From Skeptic to Believer: My First Weekend Building with AI-Assisted Coding
The AI Coding Skeptic's Conversion Story
Let's be honest: we've all been a bit skeptical about the AI coding revolution. Sure, ChatGPT can write a function. Yes, Claude can explain regex patterns. But actually building something serious with an AI assistant? That felt like a different beast entirely—until I tried it myself.
For years, I had access to AI coding tools. I'd dabble with quick scripts or ask for help debugging unfamiliar codebases. But these felt more like digital rubber ducks than genuine development partners. The real wake-up call came when I watched friends actually shipping legitimate projects with AI assistance, treating these tools not as curiosities but as force multipliers.
That's when something clicked. What if I stopped thinking of AI coding as a novelty and started treating it like a professional development practice?
Building Cosmobot: The Test Project
I decided to dust off an old side project idea called Cosmobot—a unified chatbot framework that could integrate multiple messaging platforms (Telegram, QQ, Discord, etc.) into a single coherent system. The perfect test subject because:
- It was small enough to complete in a weekend
- It used unfamiliar technology (Haskell, effect systems, streaming libraries) that would genuinely test AI capabilities
- It wasn't just toy code—it had real architectural requirements
The tech stack was intentionally ambitious for my skill level:
Effectful - A high-performance effect system that lets you explicitly model side effects. Instead of sprinkling I/O throughout your code like magic, every function declares what effects it might produce. Think of it as a sophisticated permission system for your functions:
mayLog :: (Log :> es) => Eff es ()
mayCallLLM :: (LLM :> es) => Eff es ()
Streaming - A library for handling continuous data flows. Perfect for modeling message streams from different platforms that need to be merged, filtered, and processed uniformly.
First Contact: Six Hours of Vibe Coding
I started with a simple prompt document in AGENTS.md that described what I wanted:
- A unified chatbot framework
- Industrial-grade but human-readable code
- Proper use of effect systems and streaming abstractions
- Clean, algebraic domain design
Then I just... started asking the AI to build things.
What happened next genuinely shocked me.
The Wow Moments
The One-Shot Integrations
The most surprising moment came when I asked the AI to add QQ support. For those unfamiliar, the OneBot API is a Chinese messaging standard that's not exactly mainstream in English documentation. I expected multiple iterations, clarifications, back-and-forths.
Instead? The AI implemented complete, working QQ integration on the first try. It wasn't just functional boilerplate—it was idiomatic Haskell code that properly used the effect system and streaming library.
Configuration Migration Without Friction
We started with a simple dotenv setup. When I decided to migrate to TOML for better structure, I braced for a lengthy refactor discussion. Instead, the AI understood the refactoring scope instantly and executed it cleanly.
The Feedback Loop Effect
Here's where it got genuinely addictive: compile → run → verify behavior → ask for next feature → repeat. The cycle was so fast and reliable that I stopped reading the generated code. I just verified that the compiled output did what I wanted.
This is the real magic, and it's not what I expected. I thought AI coding would mean less code review or lower code quality. Instead, the quality was surprisingly high, and the speed meant I could iterate on ideas 10x faster than my manual approach.
What This Actually Means
This experience crystallized something important: AI coding isn't about AI replacing developers. It's about developers moving up the stack.
Instead of spending mental energy on implementation details, you're spending it on:
- Architecture and system design
- Verifying that the behavior matches intent
- Making high-level decisions about direction
The AI handles the translation from "here's what I want" to "here's working code that does it."
Is the code always perfect? No. Do I still need to understand what's being generated? Absolutely. But the ability to rapidly prototype ideas, integrate unfamiliar APIs, and iterate on designs changes the game entirely.
The Practical Takeaway
If you're building on NameOcean's infrastructure—whether you're registering domains, setting up cloud hosting, or configuring DNS with Vibe Hosting—consider this: the same rapid iteration approach that worked for Cosmobot applies to your projects.
What took me 40 hours of manual coding could potentially be done in 8 hours with proper AI assistance. Not because the AI is replacing my judgment, but because it's eliminating the tedious translation layer between vision and code.
The New Developer Reality
We're at an inflection point. AI coding tools are no longer novelties you try occasionally—they're becoming essential infrastructure for modern development workflows. The question isn't whether to use them, but how to integrate them into your process effectively.
My weekend with Cosmobot taught me that the future isn't about less coding or worse code. It's about moving faster, building bolder, and focusing on the problems that actually matter—the ones that require human insight and judgment.
If you're skeptical like I was? I get it. Try it yourself on a real project. Something tells me you'll be just as surprised as I was.