Patrick God’s Post

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

Been using Copilot heavily at work recently, and noticed something interesting… Small observation while using GitHub Copilot inside VS Code / IntelliJ. The biggest risk isn’t wrong code. It’s how easily we accept code without fully processing it. You’re in flow → Copilot suggests → you hit Tab And keep moving. But later: → The logic doesn’t fully match your system → It introduces a silent assumption → Or it breaks consistency with existing code Also noticed this in larger files: Suggestions start drifting away from earlier patterns Even within the same class. The issue isn’t intelligence. It’s a subtle UX gap: Speed is optimized. Awareness isn’t. Feels like a small fix could make a big difference: → Show a quick diff or highlight what’s being introduced → Surface assumptions in the suggestion → Add a lightweight “what this does” preview before acceptance Because in AI-assisted coding: Speed helps you write faster But awareness helps you write better And over time, that’s what really matters. #AI #GitHubCopilot #DeveloperExperience #ProductThinking #SoftwareEngineering

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?

Grok Build and Grok CLI are scheduled to launch next week, and a new Grok Code model may also be coming soon. Fun fact: AI-powered coding assistants, like GitHub Copilot, helped demonstrate how models can suggest code and speed up routine development tasks—paving the way for more integrated developer tools. #DeveloperTools #AI #CodeGeneration #SoftwareEngineering

I opened VS Code today, typed nothing… and somehow still managed to “spend tokens.” For a second, I thought Copilot had developed trust issues. Turns out—it’s just very prepared. 😄 I went down a rabbit hole to understand what GitHub Copilot is actually doing behind the scenes, and this completely changed how I think about AI-assisted coding: Fresh sessions aren’t really “empty” Even before you type a single character, Copilot is already loading context like: System prompt + tool definitions Your workspace file structure Instruction files (.instructions.md) User memory, skills, and agent registries So yeah… your “blank editor” isn’t blank at all. Here’s the interesting part 👇 You actually have control over how much gets loaded. Tweak the applyTo frontmatter to limit which instruction files auto-load Or convert them into .prompt.md files so they only activate when you call them Result? Less unnecessary context → fewer tokens used → faster, more focused suggestions. It’s a tiny config change, but in a large enterprise codebase, this can make a serious difference in performance and cost. Sometimes productivity isn’t about adding more tools— it’s about understanding what your tools are already doing. #GitHubCopilot #AI #DeveloperProductivity #VSCode #CodingTips #SoftwareEngineering

In a recent Rubber Duck Thursday stream, Cassidy Williams shares how an emoji list generator was created using the GitHub Copilot CLI. I found it interesting that this tool not only enhances creativity but also showcases the potential of AI in streamlining coding processes. What are your thoughts on the role of AI in improving developer productivity?

Current AI landscape for software development is changing extremely fast these days, I'm struggling on choosing a stable and scalable tool/model/IDE/etc. Right now I'm using JetBrains IDE's with GitHub copilot plugin (with a PRO account) and I'm pretty happy with it :D ... but I feel that lacking a true agentic workflow (agents+skills) is slowing me (and my team) down ... I am considering switching to Claude while keep using JetBrains IDEs, but having a paid GitHub sub, I want to squeeze it to the last drop :P Said that, I need a reliable resource (course, book, podcast, tutorial, documentation, etc) to learn how to implement this stack (JetBrains + Copilot) using agents and skills... Any suggestions? #AI #SoftwareDevelopment #DeveloperTools #GitHubCopilot #JetBrains #AIWorkflow #AgenticAI #Productivity #DevExperience #Programming

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.

A few months ago, I started using GitHub Copilot, and it’s made writing code feel smoother and faster. It speeds up boilerplate code and offers smarter suggestions for functions. I usually pair it with VSCode and Playwright to test and ship features more quickly. One thing I’ve learned: don’t accept every suggestion without a careful check. Balancing AI help with manual reviews keeps my code reliable and clean. If you want to speed up your workflow but stay in control, try Copilot alongside tools like Cypress or Claude Code. How are you bringing AI into your coding routine? 🚀 #GitHubCopilot #AI #CodingLife #SoftwareEngineering #AIDrivenDevelopment

GitHub Copilot CLI Just Unleashed! How Does It Stack Up Against Claude? The news is out: GitHub Copilot CLI is now generally available! This is HUGE for streamlining developer workflows. As we embrace these AI-powered coding assistants, it's natural to wonder how different models like Claude integrate and compare. While Copilot CLI focuses on command-line mastery, Claude shines in its ability to understand context and generate complex, nuanced text. Think of Claude as your brainstorming partner for documentation, explaining code concepts, or even helping craft better commit messages. Could Claude's strength in understanding natural language complement Copilot CLI's efficiency in command-line tasks? Perhaps an integration in the future? What are your initial thoughts on Copilot CLI? How do you see tools like Claude fitting into the evolving landscape of AI-assisted development? Share your perspectives below! 👇 #GitHubCopilot #ClaudeAI #AI #ArtificialIntelligence #DeveloperTools #Coding #Programming #CLI #Innovation #Tech Read Full Article Here: https://lnkd.in/gMnd5ds5