GitHub Copilot CLI: AI-Powered Terminal Assistance

Lately, I’ve been diving into AI in Software Testing and getting hands-on with GitHub Copilot—and it’s been an interesting shift in how I approach development of test automation scripts. To make this exploration more structured, I’ve been following the GH-300 (https://lnkd.in/gC3ucbT4) curriculum, which has helped me go beyond just “using” Copilot to actually understand its: 🔹 Strengths Copilot is great at accelerating boilerplate code, suggesting reusable patterns and exploring pull requests—especially useful when working with frameworks like Playwright. 🔹 Limitations It still requires strong human oversight. Context gaps, incorrect assumptions, and occasional flaky suggestions mean you can’t rely on it blindly—especially in critical test scenarios. 🔹 Real Value in Testing When used thoughtfully, it can significantly speed up: ✔ Test case generation ✔ Locator strategies 🔹 The Mindset Shift It’s less about “AI writing code for you” and more about pair programming with context awareness. The better your prompts, the better the output. This journey is helping me understand how AI can augment test engineers, especially in building more resilient and scalable automation frameworks. Still early days, but definitely an exciting and compelling space to explore🚀. #GitHub #Copilot #AI #SoftwareTesting

GitHub Copilot + CLI = faster dev workflows. AI in the terminal is no longer a future thing. GitHub Copilot CLI helps you code, test, and iterate faster. https://lnkd.in/eWmnXQwW

Developers: you may want to check your GitHub settings before April 24. GitHub is updating its policy so interactions with personal repositories may be used for AI model training. If you’re using personal repos and don’t want that data included, you’ll need to opt out manually. Copilot Business and Enterprise users are not affected. Official announcement: https://lnkd.in/eMXCDsuF To opt out: Profile → Settings → Copilot → Features → Privacy Are you opting out, or are you fine with your repos being used for training? On one hand, they are public. Any unscrupulous actor could already be using them. If you're already using GitHub Coding Agent, it may improve your experience. An option to differentiate training on public vs private repos might make the decision easier. The blog announcement linked above includes this statement under what will not be used for training irrespective of your choice: *Content from your issues, discussions, or private repositories at rest. We use the phrase “at rest” deliberately because Copilot does process code from private repositories when you are actively using Copilot. This interaction data is required to run the service and could be used for model training unless you opt out.* Interaction data is defined as: "specifically inputs, outputs, code snippets, and associated context." That sounds like it includes commits. #github #ai #developer #dataprivacy #softwaredevelopment

It started with frustration. 💛 Every time I switched AI tools — Claude Code, Cursor, GitHub Copilot — I had to rebuild my agent setup from scratch. Different syntax. Different skill formats. Different everything. So I started collecting them. Obsessively. First it was just a folder of notes. Then a folder of folders. Then it had its own README. Then it had its own README with a table of contents. 784 agent definitions. 748 skills. 141 prompts. Not because I planned a big open source project — but because every time I figured out a better way to work in one tool, I didn't want to lose that knowledge when I moved to another. Over time it became AGENTS-COLLECTION. A living archive across 11 platforms: Claude Code, Cursor, Antigravity, OpenCode, GitHub Copilot, NanoBot, NanoClaw, OpenClaw, PicoClaw, ZeroClaw, and more. 2,700+ files. 30MB of agent definitions (600MB of runtime data excluded — only the knowledge counts). What started as notes to my future self is now TypeScript-structured and openly shared. If you're building with AI agents and feel like you're reinventing the wheel every time you switch tools — this one's for you. 🔗 https://lnkd.in/d6gV6g5N #AIAgents #ClaudeCode #Cursor #GitHubCopilot #PromptEngineering #LLM #BuildInPublic #OpenSource #AIEngineering #DeveloperTools

Seeing GitHub pause subscriptions to GitHub Copilot is starting to make me wonder about the real reasons. It pretty clearly points to the high costs of AI, and that Copilot’s pricing might actually be lower than it should be. It makes me question what happens in the future, if prices go up, could coding tools become less accessible, reserved only for those who can afford LLMs? Coding was my lifesaver back in 2019, will it one day become something only the rich can afford?

GitHub Copilot started injecting ads into developers' pull requests. Not a hypothetical. It happened this week. A developer asked Copilot to fix a typo. Instead of just making the correction, it rewrote the PR description and slipped in a promo for Copilot and Raycast. Buried in the markdown was a hidden HTML comment: START COPILOT CODING AGENT TIPS. Then someone searched GitHub for that exact phrase. Over 11,000 matching pull requests. Across thousands of repositories. The same promo text showed up on GitLab too — baked into the model layer, not the platform. GitHub pulled the feature within hours. Their principal PM called it "the wrong judgement call." But 11,000 PRs were already contaminated before anyone noticed. This is the pattern. You give a tool write access to your codebase, and somewhere in a product meeting, someone decides that access is also a distribution channel. The AI dev tools that survive long-term will be the ones that treat your code as yours, not as ad inventory. The moment your AI assistant starts working for someone other than you, it stops being an assistant. At what point does an AI tool inserting its own content into your work become a dealbreaker? #AI #GitHub #Copilot #DeveloperTools #SoftwareEngineering #OpenSource #TechEthics Join Agentic Engineering Club -> t.me/villson_hub

I've been building an open source CLI that runs AI coding agents for you. It breaks work into tasks, runs them in parallel across repos, then spawns a second model to review the first one's output. Shipped v0.2.5 today. The bit worth mentioning: the planner now detects what tooling your project has (subagents, MCP servers, instruction files) and bakes delegation hints into generated tasks. The agent figures out on its own to route security diffs to your auditor or UI checks to Playwright. No configuration needed. Works with Claude Code and GitHub Copilot. MIT licensed. https://lnkd.in/eaKu4yRm #opensource #aicoding #devtools #claudecode #githubcopilot #cli #aiagents

Claudesidian: Claude Code + Obsidian Starter Kit Turn your Obsidian vault into an AI-powered second brain using Claude Code. What is this? This is a pre-configured Obsidian vault structure designed to work seamlessly with Claude Code, enabling you to: Use AI as a thinking partner, not just a writing assistant Organize knowledge using the PARA method Maintain version control with Git Access your vault from anywhere (including mobile)

GitHub Copilot started injecting ads into developers' pull requests. Over 11,000 PRs were affected before anyone noticed. A developer asked Copilot to fix a typo in his PR description. Instead, it rewrote the text to include a promo for Raycast. Not a hallucination. Not a bug, despite what Microsoft later claimed. A product tip baked into the agent's output, placed directly into someone else's code review. Search GitHub for that exact promotional string and you get thousands of hits across unrelated repositories. This wasn't one bad output. It was systematic. GitHub killed the feature within hours after the backlash. Tim Rogers from their team admitted the judgment call was wrong. But the pattern here matters more than the apology. We've seen this before. Cory Doctorow calls it enshittification: platforms start useful, then squeeze users for business value, then squeeze businesses for their own margin. GitHub just speed-ran the cycle inside an AI feature. The real question for anyone building on top of these tools: if your AI coding assistant can silently alter your PR text, what else is it editing that you haven't caught? When you hand an AI tool write access to your codebase, you're trusting not just the model, but every product decision the company behind it will ever make. Trust is infrastructure. Treat it like one. #AI #GitHub #Copilot #DeveloperTools #OpenSource #SoftwareEngineering #TechEthics Join Agentic Engineering Club → t.me/villson_hub

GitHub Spec Kit v0.7.4 is out with practical upgrades for AI-assisted workflows. The release improves non-interactive Copilot permissions, fixes UTF-8 BOM issues in agent context files, adds academic citation support, and expands the community catalog with new tools like spec-validate, version-guard, and memory-loader.