Torvald Baade Bringsvor’s Post

Most of us use GitHub Copilot like autocomplete… I felt the same while building a full-stack system. It kept giving: generic code inefficient business logic - giving the universal logics instead of Architecture oriented zero awareness of system architecture So I tried something different 👇 👉 Instead of writing better prompts, I designed a system around Copilot. Custom agents (like roles for AI) Global instructions Domain skills + repo context Result? What has been the Outcome. Copilot stopped guessing… and started behaving like a context-aware engineer. I wrote a full breakdown + case study here: 👉 https://lnkd.in/guzTgCEY Big takeaway: AI doesn’t get better with prompts. It gets better with structure. Curious — how are you using Copilot today? Still prompting… or building systems around it? 👀 #AI #GitHubCopilot #SoftwareEngineering #DeveloperTools #BuildInPublic #MachineLearning

Ollama and GitHub Copilot. Another important union for privacy and power in the terminal.... The AI development landscape gains another chapter in its evolution. The announcement that Ollama now supports GitHub Copilot CLI reinforces an integration movement seeking the balance between cloud intelligence and local processing security. To date, no one holds an exclusive path to efficiency, but this combination of tools certainly opens new doors for developers. What this integration allows you to do now: - Repository Exploration. You can use Copilot CLI to map codebases and understand complex structures with the support of local processing. - Terminal Automation. This union allows for task planning based on GitHub tickets where AI assists in editing files and installing dependencies more fluidly. - Privacy and Control. By using Ollama as a backend option, developers gain another layer of choice regarding where their sensitive context should be processed. My personal analysis on this movement In my view, what we are witnessing is the consolidation of a hybrid model. Copilot's support for Ollama is another step acknowledging that the future of corporate software will not be centered on a single closed solution. My predictive analysis is that the terminal will remain the primary command center, now powered by agents that respect the security perimeter of each project. True productivity does not stem from a single tool, but from the ability to integrate the best available solutions into your workflow. #Ollama #GitHubCopilot #AI #OpenSource #CTO #SoftwareDevelopment #Privacy #TechTrends #Coding

The latest changes to GitHub Copilot don’t just feel like a product update — they feel like a reality check. Tightened usage limits, reduced model access, and paused new sign-ups all point to one thing: the current model for AI developer tools may not be sustainable at scale. Yes, agentic workflows are more demanding. Yes, infrastructure costs are real. But from a user perspective, this shifts the burden back to developers — forcing us to think about tokens, limits, and model multipliers while trying to stay productive. That’s a step in the wrong direction. One of the biggest promises of tools like Copilot was to reduce cognitive load, not introduce a new layer of resource management. What’s more concerning is the signal this sends: If even a flagship product like Copilot needs to pull back on availability and tighten limits, what does that mean for the long-term viability of AI-assisted development as we know it today? Transparency improvements (like usage visibility in VS Code) are welcome — but they don’t address the core issue: 👉 The gap between how these tools are marketed vs how they actually scale under real-world usage. This feels less like a temporary adjustment, and more like the beginning of a pricing and capability correction across the industry. https://lnkd.in/gZuuVBE8 #GitHubCopilot

GitHub Copilot Launches New AI-Generated Software Framework for Developers 📌 GitHub Copilot unleashes a new AI-generated software framework, transforming dev workflows from snippets to full ecosystems - think encrypted vaults and remote shells. Vibe coding is no longer fantasy; it’s powering 41% of 2025 code, with giants like Snap using AI for over 65%. DevOps teams now wield agentic tools, GPU-accelerated SDKs, and context-rich models to rebuild systems faster - and smarter. 🔗 Read more: https://lnkd.in/djMtQtKC #Githubcopilot #Llm #Vibecoding #Softwareframework #Developertool

GitHub has announced updates to its Copilot individual plans, signaling adjustments in how AI-powered coding tools are positioned for developers. These changes highlight the rapid evolution of AI in software development and the growing importance of flexible access models for developers worldwide. 🔗 https://lnkd.in/gkMkK-NF #GitHub #Copilot #ArtificialIntelligence #SoftwareDevelopment #DeveloperTools #TechIndustry #Innovation #DigitalTransformation

GitHub Copilot gets called an autocomplete tool. That undersells it significantly. Yes, it suggests code inline as you type. But the more interesting capability is what it does at the workspace level — it can read your entire codebase, answer questions about it, explain unfamiliar code, and act as an agent that spans multiple files. The numbers back up the adoption: 1.8M+ paid subscribers, integrations across every major IDE, and deep GitHub ecosystem access that no third-party coding agent can replicate natively. If you're evaluating AI coding tools, GitHub Copilot is the baseline everything else gets compared to. Full profile and alternatives → https://lnkd.in/ewkPwZeA

Stop Wasting Tokens: The 2026 GitHub Copilot Power Guide 🚀🛠️ Over the past few years, GitHub Copilot has evolved far beyond autocomplete. What used to be helpful suggestions is now closer to a system of specialized AI agents that can assist across your entire workflow. And with that shift, how we use it as developers is changing too. 🛠️ From prompting → to delegation Instead of relying on a single “do everything” approach, Copilot works best when you guide it clearly: • @terminal → for CLI, scripts, debugging • @docs → for accurate framework references • @test → for generating unit tests quickly 👉 Small shift, big impact on productivity ⚡ Thinking in systems, not steps One of the biggest unlocks is using tools like Composer for multi-file workflows. Instead of breaking tasks into many prompts, you can describe the outcome: “Add a Stripe webhook with a success email flow” …and let Copilot handle structure across files. 👉 Less back-and-forth, more momentum 🧠 Context matters more than ever Copilot performs best when the context is clear and focused. A few habits that help: • Keep only relevant files open • Use explicit references like #file:UserController.ts • Avoid vague descriptions when you can be precise 👉 Better context → better results 🧬 Let your types do the talking Providing structure (TypeScript interfaces, schemas) often works better than long explanations. It helps Copilot align with your system faster and more accurately. 🔁 Consistency improves results Using a simple structure for prompts: [Task] [Context] [Constraints] [Output Format] …can noticeably improve both output quality and efficiency over time. 🚀 The bigger shift As developers, the value is gradually moving from: Writing every line of code → Designing how systems get built Copilot is no longer just a tool you use. It’s something you collaborate with and guide. Curious how others are adapting their workflows—what’s been your biggest unlock so far? #GitHubCopilot #AIEngineering #SoftwareDevelopment #DeveloperProductivity #DevTools #GenerativeAI #TechLeadership #SeniorDevelopers #AIWorkflow

I had a conversation yesterday about GitHub Copilot that stuck with me. It started first. It had distribution, trust, enterprise adoption. On paper, it should have dominated. Instead, it feels like it’s constantly catching up with tools like Cursor or Claude Code. Why? Not because it’s worse. Because it played a different game. Copilot optimized for doing things “right”: security, compliance, controlled evolution, zero data retention. Others optimized for speed: ship fast, iterate faster, learn in public. And in AI tooling right now, speed compounds more than quality. This is the part that matters: 👉 this is not about “speed vs quality” 👉 it’s about where the boundary is today In stable markets, quality dominates. In expanding markets, speed wins—because being late costs more than being imperfect. AI is pushing that boundary further than we’re used to. We’re normalizing probabilistic correctness, incomplete features and constantly evolving behavior. Things that would have been unacceptable a few years ago. So the real challenge isn’t choosing one side. It’s understanding how far you can bend quality before it breaks trust. Too conservative → irrelevant Too aggressive → unreliable The hard part is that this line moves. I wrote a deeper take on this (and why this starts to look like a “new Nokia moment”): 👉 https://lnkd.in/dEcyE8Kn Curious how others are handling this shift internally.

𝗬𝗼𝘂 𝗵𝗮𝘃𝗲 𝟵 𝗱𝗮𝘆𝘀 𝘁𝗼 𝗼𝗽𝘁 𝗼𝘂𝘁 𝗼𝗳 𝗚𝗶𝘁𝗛𝘂𝗯 𝗖𝗼𝗽𝗶𝗹𝗼𝘁 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗼𝗻 𝘆𝗼𝘂𝗿 𝗰𝗼𝗱𝗲. On April 24, GitHub starts using interaction data from Copilot Free, Pro, and Pro+ accounts to train its AI models - opt-in by default. That means prompts, suggestions, and code snippets from your sessions, including private repos. Business and Enterprise plans are not affected. If you are on an individual plan and this matters to you, you need to opt out before the 24th. ● 𝗪𝗵𝗮𝘁'𝘀 𝗰𝗼𝗹𝗹𝗲𝗰𝘁𝗲𝗱 - your prompts, Copilot's suggestions, code snippets, and session context from both public and private repos; stored repo contents at rest are not included ● 𝗪𝗵𝗼 𝗶𝘀 𝗮𝗳𝗳𝗲𝗰𝘁𝗲𝗱 - Free, Pro, and Pro+ individual users only; Copilot Business and Enterprise have separate enterprise data terms and are not in scope ● 𝗛𝗼𝘄 𝘁𝗼 𝗼𝗽𝘁 𝗼𝘂𝘁 - GitHub Settings > Copilot > Features > disable "Allow GitHub to use my data for AI model training"; takes effect immediately 💡 If you manage a team where developers use individual Copilot plans, now is the time to communicate this - especially if your contributor agreements or client contracts restrict how interaction data from private repos can be used. Have you already opted out, or are you comfortable with the default? #GitHubCopilot #DeveloperPrivacy #GitHub #AIPolicy

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