GitHub's Copilot Update: Opt Out of Data Use for AI Training

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 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

GitHub dropping Opus from Copilot Pro is not really a model story. It is a packaging story. Agentic coding is expensive. Flat-rate pricing was always going to break. Users can accept limits. They do not accept surprises. That is why this hit so hard. My take: this is the clearest sign yet that AI dev tools will be priced around capacity, not just model quality. The real question now: cheap monthly price, or reliable agent capacity?

The premise needs a small correction first: GitHub Copilot hasn't actually lost the AI coding war — but it has gone from dominant pioneer to a genuinely threatened incumbent. Here's the full picture: Copilot is struggling, but still standing Copilot holds 42% market share and has reached 20 million cumulative users, deployed across 90% of Fortune 100 companies. Quantumrun Those are not the numbers of a defeated product. But the competitive pressure is very real, and the cracks are showing. Why developers are losing faith: Developer complaints about suggestion quality, latency, and context awareness have grown significantly since late 2025, with model swaps being a primary cause — GitHub cycled through Codex, multiple GPT-4 variants, and GPT-5 series, and each transition introduced regressions. Nxcode In March 2026, Copilot injected promotional "tips" into over 1.5 million pull requests, badly eroding developer trust. Nxcode Copilot's suggestion acceptance rate sits at 35–40%, compared to Cursor's 42–45%. Nxcode Tasks requiring changes across 10+ files with architectural implications produce noticeably more mistakes than competing tools. Nxcode Its characteristic failure mode is the confident wrong answer.

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?

Copilot just escaped the IDE! The GitHub Copilot SDK hit public preview this week, and the premise is simple: the same agentic engine that runs inside your editor (planner, tool loop, runtime and all) is now available to embed directly into your own applications, internal tools, and workflows. It supports Node.js, TypeScript, Python, Go, .NET, and Java, with custom tool definitions, MCP integration, streaming responses, a proper permissions framework, and OpenTelemetry support built in. With BYOK support, you can point it at your own API keys: Anthropic, OpenAI, Microsoft Foundry,... What I find most interesting isn't the feature list... It's what the SDK signals architecturally. AI coding assistance moving from something you use to something you embed. Your internal platforms, your pipelines, your custom agents... Copilot as infrastructure rather than interface. That's a different conversation than "should we buy seats"... #GitHubCopilot #NotAHallucination

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

One of my favorite things about GitHub Copilot CLI: you can just ask it to use multiple models for a task. No need to switch models yourself. Just tell Copilot to get a second opinion from a different model. Or ask it to use different personas, one to build, another to review. Why does this work? Different model families have different strengths and blind spots. One might nail the architecture but miss an edge case. Another might catch a subtle bug the first one overlooked. It's the same principle behind the new Rubber Duck feature, cross-model review catches things self-review doesn't. But you don't need to wait for that feature. You can do this right now by just asking. Next time you're working on something complex, try: "review this code using a different model" or "give me a second opinion on this plan." The second perspective is worth it. #GitHubCopilot #AI #developer #programming

Is GitHub Copilot turning us into code-generating monkeys? It's the debate raging through dev circles: this AI pair programmer, powerful as it is, might be actively eroding our core skills. Here's my take: - Copilot excels at boilerplate, repetitive tasks, and even suggesting complex algorithms. This is undeniable. - But are we understanding the code it generates, or just accepting it? True debugging and problem-solving skills atrophy if we rely on it blindly. - The risk is becoming an "autocomplete jockey" rather than a deeply analytical engineer. We might lose the intuition that comes from wrestling with problems ourselves. - It's a tool, not a replacement for fundamental knowledge. Like a calculator, it's great for speed, but you still need to know math. The danger isn't Copilot itself, but how we choose to use it. It should augment our abilities, not become a crutch that prevents growth. Let's be mindful. Save this if you're wrestling with this question. Follow for more unfiltered tech takes. #AIinTech #DeveloperLife #SoftwareEngineering #FutureOfCode

More beginners are sharing how GitHub Copilot helped them ship apps faster than they expected. It’s not about the AI writing perfect code. It’s about boosting confidence to experiment and fix mistakes early. This trend shows the new norm: coding with AI as a teammate, not a crutch. If you haven’t tried Copilot, now’s a great time to see how it changes your workflow.