Vlad Lavysh’s Post

GitHub Copilot Pro users just lost the Opus frontier-model lane. GitHub may call it plan restructuring. Developers will call it being pushed up the pricing ladder. That is not just a model upgrade. That is a cost-model upgrade. The real AI coding question is shifting from: “Can this model write code?” to “Can our team afford to let it think?” Pretty soon, token budgets may start looking scarier than HR budgets. Welcome to the new engineering economy. #GitHubCopilot #AIEngineering #SoftwareEngineering #DeveloperProductivity #AgenticAI #GenAI

Running a full dev stack with zero developers on the team. Here's the setup that made it possible, and yes, it's production-grade. Once the prototype was connected to GitHub, I opened the repo in Cursor IDE and ran Claude Code inside the terminal. Claude reads the codebase, writes code, creates files, and runs tasks, all from a plain-English instruction. No need to understand every line. For infrastructure, I connected: → Supabase (database, auth, storage) → Vercel (live hosting + auto-deploy on every GitHub push) When errors showed up? Copy the logs. Paste to Claude. It fixes them. The full loop: Cursor → Git commit → GitHub → Vercel → Live app 🚀 This isn't vibe coding. It's structured, version-controlled,  production-ready work, with AI doing the heavy lifting. PMs: you don't need a dev team to validate an idea anymore. #ProductManagement #AI #ClaudeAI #Cluadecode #CursorAI #BuildInPublic #NoCode #AITools #SoftwareDevelopment #FutureOfWork #TechLeadership

I was skeptical of AI coding tools for a long time. I thought they'd write mediocre code and I'd spend more time fixing it than writing it myself. A year of using GitHub Copilot, Cursor, and Windsurf later, here's what actually changed: Boilerplate: I used to write it from memory every time. Now it's generated in seconds and I just review it. That alone saves 30-40 minutes a day. Context switching: I used to jump between IDE, docs, Stack Overflow constantly. Now I ask inline and stay in flow. The answers come with context about my actual code. Unit tests: honestly? I used to skip them under deadline pressure. Now they're generated alongside the feature and I'm actually shipping with better coverage. Refactoring: the thing I avoided most. Now I do it more often because the AI explains what will break before I commit. The key insight: it didn't make me think less. It removed the friction around thinking. Senior developers benefit from this more than juniors, because we know exactly what to ask for and we can immediately tell when the output is wrong. Are you using AI tools in your daily workflow yet? #GitHubCopilot #Cursor #Windsurf #Java #AITools #SoftwareEngineering #Developer

As so many, I'd already landed on a pattern: using one model for development support, a different model family for code review. Just because the reviews were genuinely sharper. No grand theory, just vibes and better results. Turns out GitHub had the same instinct and decided to build it into the tool. Rubber Duck is a new experimental feature in Copilot CLI that pairs your primary coding agent with a reviewer from a completely different model family. For example, a Claude model orchestrates, and a GPT-model critiques... And crucially, it does this before anything gets built, not after you're already committed to a direction. Here's where it gets a bit uncomfortable though: Sonnet paired with Rubber Duck closed 74.7% of the performance gap between Sonnet and Opus on SWE-Bench Pro. Opus on a budget, basically. Great news, obviously, but if a second opinion from a different model family moves the needle that much, it's worth asking what that means for how we should be architecting agentic pipelines in the first place. Because the failure mode here isn't hallucination. It's compounding confidence... One assumption nobody questioned at step 2 quietly becoming a structural problem by step 47. Rubber Duck just asks the awkward questions early, before the damage is already baked in. Essentially a code reviewer who hasn't met you yet and has no reason to be nice. It turns out the duck needed a nemesis. 🐥🔪 I'm curious whether anyone else has stumbled onto patterns like this before the tools caught up? Drop them below, GitHub's clearly taking notes. 📋 Available now via /𝑒𝑥𝑝𝑒𝑟𝑖𝑚𝑒𝑛𝑡𝑎𝑙 in Copilot CLI. #GitHubCopilot #AIEngineering #AgenticAI

came across gitreverse.com lately and my first reaction was "wait... doesn't the README already do this?" turns out that's the right question to ask. the tool takes any public GitHub repo and generates a single prompt you can paste into Cursor or Claude Code to rebuild the project from scratch. cool concept. but yeah, if the README is solid, you're not getting much extra. where it actually clicks is when: - the repo has a terrible or no README (which is like... most repos) - you want to rebuild something to learn it, not just read about it - you're trying to feed context into an AI tool and don't want to manually copy 40 files a README is written for users. this output is written for AI tools. different format, different purpose. still think it's a clever idea even if the use case is narrow. the trick is just swapping "github" with "gitreverse" in any repo URL and it does the rest. not a game changer but genuinely useful if you learn by building. #DevTools #AITools #GitHub #LearnByBuilding #VibeCoding #PromptEngineering #CursorAI #ClaudeCode #OpenSource #CodeSmarter #SoftwareEngineering #100DaysOfCode #Programming #MachineLearning #ArtificialIntelligence #TechTwitter #Developers #WebDevelopment #BuildInPublic #AIAssistant

GitHub Copilot has crossed the line from autocomplete to coding agent. The early version helped you finish a line. The current version can open a pull request, write the tests, run them, review its own work, and ask for human input only when it hits a real decision point. Engineering leaders are reporting meaningful gains on well scoped work, often in the 30 to 55 percent range for net delivery speed. The gains concentrate on tasks that are clear, repetitive, and well specified. Ambiguous work still needs humans leading the thinking. The skill that matters most now is not clever coding. It is writing clear specifications, designing clean interfaces, and knowing when to trust the agent and when to step in. Senior engineers are more valuable than ever. Their judgment is what keeps AI generated code from quietly eroding a codebase. #GitHubCopilot #DeveloperProductivity #AIEngineering #AkashInnoTech

For the past 1 month I created atleast 6 different claude plugins and 5 different skills. Some of those got merged into amazing repos. Even if vibecoding did nothing else, it gave me opportunity to contribute to OpenSource. Repos I contributed to include some of the finest and most used skills-hubs Like #AwesomeCopilot #EverythingCLaudeCode #Framecraft - https://lnkd.in/gD27_A4T #CodeTour - https://lnkd.in/erPwXRX2 Here is a list of recent merges : CODE-TOUR ECOSYSTEM 📚 📚Generates interactive code walkthroughs automatically. Claude analyzes your codebase and creates guided tours, making onboarding and code understanding effortless. 💼 #ClaudeSkills : https://lnkd.in/eJMSENtE ⭐ 10,710 ⭐ #AwesomeCopilot :  https://lnkd.in/emsj9PUU  ⭐ 29,430 ⚡ #EverythingClaudeCode: https://lnkd.in/exxqcD9Q FRAMECRAFT ECOSYSTEM 🎥 🎥 Builds demo videos for code projects automatically. Showcases functionality visually without the heavy lifting of manual production. #ClaudeCodePluginsPlusSkills: https://lnkd.in/eAdem3ND ⭐ 1,913 🎬 #ClaudeSkills : https://lnkd.in/eJMSENtE ⭐ 10,710 Grateful for these opportunities to contribute to the developer tools ecosystem. 💻 #OpenSource #AI #DeveloperTools #GitHub #Contributions #SoftwareDevelopment #Coding #WebDevelopment #Programming #TechCommunity #CloudComputing #DevOps #Docker #Automation #ArtificialIntelligence #MachineLearning #Claude #GitHubContributor #CodeQuality #BestPractices #ContinuousIntegration #Deployment #Tools #Innovation #TechStack #Developer #SoftwareEngineer #FullStack #BackendDevelopment #FrontendDevelopment #WebTools #BrowserExtension #Productivity #CodeTour #VideoGeneration #Containerization #NetworkInfrastructure #DeveloperTools #OpenSourceCommunity #Community #Collaboration #GrowthMindset #LearningJourney #CareerDevelopment #TechLeadership #Innovation #Excellence #Passion #MakingAnImpact

Stack Overflow used to be the default “help button” for developers. Now, AI coding tools like GitHub Copilot are changing what “help” looks like. A quick story: I’ve watched teammates stop hunting for the perfect answer thread. Instead, they ask the editor for a snippet, get a working draft in seconds, then refine it. That shift is huge. Stack Overflow still matters—especially for edge cases, gotchas, and real debugging war stories. But AI is faster at the first 80%: syntax, boilerplate, translations between ideas and code. The real challenge is learning to ask better questions. If you don’t know what you’re actually trying to build, AI will happily generate code that looks right and behaves wrong. My take: the best developers won’t “choose sides.” They’ll use AI for speed, and Stack Overflow for depth and truth. Takeaway: Treat AI like a junior pair programmer—and treat Stack Overflow like a senior debugger. What’s changed for you in how you search for solutions? --- #DeveloperProductivity #AIEngineering #SoftwareEngineering #StackOverflow #GitHubCopilot

Cursor just hit 1,000,000 paying developers. That number took GitHub Copilot 3 years to reach. Cursor did it in 18 months. This is not a trend. This is a fundamental shift in how software gets built. Here is what changed in March 2026 alone: → Cursor launched parallel subagents your AI now runs multiple tasks at the same time, not one by one → BugBot was added for automated pull request code reviews → Arena Mode lets you run two AI models side by side on the same task and vote on which is better The result: developers using Cursor are shipping features in hours that used to take days. But here is the uncomfortable truth most people won't say out loud: The gap between developers who use AI tools and developers who don't is no longer about skill. It is about speed. And that gap is now measured in weeks, not months. If you are still writing boilerplate code by hand in 2026, you are not being careful. You are falling behind. Which AI coding tool do you use daily? Drop it in the comments. #Cursor #AICoding #VibeCoding #SoftwareDevelopment #AITools #DeveloperProductivity #GitHub #FutureOfWork #TechTrends2026

🔬 Tools I Use in My Work (Part 3): VS Code + GitHub Copilot Most people don’t realise how much time they lose until they stop switching between tools. That was me; Tabs everywhere. Searching for small fixes. Losing flow over tiny interruptions. So I changed one thing: 👉 I built my workflow around VS Code + GitHub Copilot Not to think for me. Not to replace judgment. But to remove the friction around the work. Whether you’re learning to code or already deep into R/Python, this setup meets you where you are — helping you understand faster and execute with less friction. Here’s what that changed: 📦 1. Everything in one place Code, terminal, version control, chat — no more bouncing between tools. ⚡ 2. Fewer interruptions Inline suggestions handle small fixes before they break my focus. 🧠 3. Faster understanding I can ask what code is doing inside the editor — no context switching. 🧭 4. Clearer starting point Planning workflows help structure tasks before writing anything. 🗂️ 5. Better task visibility Sessions and agents make it easier to track ongoing work. 🛠️ 6. Small gains that compound Faster edits. Smarter search. Less mental switching. ⚠️ What I’ve learned using it: • 🔁 It can over-suggest — you must stay intentional • 🤖 Agents are powerful, but not always necessary • ⏳ Setup takes time • 🧩 You still need to think — always 💡 The real impact Less friction. Fewer interruptions. More time thinking about the problem — not the tools. Curious — what’s your experience with VS Code + GitHub Copilot? #VSCode #GitHubCopilot #RStats #Python #DataAnalysis #ResearchWorkflow #OpenScience #Productivity #AIinResearch