GitHub Copilot Code Review Adoption Metrics Now in API

I kept running into the same 𝙥𝙧𝙤𝙗𝙡𝙚𝙢 𝙬𝙞𝙩𝙝 𝙂𝙞𝙩𝙃𝙪𝙗 𝘾𝙤𝙥𝙞𝙡𝙤𝙩: I could see that I was using it a lot. I could not clearly see which repo/workspace was driving that usage. So I built a local-first 𝘾𝙤𝙥𝙞𝙡𝙤𝙩 𝙪𝙨𝙖𝙜𝙚 𝙩𝙤𝙤𝙡𝙠𝙞𝙩. It has two parts: • a "𝘝𝘚 𝘊𝘰𝘥𝘦 𝘦𝘹𝘵𝘦𝘯𝘴𝘪𝘰𝘯" that shows token usage directly inside the editor • a "𝘴𝘵𝘢𝘯𝘥𝘢𝘭𝘰𝘯𝘦 𝘊𝘓𝘐 + 𝘥𝘢𝘴𝘩𝘣𝘰𝘢𝘳𝘥" for repo/workspace-level analytics, trends, model usage, and premium request estimates The goal was not perfect billing reconciliation. It was 𝙥𝙧𝙖𝙘𝙩𝙞𝙘𝙖𝙡 𝙫𝙞𝙨𝙞𝙗𝙞𝙡𝙞𝙩𝙮: better usage awareness, easier debugging, and clearer per-project attribution when working across multiple systems in parallel. Open source: https://lnkd.in/dzRdyvxM I’d love feedback from heavy Copilot users: what’s the one usage metric you wish GitHub exposed more clearly today? #GitHubCopilot #VSCode #OpenSource

🎬 GitHub Copilot CLI Multi-AI Integration: Second Opinion Feature Explained GitHub Copilot CLI introduces multi-AI model integration, allowing developers to get diverse code suggestions from different AI families for more robust development workflows. ▶️ Watch the full breakdown: https://is.gd/EyIr5K

Been using GitHub Copilot (agent mode) for automation - feature files, step defs, page objects… all of it. And wow… this thing has confidence. 😂 Give it a vague BRD? It will happily invent logic like it’s part of the requirements. Generic prompt? You’ll get generic nonsense back. Lesson learned real quick: You don’t “use” Copilot - you manage it. That said… Debugging is ridiculously fast now. And yes, it sometimes generates code so complex I just stare at it… but hey, it works 🤷♀️ Big takeaway: Copilot is basically that overconfident teammate who moves fast, breaks things, but somehow still gets the job done - if you guide it well. Anyone else seeing this? Or is it just me fighting my AI coworker daily? #AIinQA #GitHubCopilot #SoftwareTesting #AutomationTesting #AIinTech #ShiftLeft #FutureOfWork

5 months ago I sat down with Scott Durow🌈 and Rami Mounla after Power Platform Community Conference to record where coding agents were heading. Rewatched it this week. Holds up better than most of what we were all saying back then. Governance muscle from the low-code years carries over. Who gets access. Which tools they can call. AuthZ/AuthN. How it gets reviewed when it goes to prod. One citizen dev with a coding agent just creates a lot more code in one run. The AI harnesses matured. Most of the practical progress went into instrumentation around the context window. Auto-compaction, handling context rot, steering through progressive tool descriptions. Copilot CLI made big jumps on that front. Primitives underneath are becoming standards - agent definitions, skills, hooks, MCP tools. What changed is how well the harness manages the window around all of that. My own opinion shifted too. In autumn the results were mostly there but I wasn't confident about it yet and I was saying so. Now it's not a question for me whether we go this way. Only who, how fast and how well. Coding agents run end-to-end through #UDPP26 next week. Scott opens with GitHub Copilot CLI tips for people who haven't started yet. We'll walk through the new CLI tools and skills Microsoft just released and where they fit with Power Platform. Then the full build lifecycle with agents. 🦸Diana Birkelbach on frontend. Jonas Rapp on backend. Matěj Samler on whole Power Platform solutions end-to-end. Raphael POTHIN on securing what those agents produce. Julie Koťátková on agents running UI tests against the result. Rami Mounla closes with the authoring and review workflow that keeps control of what actually ships. Governance track runs in parallel. Jan Hajek and Sabin Nair on Entra Agent ID and MCP in the tenant. Jukka Niiranen on inventory and cost. Marcel Ferreira and Casey Burke on ALM. 📅 April 27-28, Livestream + Q&A + recordings on demand + on-site if you're close #PowerApps #PowerPlatform #GitHubCopilot #GitHubCopilotCLI #CopilotStudio #Governance #ProDev #Dataverse

Claude Code vs Github Copilot I asked Perplexity this question: "Forget about models for this question. Just compare the technical features and prompt orchestration and community and technical evaluations that compare Claude Code and Github Copilot running in VS Code." I plugged the answer into ChatGPT 5.4, asking it to expand on the answer and sources into a comprehensive white paper. It's an interesting analysis and explains much of what my gut tells me after an intense weekend of using Claude Code thanks to a GitHub Copilot 48 hour rate limit ban. Here's the white paper conclusion: "For engineering leaders, the key mistake is to treat the choice as a model bake-off. The more useful evaluation is operational: Which tool makes your preferred workflow legible, steerable, and recoverable? Claude Code wins when explicit orchestration is the priority. Copilot wins when ambient integration is the priority. Mature teams will often benefit from both, because real software development alternates between surgical typing flow and repo-scale excavation."

GitHub Copilot and Claude Code may both use slash commands, but they are not the same thing. Here is the simplest way to think about it: Built-in GitHub Copilot slash commands Controlled by GitHub and Microsoft Available based on your VS Code version, Copilot features, and extensions Designed for built-in actions inside Copilot Chat Examples: /explain, /fix, /tests, /doc, /new, /help Custom slash commands in Claude Code User-defined command patterns Used to shape how Claude responds Helpful for formatting, tone, structure, reasoning, and analysis Examples: /TLDR, /ELI5, /CHECKLIST, /SWOT, /COMPARE, /STEP-BY-STEP Claude Code skills Reusable automations for tasks you do often Great for turning repeated workflows into commands Examples: /review, /security, /optimize, /a11y, /test-plan My takeaway: GitHub Copilot slash commands = built-in product features Claude Code custom commands = flexible response controls Claude Code skills = repeatable workflow automation This distinction matters because many people see “slash commands” and assume they all work the same way. They do not. #GitHubCopilot #ClaudeCode #AI #DeveloperTools #VSCode #SoftwareDevelopment #Productivity #GenAI #Coding

Source: GitHub Official Documentation — docs.github.com/en/copilot GitHub Copilot shines when you give it the right context. 🤖 A .github/copilot-instructions.md file committed to your repo is all it takes — shared with your whole team, updated as your stack evolves. The framework is simple: WHAT — your stack & project structure WHY — architecture principles and conventions HOW — build, test, and lint commands Copilot follows what you tell it. Think of it as onboarding docs for your AI pair programmer — every contributor gets the same focused suggestions from day one. #GitHubCopilot #DeveloperProductivity #AITool

𝗩𝗦 𝗖𝗼𝗱𝗲 𝗷𝘂𝘀𝘁 𝗴𝗼𝘁 𝗮 𝗾𝘂𝗶𝗲𝘁 𝗯𝘂𝘁 𝘀𝗶𝗴𝗻𝗶𝗳𝗶𝗰𝗮𝗻𝘁 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝘂𝗽𝗴𝗿𝗮𝗱𝗲. VS Code 1.117 landed April 22 and the feature most enterprise orgs will care about is BYOK - Bring Your Own Key. If you are on Copilot Business or Enterprise, you can now connect your own model API keys directly into VS Code chat, with your admin setting the guardrails. ● 𝗕𝗬𝗢𝗞 - connect Anthropic, OpenAI, or other provider keys directly in VS Code; admins control availability via policy on GitHub.com; developers stay in the IDE they already use ● 𝗜𝗻𝗰𝗿𝗲𝗺𝗲𝗻𝘁𝗮𝗹 𝗿𝗲𝗻𝗱𝗲𝗿𝗶𝗻𝗴 - chat responses stream block-by-block now, which noticeably reduces perceived wait time on longer outputs ● 𝗕𝘂𝗶𝗹𝘁-𝗶𝗻 𝗖𝗼𝗽𝗶𝗹𝗼𝘁 𝗖𝗵𝗮𝘁 - from 1.116 (April 15): Copilot Chat is now a built-in VS Code extension; new users no longer need to install anything to get started BYOK removes the hard dependency on GitHub's model choices. You can route to whichever model performs best on your codebase without switching IDEs or toolchains. What model would you connect first - and has model lock-in been a real barrier for your team's VS Code Copilot adoption? #VSCode #GitHubCopilot #DeveloperTools

VaultSpeed was built around a simple idea: everything is an API. That openness is what makes VaultSpeed Labs possible. Labs is our GitHub home for the tools that come out of that philosophy: developer tooling, templates, Pro Serv accelerators built from real customer engagements. Things our team built because the platform was designed to be extended. If you're working with VaultSpeed, or evaluating it, Labs is where you see what the SDK actually unlocks in practice. https://lnkd.in/e5nZsAtp

Copilot usage metrics just got more honest (CLI included) 🚨 Copilot dashboards been undercounting usage? GitHub just integrated Copilot CLI activity into the main Copilot usage metrics—so top-level totals and feature breakdowns finally reflect IDE + CLI combined. ✅ What changes: • Top-level totals now include CLI (activity counts + LOC added/deleted) • CLI shows up in breakdowns as feature=copilot_cli • totals_by_ide stays IDE-only • Existing totals_by_cli section still remains 🎯 Why click: If reporting, thresholds, or adoption dashboards rely on “IDE-only” assumptions, numbers will jump—this update explains exactly what to adjust (and where CLI now appears). 📌 Admins: this makes it easier to compare CLI vs other Copilot capabilities—without manual stitching. https://lnkd.in/ePRUnmj5 #GitHub #Copilot #DevOps #Analytics #CLITools