GitHub Copilot Tightens Developer Usage Limits

How GitHub Copilot Runs Safely in Docker Sandbox with MicroVMs Click here for source https://lnkd.in/get9BvdN Do you want to know more click here https://lnkd.in/exH3zxjM GitHub Copilot + MicroVMs via Docker Sandbox explained! In this video, I show how a local GitHub Copilot agent can run inside an isolated Docker Sandbox powered by MicroVMs for safer AI coding and agentic refactoring. You’ll learn how Docker Sandbox gives GitHub Copilot access to a private Docker daemon inside a MicroVM, so you can build images, modernizing legacy applications. I also cover how Docker Sandbox helps preserve the same workspace paths across the host and sandbox, why that matters for real projects, and how this setup can support GitHub Copilot CLI workflows with better isolation, security, and developer productivity. If you want to understand GitHub Copilot, MicroVMs, Docker Sandbox, secure AI coding agents, and agentic refactoring in a practical way, this video is for you. Topics covered: - github copilot - local github copilot agent - microvms - docker sandbox - github copilot cli - agentic refactoring - secure ai coding - docker build and docker compose in sandbox - legacy app modernization - docker desktop sandbox workflow github copilot github copilot cli github copilot tutorial github copilot explained github copilot agent local github copilot agent github copilot microvms github copilot docker sandbox microvms microvm docker sandbox docker sandboxes docker sandbox tutorial docker sandbox explained docker desktop docker build docker compose ai coding agent ai coding assistant agentic refactoring secure ai coding secure coding agent local ai agent sandboxed ai agent private docker daemon isolated docker daemon docker socket risk no docker socket secure docker workflow legacy code modernization java modernization dotnet modernization containerized testing secure developer workflow microvm isolation github copilot local setup docker sandbox copilot github copilot docker desktop modernizing legacy apps ai refactoring #githubcopilot #microvms #dockersandbox #githubcopilotcli #localgithubcopilotagent #agenticrefactoring #aicoding #secureaicoding #dockertutorial #dockerdesktop #microvm #sandbox #dockerbuild #dockercompose #legacycodemodernization

GitHub is sitting in a strange spot right now: critical infra for almost every dev team, but struggling with reliability just as AI agents are flooding it with new load. Availability dropping to "one nine" and a steady stream of outages point to infra that was built for humans, not thousands of bots spinning up repos and hammering APIs in the background. At the same time, a tiny startup like Pierre Computer claims to handle repo creation at a scale that looks tailor-made for agents, not people. If GitHub wants to stay the top git platform for AI-native development, it has to treat agent traffic as first-class. That means an AI-native git layer, better scaling of stateful systems like databases and Redis, and a clear North Star around being the backbone for agentic code lifecycles. The current mix of Copilot branding, internal politics, and no CEO naturally pulls attention away from the boring but essential work of hardening the platform. But it is also worth being cautious with the clean narrative. GitHub runs a very different workload from a greenfield product in closed beta, with years of baggage, enterprise constraints, and a massive ecosystem to keep stable. Self-reported numbers from a startup and a rough month of incidents are not enough on their own to declare the incumbent broken or the new model proven. Shutting down Copilot or slicing away half the product surface sounds decisive, yet could throw away real value while the market is still figuring out how devs and agents should work together. The useful takeaway is not that GitHub is doomed or that an AI-only platform will automatically win, but that infrastructure and product strategy now have to be designed around agents and humans coexisting at scale. Getting that tradeoff right - reliability for everyone, while building new, agent-native primitives with a clear focus - will matter a lot more than any single outage or launch over the next few years. https://lnkd.in/dECY42Vt

GitHub Copilot Sessions: The Do's and Don'ts Sessions is GitHub's new agentic-first coding app — VS Code-friendly UI, powered by Copilot CLI. Still early, but here's how to get the most out of it. ✅ DO: → Scope your issues tightly One clear deliverable per session. "Add auth middleware to /api routes" beats "improve security." → Use copilot-instructions.md Set project conventions ONCE. Every session inherits them. Consistent output across agents. → Split complex tasks into smaller sessions Smaller sessions = better results. Let each agent focus on one thing. → Review every PR before merging Agent output is fast, not infallible. Always review for logic, security, and edge cases. → Use the Agents tab for visibility Track all sessions, PRs, and agent activity in one place. Essential for teams. → Combine with /fleet for parallel work Kick off multiple sessions on independent tasks simultaneously. → Add path-specific instructions Different rules for different folders. Frontend and backend can have separate conventions. ❌ DON'T: → Don't dump vague prompts "Make the app better" gives you nothing. Be specific or get generic output. → Don't skip testing Agent-generated code needs the same test coverage as human code. No exceptions. → Don't keep irrelevant files open More noise = worse suggestions. Open only what the agent needs to see. → Don't treat it as a replacement It augments your expertise. Architecture decisions, code reviews, and creative problem-solving still need you. → Don't ignore security Check for hardcoded secrets, SQL injection, unvalidated inputs. Agents don't think about threat models. → Don't forget to document Agent-generated code without context becomes tech debt. Document as you go. Sessions is early. But the direction is clear: agent-centric, not repo-centric. The future of coding isn't typing faster. It's orchestrating better. #GitHubCopilot #CopilotSessions #AgenticAI #DeveloperTools #CodingAgent #DevProductivity

Over the past few weeks, I’ve been using Claude Code and GitHub Copilot more actively. At first, I did what most of us do. I gave a single, big prompt and expected a clean, perfect solution. Sometimes it worked. Most of the time, it didn’t. The output was either too generic, slightly off or missing important pieces. And I realised the issue wasn’t the tool. It was how I was asking. Then I made one simple change. Instead of giving one large instruction, I started breaking my task into smaller, clear sub-tasks and feeding it step by step. The difference in output was immediate. Here’s a simple example. "Build a simple expense tracker app for daily use. It should help users log expenses quickly and track spending over time.” Then I broke it down: 1) Create input fields for date, category, and amount 2) Add a button to save each expense 3) Store data locally (local storage or database) 4) Show a list of all expenses 5) Add total spending summary 6) Include basic category-wise breakdown 7) Keep UI simple and mobile-friendly Now the output becomes structured, usable, and much closer to what you actually need. When you break down your thinking, the tool simply follows. This small habit didn’t just improve the output. It made me think more clearly about the problem itself. Structure your thoughts, because better input doesn’t just give better output, it builds better thinking. #claude #claudecode #github #copilot #githubcopilot #prompting #promptbreakdown #vscode #vibecoding

I've been putting more and more context into GitHub repos. Not just code, but the why behind decisions. Design docs, ADRs, transcripts of meetings where someone argued for approach A and someone else pushed back with approach B, etc. If you're using AI agents like Claude Code, this context matters a lot. Without it, the agent will re-ask questions your team already settled six months ago in a meeting. It can see the code but not the reasoning. The catch is that the more context you add, the harder it gets to find anything. grep "retry" doesn't find the issue titled "Resilience strategy for upstream timeouts." The words don't match even though the topic is the same. I looked at existing semantic search tools and they all have the same friction. Either you need to build an index before you can search (which goes stale), or a hosted service. Neither works when you just cloned a repo 2 minutes ago and need to find something now. That's the actual workflow when working with agents, though. So I built vex (Vector EXamine). Ask a question in plain English, get back the relevant code, docs, and issues. It searches by meaning. No setup, and it's fast enough to not break your flow. There's also vex sync github, which pulls your issues and PRs down as local Markdown. After that, searches automatically cover them too. So "why did we change the auth flow" might surface the PR discussion from three months ago, the architecture doc, and the code, all together. It ships with a Claude Code skill so agents can use it alongside their usual tools too. Still a work in progress. Try it and let me know what you think: https://lnkd.in/gKuEmB8w (Short video attached. 1.5gb of files, no index, results in under a second)

GitHub has introduced a new `gh skill` command in its CLI that makes it much easier to manage AI agent skills. With a simple command, developers can now discover, install, update, and publish skills directly from GitHub repositories, replacing manual setup with a streamlined, package-manager–like experience. On top of that, GitHub adds robustness features such as version pinning, immutable releases, and change detection based on Git metadata, helping ensure consistency, reproducibility, and security when sharing and evolving skills across teams. https://lnkd.in/dsha8y3K

A.I can make you save money and gain a competitive advantage * When you consider that the team of humans you’ve spent years, countless hours and effort to build, is too competent to want to use A.I to code, you’re actually saving money from subscriptions and tokens that your competitors are paying for! And after the 2 major upcoming IPOs, those prices are likely to ramp up for profit, with cheaper models getting more and more usage limits to stop subsidized usage applied today ( or the abrupt cancellations of models like Sora which can have devastating consequences on businesses that have built around these models that need to scramble overnight to find a replacement ). And when some problems will happen down the line that the AI won’t be able to fix, the humans who’ve maintained their skills and knowledge of how things work and how to build things from scratch will be the ones everyone will turn to. Having a team with such skills into the future that’s coming will be like being the agency with Cobol developers every bank and insurance companies want to pay millions to to maintain and evolve their projects. Over and above shipping code quality over speed today, we’re preparing for a future where those who will stand out will be those who’ll be able to build from scratch, debug and evolve projects without AI, offering unique solutions that rise above that of the client’s competitor, while everyone else will be competing around who can best prompt the same agent offering the same similar solutions to every competitor alike. Let alone figuring out how to evolve the project over 5 years if the original agents have disappeared ( e.g the builder.ai collapse of last year ) It’s not about just coding fast, it’s about understanding the clients needs properly, and executing it in a way that’s - efficient - safe - owned by the client - maintainable by any future developer without needing access to the original agents - sustainable - ethical - without technical debt - bespoke, with the dna of the client differentiating it from their competitors - cost efficient, when considering future evolutions over the tests - without vendor lock in And with that, we use AI just like we’d use any other stack, when appropriate for the project, in a controlled environment that’s coherent and safe for the client, and we all make money out of it, just not for coding unique bespoke solutions, nor thinking about innovative solutions to the challenges of our clients, this our talented humans do it better, with a unique angle different from the AI ones that will give our client an edge over their competitors 🙂

Hey devs 👋 I built a small desktop app for one annoying part of shipping code: writing commit messages that sound like you were done with life halfway through. It’s called **GitRoast**. You know the moment: you finish coding you run `git add .` and then suddenly your commit message is: `fix stuff` GitRoast reads your **staged changes** and gives you **one clean commit message** worth copying into your normal workflow. That’s it. I didn’t want this to become: * a full Git client * an auto-commit tool * a file editor * another workflow to learn The goal was simple: keep Git exactly how it is, just make commit writing faster and less boring. How it works: 1. Open your repo 2. Stage what matters 3. Click **Generate** 4. Copy the commit 5. Keep shipping 🚀 A few things I cared about: * repo inspection stays local * only staged Git data is sent when you click **Generate** * no full repo upload * no file editing * no auto-commits It’s built for devs who already like Git the way it is — they just want less friction and fewer commits like: `update` `fix` `stuff` `final_final_v2` If that sounds familiar, you might enjoy this 😄 👉 Check out the GitHub repo: (https://lnkd.in/ggQRQvp8) 💛 If you’d like to support GitRoast and future releases: (https://lnkd.in/gfK_BTvW) My commits still aren’t perfect. They’re just less emotionally exhausted now. #developers #git #productivity #desktopapp #buildinpublic #tauri #javascript #frontend #backend #ai #git #commit #fullstack #forfun

Documentation rarely fails loudly — it just quietly loses users. Learn how our Drasi team used GitHub Copilot as a “synthetic new user” to continuously test their documentation and surface bugs before real developers hit them. - Documentation breaks due to silent drift, not just bad instructions - Manual doc testing does not scale for fast‑moving open source projects - Treating documentation testing as a monitoring problem is a mindset shift - GitHub Copilot can follow instructions exactly as written, exposing gaps humans miss - “Getting started” guides deserve the same rigour as production code For many developers, documentation is the product. If the first experience fails, trust is gone — and users simply move on. If you’re shipping faster than you can manually test your docs, this is well worth reading. Read the full article here: How Drasi used GitHub Copilot to find documentation bugs https://msft.it/6049QAIED #OpenSource #GitHubCopilot #DeveloperExperience #Documentation #AIinDev #CloudNative #CNCF #MicrosoftAdvocate #MicrosoftEmployee #DevRel

If you maintain an open source project, you already know: your code doesn’t get judged by your benchmarks. It gets judged by your Getting Started guide. If step 3 fails… Nobody opens an issue. Nobody asks for help. They just close the tab. We learned this the hard way on Drasi (one of the projects in our Open Source Incubations team) when an upstream Dev Container update bumped the minimum Docker version and silently broke *every* tutorial by disrupting the Docker daemon connection. No failing builds. No red CI. Just a broken onboarding experience for every new developer trying the project. So we stopped treating documentation like something you occasionally QA and started treating it like something you monitor. Using GitHub Copilot, the Drasi team built an AI agent that behaves like a brand‑new user and executes tutorials exactly as written, with no tribal knowledge, no assumptions, and no skipped “obvious” steps. Which turns out to be perfect for catching things like implicit steps we forgot to write down and silent drift when dependencies or configs change upstream. We can do this before our community ever hits them. This is the kind of AI‑augmented workflow I’m most excited about in OSS: Not just writing code faster but making projects easier to adopt and contribute to. If you’ve ever rage‑quit a tutorial, this one’s for you: How Drasi used GitHub Copilot to find documentation bugs: https://lnkd.in/gjURztdh Would love to hear how other maintainers are thinking about testing docs like code. #OpenSource #Maintainers #CNCF #GitHubCopilot #DevEx #Drasi