Migrating SVN to GitHub with AI Assistance

Have you ever wondered why Claude Opus feels DIFFERENT in Claude Code vs GitHub Copilot, even though it's the same model? Same LLM. Same weights. Same intelligence. But completely different behavior, capability and output. The answer is NOT the model. It's the "HARNESS". What is a Harness? A harness is the invisible orchestration layer that wraps around an LLM. It consists of: -> Function Tools: what the model is allowed to do(read files, run code, search web, call APIs) -> Agent Orchestration: how the model thinks and plans across multiple steps -> System Prompts: the personality, constraints and context given to the model -> Memory & Context Management: what the model remembers across a session -> Feedback Loops: how the model self-corrects and retries Claude Code vs GitHub Copilot Claude Code's harness gives Claude Opus: Full file system access Terminal/bash execution Agentic multi-step planning Ability to run, test and fix code autonomously GitHub Copilot's harness gives Claude Opus: IDE context awareness Inline code suggestions PR and diff understanding GitHub ecosystem integration Same brain but different hands. Different environment so different results. Andrej Karpathy, one of the founding fathers of modern AI, has pointed out something that most people overlook: "A smaller model with a great harness can outperform a much larger model with a poor one." A well-harnessed GPT-4o Mini can beat a poorly-harnessed GPT-4. A well-harnessed Claude Haiku can beat a poorly-harnessed Claude Opus. The harness multiplies the model's potential or it limits it. So the real competitive advantage is how you harness a model. The best AI teams in the world aren't just picking the strongest model but they're engineering the best harness around it. Tool design. Agent loops. Context strategy. Orchestration architecture. That is where the real differentiation happens. "The model is the engine. The harness is the vehicle." What are your views on this? 🤔 #AI #LLM #AgentAI #ClaudeAI #GitHubCopilot #AIEngineering #GenAI #MLOps #SoftwareEngineering #AITools

Building in public # debugging is where the real learning happens. 🎯 My AI assistant crashed mid-conversation yesterday. Best thing that happened to me all week. 🤯 Here's what went down. 👇 I'm building my own personal AI assistant using Claude Cowork 🤖 connected to #Todoist so it can manage my tasks, run my daily shutdown ritual, and set me up for the next morning. Mid-session, I got error 401 😤 My assistant was literally in the middle of pulling my task list. Gone. 💨 So I stopped. Went to the logs. Traced what failed. 🔍 2 hours later, I fixed it. Claude Code created new code. Uploaded to #GitHub. Pushed to #Railway with a custom MCP connected to #Todoist. Redeployed. 🔧✅ Nobody talks about how much of learning Claude Code is actually just… debugging. 🤫 And here's what I realized: debugging IS the learning. 💡 With Claude Code specifically, debugging taught me more about how agents reason 🧠, how to ask for help ⛓️, and where context goes wrong than any tutorial ever could. It's unglamorous. It's slow. 🐢 And it's the most important skill you can build. 💪 If you're learning AI development and feeling stuck in a debugging loop, you're not behind. You're exactly where the real learning happens. 🚀 What's a bug that taught you something you couldn't have learned any other way? Drop it below. 👇 #BuildInPublic #ClaudeCode #AgenticAI #Debugging #LearnInPublic #AIEngineering #PersonalAI #SoftwareDevelopment #GitHub #Railway #MCPServer #AIAssistant #100DaysOfCode

I shipped code faster than ever, but debugging became a nightmare. Last month, I was working on a feature using GitHub Copilot. It felt like magic. Code suggestions sped up my workflow, and I was shipping changes in record time. Until a critical bug appeared in production. → AI tools suggested code that looked right but was hard to understand. When the bug hit, I realized I hadn't fully grasped how the logic worked. → Debugging a problem in code you didn't write is twice as hard. I spent hours tracing through lines, trying to piece together the AI's thought process. → The cost isn't just time. It's confidence. Relying too much on AI made me second-guess my skills when things went wrong. After this, I changed my approach. I use AI to suggest code, but I invest time in understanding each piece before moving on. Now, I ship code a bit slower, but I debug with less stress and more confidence. Have you ever faced similar challenges with AI tools? How do you balance speed with understanding? #MERNStack #AIInDevelopment #GitHubCopilot #Debugging #CodingChallenges

The AI coding tool market just got genuinely competitive. For most of 2025 and into early 2026, Claude Code had a clear quality lead. Not a small one. Engineers who used it on real codebases knew the gap was wide. Complex reasoning, large context, instruction-following on hard refactors. Nothing else came close. That gap is closing faster than most people expected. GitHub Copilot Coding Agent is generally available now, with a browser, VS Code integration, and a proper async task model. OpenAI Codex CLI is open source and has been improving steadily. Gemini CLI has had a few quiet releases that surprised people paying attention. And Claude Code, at exactly this moment, has a documented quality regression. GitHub issue 42796 has 178 comments from engineers reporting the same failures since February. The community workaround is a single line in CLAUDE.md: behave like you did in January. That it works tells you something about how the model is being served under pressure. What I keep thinking about is the infrastructure math. Hundreds of thousands of developers adopted Claude Code over the last six months. Serving all of them at the quality they bought the product for requires enormous compute. Every major LLM company has gone through this scaling crunch. Anthropic will fix it. But the window where competitors were clearly behind has closed. Teams that assumed one tool stays dominant indefinitely are now rebuilding workflows mid-project. My answer to which tool to build around in Q3 has changed. Has yours? #ClaudeCode #AIEngineering #DeveloperTools

The AI coding landscape has shifted drastically in 2026, moving from simple autocompletes to fully autonomous agents. Choosing the right tool now depends entirely on your specific workflow and technical needs. This comparison breaks down the three current giants: Claude Code, Cursor, and GitHub Copilot: • Claude Code (The Power User Choice): Operating as a terminal-native agent, it is built for complex refactoring and autonomous multi-file edits. It offers the highest level of agentic autonomy but comes with a steeper learning curve for those comfortable in the CLI. • Cursor (The Daily Driver): As an AI-native IDE, it provides the best tab-completion experience and a familiar VS Code environment. It’s the top pick for greenfield projects where you need a visual interface and multi-model flexibility. • GitHub Copilot (The Enterprise Standard): Still the king of low-friction adoption, it integrates deeply with the GitHub ecosystem. It’s the go-to for large teams requiring SOC 2 compliance and IP indemnity. With 95% of developers now using AI tools weekly, the question isn't whether to use them, but how to stack them. Many are finding the "Power Stack"—using Cursor for daily coding and Claude Code for heavy lifting—to be the winning combo. Which of these has made the biggest impact on your deployment speed this year? . . . #AICoding #SoftwareEngineering #ClaudeCode #CursorAI #GitHubCopilot #DeveloperTools #Programming2026

When I said we lived in an AI 𝗯𝘂𝗯𝗯𝗹𝗲, nobody believed me. The 𝗱𝗲𝗺𝗼𝗰𝗿𝗮𝘁𝗶𝗰 manifesto was saying: “everybody can code, everybody should code.” Yesterday, GitHub paused new sign-ups for GitHub Copilot Pro, Pro+, and Student plans.  Also planned to increase the fee on AI consumption. We’re using resources out of a marketing program and an 𝗶𝗱𝗲𝗼𝗹𝗼𝗴𝗶𝗰𝗮𝗹 one. It’s a subsidized marketing with 𝗰𝗵𝗲𝗮𝗽 tokens to 𝗵𝗼𝗼𝗸 users moving to a sustainable infrastructure or real-world 𝗽𝗿𝗶𝗰𝗶𝗻𝗴. The only way to survive is using local models as a "bridge" or a "pre-processor" for giants like Gemini and Claude, and the only 𝗹𝗼𝗴𝗶𝗰𝗮𝗹 move to avoid going 𝗯𝗿𝗼𝗸𝗲 while staying productive. For instance, models like Context7, an open-source Model Context Protocol (MCP) server developed by Upstash, provide AI coding 𝗮𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁𝘀 with real-time, up-to-date documentation for programming libraries and frameworks. It addresses a critical problem: AI models often have outdated knowledge about software libraries or hallucinate deprecated APIs, leading to incorrect code suggestions. See how to wire into 𝗚𝗶𝘁𝗛𝘂𝗯 𝗖𝗼𝗽𝗶𝗹𝗼𝘁 𝗖𝗟I, with the container booting autonomously every time the terminal opens. https://lnkd.in/dEGvAauq #AICoding #MCP #GitHubCopilot #Upstash #Context7 #LocalAI #DevTools #OpenSource #SoftwareArchitecture #CodingBubble #LLM #TechStrategy

GitHub Copilot now defaults to GPT-4.1 across chat, agent mode, and code completions. But the model is just 20% of the story. Here's what actually happens when Copilot suggests code: → Context gathering: current file, neighboring files, repo structure, file paths → Code snippet sent to cloud (encrypted, processed, not stored) → GPT-4.1 generates completion → Post-processing: filter insecure code suggestions, re-rank based on your previous choices → Telemetry feeds back to improve future suggestions The UX tricks: → Speculative suggestions: prefetch likely completions before you ask → Diffing model: returns only the diff, not the whole function → 30+ programming languages supported The agentic layer (Coding Agent): → Can navigate your codebase independently → Makes decisions about file modifications → Executes terminal commands → Verifies changes work correctly → Uses isolated environments (separate branch per task) Copilot evolved from autocomplete → chat → agent in 3 years. The architecture evolved with it. I decoded the full system — from keystroke to suggestion — in a visual breakdown. Swipe through. This is how your AI pair programmer actually works. That's a wrap on Series 3: AI Architecture Decoded — 12 products, 12 architectures, 12 engineering stories you'll never find in a tutorial. Thank you for learning with me. 🙏 Which product architecture blew your mind the most? 👇 ### Sources - [Inside GitHub Copilot's Architecture (DEV Community)](https://lnkd.in/g7C5fceF) - [Under the Hood: AI Models Powering Copilot (GitHub Blog)](https://lnkd.in/gDPz_7hX) - [GitHub Copilot Coding Agent Architecture (ITNEXT)](https://lnkd.in/gjPZJyQr) - [How to Maximize Copilot's Agentic Capabilities (GitHub Blog)](https://lnkd.in/gJWFx4mc)

Should I ask Claude Code to... Code? It’s been 3 years since I first subscribed to GitHub Copilot, and the evolution of AI tools has been fast to say the least. However, I’ve come to a realisation that feels slightly counter-intuitive: I’ve stopped asking AI to code. Or, at least, I don’t ask it to code directly. I was recently reading an article on how tools like Claude Code perform best when you stop "asking for code," and it became apparent. Good Software Engineering has always been about solving the problem first, then implementing the solution. If you start writing code before you have a clear solution, you usually end up with more problems than you started with. I consistently get better results when I: 1. Refine the plan and logic first. 2. Establish a clear architectural direction. 3. Use the AI as a sounding board for the strategy before the syntax. Do you think AI is making us better architects, or lazier coders? 🤔 Next time you’re using Copilot or Claude, focus on the problem. Prompt it until you find the solution, then let it generate the code based on that roadmap. #SoftwareEngineering #GitHubCopilot #ClaudeAI #ProgrammingTips #ArtificialIntelligence #TechThoughtLeadership

💻 Vibe coding is reshaping how modern software gets built. With tools like Claude Code and GitHub Copilot, the focus shifts toward architecture, problem-solving, and intent — while AI accelerates execution. Faster iteration, cleaner code, and a new developer mindset. #AI #SoftwareEngineering #GitHubCopilot #ClaudeCode #Productivity

Day 67 Today, I developed a small mock test website using AI tools for practice. Initially, I prepared PDFs containing the questions. Then, using AI tools like Claude, I converted those PDFs into JSON format and stored the data in a "data.js" file. For development, I used GitHub Copilot by giving structured prompts, which helped me build both the functionality and the UI design efficiently. The final output was successful, and I achieved the expected result: 🔗 https://lnkd.in/gKEcQvuq What surprised me the most was that I was able to build the entire project within 3 hours using AI assistance. After development, I decided to deploy the project on Vercel. Since it was my first time deploying, I had no prior knowledge. I relied on AI guidance throughout the process. During deployment, I faced several bugs, and resolving them took more than 1 hour. Through this experience, I also learned and practiced important Git commands required to push code to GitHub. Key Learnings: - Development is important, but debugging is even more critical - Patience plays a major role when solving bugs - Even small issues require checking code carefully, sometimes line by line - AI tools can significantly speed up development, but understanding the process is essential - Deployment is not just a final step — it’s a learning phase on its own #Day67 #WebDevelopment #AI #LearningJourney #Vercel #GitHub #FrontendDevelopment