Exploring AI in Software Testing with GitHub Copilot

GitHub Copilot CLI brings AI assistance directly to your terminal. Instead of switching to a browser or code editor, you can ask questions, generate full-featured applications, review code, generate tests, and debug issues without leaving your command line. here is the beginner samples https://lnkd.in/g4RMVENQ #GenAI #AI #Github #Copilot

Your private code is now a training manual! 🤯💻 GitHub recently confirmed that starting April 24, Copilot will use your interactions and private code snippets to train their models by default. 🚨 The kicker? You are likely already opted in! 😤 🚀 The 30 Second Fix 🚀 1️⃣ Nav to: https://lnkd.in/eAsvEQRZ 🌐 2️⃣ Find Allow GitHub to use my data for AI model training 🔍 3️⃣ Set it to Disabled ❌ 💼 Who is safe? 💼 Business and Enterprise users are skipped! This update only hits Free, Pro, and Pro+ accounts. 🎯 💡 The Master Take 💡 Opt Out by default is a bold move. Protect your "secret sauce" before the deadline hits. 🛡️✍️ Source: https://lnkd.in/ect8ptvQ 🔗 #GitHub #Copilot #AI #DataPrivacy #Coding #TechNews #SoftwareEngineering #Programming

Cursor vs GitHub After 6 months of deep evaluation across multiple engineering teams, the developer experience gap is wider than expected. SETUP & ONBOARDING: Cursor wins decisively here. Download, authenticate, and you're coding with AI in under 5 minutes. GitHub requires VS Code setup, extension management, and often wrestling with authentication flows that can take 20-30 minutes for new team members. DOCUMENTATION QUALITY: GitHub Copilot benefits from Microsoft's enterprise documentation machine - comprehensive but sometimes overwhelming. Cursor's docs are leaner, more example-driven, and get developers to their "aha moment" faster. SDK & INTEGRATION: This is where it gets interesting. Copilot's tight VS Code integration means familiar keybindings and workflows. But Cursor's purpose-built environment offers features like AI-powered refactoring and codebase-wide context that feel genuinely next-generation. DEVELOPER HAPPINESS: Our internal surveys show 73% preference for Cursor among developers who've used both for 30+ days. The key differentiator? Less friction between thought and code. The surprising insight: tool switching costs are lower than we assumed. Most teams can evaluate both in a sprint. Which tool has transformed your team's velocity the most? See the full comparison: https://lnkd.in/e2fGGryV #Cursor #GitHubCopilot #DeveloperExperience

The Return of the Architect — Why Code Still Matters 🛠️✨ The "End of Coding" was a myth. We are entering the age of the "Architect-Engineer." I’ve been analyzing the latest insights from GitHub’s COO, Kyle Daigle, and the message is clear: It is more important than ever to understand the logic of technology, even as AI does the "heavy lifting." We are shifting from simple AI assistants to Agentic AI—systems that can break down problems and build entire solutions. But this shift creates a new, massive responsibility for leaders and creators: The Reviewer’s Burden: If you cannot "read" the logic of the code, you cannot verify the work of the AI. You become a passenger in a vehicle you can't control. Logic Over Syntax: We no longer need to be "code-monkeys" memorizing every command. We need to be Architects of Logic who understand how systems flow and how they impact the human experience. The Human Spark: AI can generate a million lines of code, but only a human understands the "Why"—the community, the purpose, and the ethical guardrails that make a product successful. The Strategic Reality: Companies that laid off developers are realizing that AI doesn't replace the thinker; it only amplifies the builder. This is why I am pushing through the "friction" of learning Python and Linux. Not to become a developer, but to ensure I remain an Architect of my own future. In an era of mass surveillance and automated content, your ability to understand the "Engine" is what gives you the power to design the "Studio." Are you mastering the logic, or just pushing the buttons? #AI #FutureOfWork #GitHub #Leadership #SoftwareEngineering #TechStrategy #Innovation #AgenticAI #HumanCentricTech

Ever tried rebuilding a GitHub repo without just hitting the fork button? Yeah… me neither. Until I decided to make it harder for myself. So I built this: https://lnkd.in/dnX2F7Q8 -- Paste any GitHub repo -- Click analyze -- Get a full AI-generated prompt to rebuild it from scratch Basically… instead of cloning code, you generate instructions to recreate it. Now I know what you're thinking: “bro just fork it” And you’re 100% right. But where’s the fun in that :) Why I did this: Because actually building something teaches way more than just copying it. What I gained: * Deeper understanding of real-world project structures * Hands-on debugging experience (a LOT of it 😅) * Better intuition for how systems are actually built If you're learning dev/AI, try this once pick a repo and rebuild it yourself. It hits different. #AI #MachineLearning #GenAI #GitHub #BuildInPublic #DeveloperJourney

GitHub Copilot Launches Repository Memory to Generate Organic Pull Requests 📌 GitHub Copilot’s new Repository Memory lets coding agents learn from a project’s evolution-not just its final state-so they generate pull requests that feel organic, not alien. This shift turns code generation into a continuous learning process, mirroring how human engineers study history before contributing. The result? Less redundant code, fewer rejections, and smarter, more realistic AI contributions. 🔗 Read more: https://lnkd.in/dvFfCA8d #Githubcopilot #Learningtocommit #Tsinghuauniversity #Llmcodingagents #Repositorymemory

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

I recently came across an interesting stat: over 40% of code on platforms like GitHub is now being written with the help of AI tools like GitHub Copilot. Sounds impressive — but also a bit concerning. Because generating code is becoming easy. Understanding it? Still hard. We’re slowly moving into a phase where: • Writing code is cheap • Debugging and system thinking is expensive The real skill now isn’t coding fast — it’s knowing what not to trust.

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?

Typed “hi” into GitHub Copilot Chat inside VSCode and the logs were eye-opening. That tiny greeting triggered a request carrying ~18,000 prompt tokens. Not because “hi” is expensive — because context is. Even simple prompts can include a large background payload such as: • Tool definitions — what the assistant can access (search, edit, terminal, git, notebooks, etc.) • Instruction layers — rules for how the agent should behave • Project context — workspace structure and relevant files • Active focus — open tabs, selected code, current editor state • Memory/state — prior chat history, preferences, session context Different products implement this differently, but the pattern is consistent. Some tips on keeping context efficient: • We tend to accumulate tools over time. Periodically audit them and keep only high-value tools always enabled. • Use Skills, since they are invoked only when relevant instead of staying always-on • Keep your workspace focused when asking questions Every token saved can create more room for useful context. 🙂 #GitHub #Copilot #VSCode #AI #Assistant #Coding #DeveloperTools #LLM #GenAI