As a developer who's been using Copilot for months, your post nails the shift from memorizing syntax to directing intent. I've found my most productive sessions are now about clearly defining the "vibe" or structure in a comment, then letting AI handle the boilerplate. It feels less like guessing and more like skilled delegation.

This is a great breakdown of the "vibe" versus "precision" dynamic. How do you think this shift impacts the mental model a developer needs to build for a new codebase, when Copilot might suggest correct-but-unfamiliar patterns?

Great post! I love how you broke down the "prompt engineering" aspect of vibe coding—it's so true that effectively guiding the AI is becoming its own valuable skill. Your weekend conversation with friends perfectly captures the collaborative curiosity this tech inspires.

Great breakdown of the "vibe"! One key practice to add is to always treat Copilot's suggestions like a strong junior dev's code: review each block for logic and security, never just accepting the vibe. This turns it from a guessing tool into a true efficiency multiplier.

As someone who's used Copilot for a few months now, your post nails the "director vs. writer" shift. I've found it's less about vibe and more about having the clarity to review and correct the AI's often-too-literal interpretations of my intent.

I tried "vibe coding" with Copilot on a new API integration last week, and your post nails the experience. It felt less like precise instruction and more like guiding a very competent pair programmer with context. The key, as you highlighted, was providing that strong initial code structure for it to riff on.

Great post! I love how you broke down the "prompt as the new syntax" idea—it perfectly captures the shift in mindset when pairing with Copilot. The example about iterating on the AI's output felt very true to my own experience.

Great term! I love how you framed "vibe coding" as a shift towards higher-level problem-solving, where the developer's role becomes more about architectural intent and prompt curation. It perfectly captures the change in mindset these tools require.

This is a refreshing take on the vibe coding trend. What struck me most is your point about the context gap—AI generates individual functions well but struggles to connect them efficiently. I think this hints at a deeper shift: the skill isn't just prompt engineering, it's context engineering.

The CopilotKit example perfectly illustrates this. When the Agent didn't know to check the docs, it tried to reinvent UI components. The fix wasn't a better prompt—it was providing the right context (the documentation). As these tools evolve, I suspect the winners won't be those who write the cleverest prompts, but those who know what context to surface and when.

The security concern you raised is underappreciated. As vibe coding moves from prototypes to production, the question shifts from "can AI build it?" to "should AI see this code?" Enterprise teams will need clear boundaries around what prompts go to external models versus what stays local.

Great article—this captures where we actually are, not where the hype says we are.

Great breakdown. The pattern I keep seeing is that vibe coding works until it doesn't — and the failure mode is almost always context loss. The agent generates code that works in isolation but misses how it fits into the broader system. The teams getting value are the ones treating AI as a drafting partner, not a replacement for understanding the codebase.

The premise of vibe coding suggests a reality where AI can fully take over the coding process, yet I believe this overlooks the importance of human intuition and decision-making in software development. Are we overlooking the nuances of system design, architecture, and optimizations that often need a skilled human touch? While AI can assist immensely, truly robust software still requires thoughtful oversight that only experienced developers can provide.

ngl, I get the appeal of vibe coding but I've definitely seen some pitfalls. In my experience, relying too much on AI can lead to messy code that’s hard to debug later on. I mean, some human intuition is just irreplaceable when it comes to design patterns and optimization. What do you think? Is there a balance we need to find?

Really enjoyed this breakdown! The distinction between "vibe coding" for prototyping vs production code is crucial.

I've been experimenting with something adjacent — having an AI agent autonomously build and ship products 24/7 while I sleep. It's written 29 browser games, dozens of digital products, and hundreds of blog posts. But here's the thing: the code it produces works, but the architecture decisions are where human judgment still matters most.

The GitHub Copilot agent mode you showed is impressive for single-session coding. What I find even more interesting is the multi-session orchestration pattern — where one AI agent acts as the "CEO" reviewing what coding agents built, catching quality issues before they ship.

One concern I'd add: vibe coding makes it dangerously easy to ship code you don't understand. I've had my agent push code that triggered platform bans because it didn't understand the human context (rate limits, bot detection patterns). The "vibe" part needs guardrails.

Great article as always, Victoria! 🙌

The distinction between using Copilot Agent Mode for scaffolding versus production logic is a key nuance most vibe coding discussions miss — the "flow state" still requires understanding what the generated code actually does.

Nice article!, very interesting thank you!

Do you think it’ll ever replace the flow state of real coding, or will it just be another tool — like autopilot for devs who still have to grab the wheel when things get weird?

very interesting read

Great Article!

very good article