Claude Code setups we come across are running on one layer. A CLAUDE.md file, maybe some basic instructions. That works for solo projects. It doesn't scale across a team. There are actually 6 layers to the full architecture: Layer 1 — Memory: What Claude reads before every session. Team rules in git, personal overrides gitignored, modular instruction files always on. Layer 2 — Skills: Self-contained expertise folders Claude invokes automatically through semantic matching. You don't call them. They show up when relevant. Layer 3 — Hooks: Shell scripts wired to 17 event triggers. They are deterministic — they run every single time without exception. This is where you put anything you can't leave to chance. Layer 4 — Agents: Parallel subagents running in isolated context windows. Code review, security audits, QA — all happening without touching your main thread. Layer 5 — Plugins: Bundle your entire workflow — skills, agents, hooks — into one package your whole team can install in a single command. Layer 6 — MCP: The connection layer between Claude and your full stack. GitHub, Jira, databases, internal APIs. The distinction that changes every architecture decision you make: → CLAUDE.md + Hooks are deterministic. They always run. → Skills + Agents are probabilistic. Claude decides. We put together a full visual breakdown of all 6 layers in the carousel above. Swipe through — it's worth 2 minutes. If your team is building seriously with Claude Code and wants to compare notes on how we've structured this in production, drop a comment or reach out directly. @Anthropic @ClaudeAI #ClaudeCode #SoftwareDevelopment #AIDevelopment #AgenticAI #EngineeringLeadership #DeveloperTools #AIEngineering #TechLeadership #SoftwareEngineering #Anthropic

🔥🚀 AI CHEAT CODE #032 🔥🚀 💡 GitHub Copilot just went AGENTIC for code reviews — and most devs have NO IDEA how to use it yet! 🤯 GitHub's new agentic code review is NOW generally available — and it's a total game-changer for PRs! 🎯 ⚡ Here's how to unlock it RIGHT NOW: 🔍 Step 1: Open any Pull Request on GitHub 👥 Step 2: Click the "Reviewers" dropdown on your PR 🤖 Step 3: Select "Copilot" as a reviewer — that's it! ⏱️ Step 4: Wait ~30 seconds while Copilot reads your ENTIRE repo, traces cross-file dependencies, and builds architectural context 💬 Step 5: Get inline comments that understand the BIG PICTURE — not just the diff! 🆚 What's ACTUALLY different now? ❌ OLD Copilot review: Only looked at changed files ✅ NEW Agentic review: Reads directory structure, traces dependencies across files, understands full architecture before commenting! 💻 BONUS CLI Cheat Code: Run this from your terminal 👇 gh pr review --request-review copilot Or just type /review in any PR comment! 🪄 🎯 Pro Tips: 💎 Agentic reviews catch multi-file bugs the old review MISSED 📊 Already 60 MILLION+ reviews done — growing 10x since launch! 🏢 Works on: Copilot Pro, Pro+, Business & Enterprise ⚙️ Runs on GitHub Actions (one-time setup if you opted out of hosted runners) This is what AI-assisted development looks like in 2026 — not just autocomplete, but an intelligent agent that UNDERSTANDS your codebase! 🧠🔥 💬 Have you tried the new agentic Copilot code review yet? Drop a 🔥 if this changed your PR game! Save this post for your next code review! ⬇️ #AI #GitHub #GitHubCopilot #CodeReview #DevOps #Coding #Programming #SoftwareEngineering #TechNews #Automation #MachineLearning #ArtificialIntelligence #WebDevelopment #OpenSource #TechTrends #Developer #AgenticAI #ProductivityHacks #Innovation #CloudComputing

I've been using Claude Code daily for 4 months. Last week I discovered a feature that changed everything: Skills. Skills are markdown instruction files that teach Claude Code specific behaviors. Instead of repeating the same prompts, you write it once — and Claude follows it forever. Here's my current setup (5 skills I use on every project): 𝟭. /𝗿𝗲𝘃𝗶𝗲𝘄 → Reviews code like a senior engineer → Catches bugs, security issues, performance problems → Suggests improvements with explanations 𝟮. /𝗰𝗼𝗺𝗺𝗶𝘁 → Analyzes staged changes → Writes conventional commit messages → Groups related changes logically 𝟯. /𝘀𝗶𝗺𝗽𝗹𝗶𝗳𝘆 → Identifies code duplication → Refactors for readability → Removes unnecessary complexity 𝟰. /𝗱𝗲𝗽𝗹𝗼𝘆 (custom) → Runs tests, builds, and deploys → Handles environment-specific configs → One command for entire CI/CD 𝟱. /𝗰𝗼𝗻𝘁𝗲𝗻𝘁 (custom) → Generates platform-specific posts → Creates hooks, scripts, threads → Built for viral content creation The difference between Skills and MCP: → MCP = connects Claude to external tools (GitHub, Slack, databases) → Skills = teaches Claude HOW to do things (methodology) → Best setups use both Where to find skills: → claudemarketplace.com has 150+ community skills → https://lnkd.in/g_k8ggjX (curated list) → Or create your own in ~/.claude/skills/ Skills are stored as simple markdown files with YAML frontmatter. You can make Claude do literally anything — from code review to content creation to deployment automation. Most developers still use Claude Code without any customization. That's like buying a Ferrari and only driving in first gear. What skills would 10x YOUR workflow? #ClaudeCode #AI #DeveloperProductivity #Anthropic #CodingTools #Automation

I'm a solid month into my regular use of Claude Code. This afternoon, I found a lovely gem starting on line 346 buried in a project markdown file. First, some context: When I'm working on a complex project and I'm about to transition from one phase of work to another (say, from more conceptual design work to more technical build and deployment), I have Claude write itself a set of "Handoff Instructions." These instructions summarize the project's purpose and background, the tech stack, key design commitments and decisions, and next steps. These handoff instructions are drafted by Claude for itself as a markdown file that I can review, refine, and then upload into a new conversation so that a project moves forward seamlessly for a new session. Reviewing today's set of Handoff Instructions, I came across this observation that Claude wrote about me (lines 346-351 in a much longer file): ## 11. Key Context for Working with This User - The user is **non-technical** and needs step-by-step instructions for all terminal commands, code edits, and configuration changes. Never assume they'll know what to do — spell it out. - They are **comfortable with the Mac terminal** and have used it extensively now for the GitLab migration and local dev setup. - They work **collaboratively** — they appreciate being told what a change does and why before being asked to execute it. - They have **strong design/product instincts** — when they suggest a reframe (like the endorsed-vs-curated split), it's worth listening carefully rather than defaulting to the simpler implementation. A glimpse of how my AI coding collaborator perceives me and our work together.

Visualizing the Logic: Elevating the Code Review Experience As developers, we spend a significant portion of our time in the Code Review phase. While standard diffs are great for catching syntax errors or logic bugs, they often fail to provide the "Big Picture." When a Pull Request touches multiple files and modules, understanding the ripple effect can be a massive cognitive load. To solve this, I’ve been working on Code Review Graph—a tool built to bring architectural clarity to the review process. The Problem: The "Diff" Fatigue Traditional line-by-line reviews make it hard to see how a change in Module A might silently break a dependency in Module Z. This often leads to missed side effects and architectural debt. The Solution: Graph-Based Insights Code Review Graph visualizes your code changes as a dynamic map. It allows reviewers to: Trace Dependencies: Instantly see the relationship between modified files. Identify Hotspots: Pinpoint areas with high complexity or heavy coupling. Accelerate Onboarding: Help new contributors understand the impact of their changes visually. The goal with this repository is to move beyond the "text-only" review and make the process more intuitive and reliable for engineering teams. Link in the comment section. #SoftwareEngineering #GitHub #OpenSource #CodeQuality #FullStackDevelopment #DevTools #SystemDesign #Programming

The way developers write code has always evolved alongside the tools they use. From documentation to forums, and now to AI-assisted coding — each step has reduced friction and increased speed 🧭 With deeper IDE integration and improved context awareness, Copilot is no longer just suggesting snippets — it’s becoming embedded in the development workflow itself. This leads to: ✔️ Less repetitive coding ✔️ Faster feature delivery ✔️ More focus on architecture and logic We’re moving toward a model where developers spend less time writing code line-by-line — and more time shaping how systems should work. #Development #GitHub #Productivity #Engineering

Our repo used to follow an atomic structure, organizing components into atoms, molecules, organisms, templates, and pages. It sounded great in theory. But in practice, we were always asking: Should this component go in atoms or molecules? Where exactly does this belong? It quickly became overhead—and didn’t make working on a specific tool or page any easier. Finding components was still a hassle. Then we realized we needed a simpler, documented folder structure: Tool └── Pages   └── Components After standardizing: * Components are easy to locate * Each tool’s code is organized and separated * Modifications are targeted, avoiding overlap with other components, pages, or tools * Pull request reviews are clearer, showing exactly which files changed * Refactoring is controlled and confident A small change, with a big impact. But we doesn't stop restructuring the folder we also document it for future developers who will maintain it https://lnkd.in/gxrMemcT

I opened VS Code today, typed nothing… and somehow still managed to “spend tokens.” For a second, I thought Copilot had developed trust issues. Turns out—it’s just very prepared. 😄 I went down a rabbit hole to understand what GitHub Copilot is actually doing behind the scenes, and this completely changed how I think about AI-assisted coding: Fresh sessions aren’t really “empty” Even before you type a single character, Copilot is already loading context like: System prompt + tool definitions Your workspace file structure Instruction files (.instructions.md) User memory, skills, and agent registries So yeah… your “blank editor” isn’t blank at all. Here’s the interesting part 👇 You actually have control over how much gets loaded. Tweak the applyTo frontmatter to limit which instruction files auto-load Or convert them into .prompt.md files so they only activate when you call them Result? Less unnecessary context → fewer tokens used → faster, more focused suggestions. It’s a tiny config change, but in a large enterprise codebase, this can make a serious difference in performance and cost. Sometimes productivity isn’t about adding more tools— it’s about understanding what your tools are already doing. #GitHubCopilot #AI #DeveloperProductivity #VSCode #CodingTips #SoftwareEngineering

Your engineering backlog shouldn't be your bottleneck. What if you could turn every GitHub Issue into a verified Pull Request—while you’re offline? Meet Ghost Developer —my latest project that brings autonomous, zero-touch engineering to any repository. This isn't just another "AI autocomplete." This is a specialized multi-agent swarm built on the official Claude Code CLI that actually lives in your codebase. Here is what the Ghost does differently: Autonomous Execution: It doesn't just suggest code; it navigates your repo, runs bash commands, and performs its own tests. Event-Driven: Integrated with GitHub Webhooks. When an issue is raised, the swarm wakes up and starts building. Self-Correcting: If a test fails, the Ghost doesn't give up. It reads the error, refactors the logic, and tries again until the build passes. PR-Ready: Once the task is finished and verified, it pushes a finalized Pull Request directly to your team. I built this to solve the "Developer To-Do List" crisis. By delegating the repetitive, logic-heavy lifting to an autonomous agent, I can focus on high-level architecture while the Ghost handles the execution. It’s fast, it’s secure, and it’s open-source. The era of "AI-Assisted" is over. We’ve entered the era of the Autonomous Engineer. Check it out here: https://lnkd.in/gc2vVanN #AI #SoftwareEngineering #AgenticAI #GitHub #Claude3 #Automation #OpenSource