As we all learn and adapt in this fast-moving tech landscape, one truth has become unavoidable: Artificial Intelligence is now an everyday reality.
AI itself isn’t brand new. The core concepts and theoretical foundations have been around since I was back in secondary school. However, the modern explosion of Large Language Models (LLMs)—kickstarted by OpenAI—opened a completely new window for civilization. It reshaped our paradigm and fundamentally altered how developers think, design, and write software.
As a programmer, system analyst, and system architect, I jumped in early. I may not be an AI researcher training base models from scratch, but as a practitioner and power user, my workflow has undergone a massive transformation.
Here is the story of how I went from manually copy-pasting code snippets to "vibe coding" entire spatial desktop environments.
The Early Days: The Copy-Paste Era
Like many developers, my journey started with the OpenAI ChatGPT web interface.
Back then, dedicated coding agents didn't really exist yet. There was no deep IDE integration, and I hadn't hooked up tools like GitHub Copilot into my day-to-day workflow. My interaction with AI was basic and entirely manual:
Describe a bug or ask for a function.
Copy the code block.
Paste it into my editor.
Tweak it when it inevitably broke.
It was useful for quick lookups, but it wasn't a structural game-changer just yet.
Going Local and the Mid-2025 Vibe Coding Shift
Equipped with an AMD Ryzen AI+ Max 395 machine, I began experimenting with local models. But before local AI could fully mature for my daily workflow, gemini-cli arrived, and my real experience with agentic vibe coding officially began.
Working with gemini-cli in mid-2025 was a mix of awe and mild frustration. Early models and agents frequently suffered from context drift, wandering away from the architectural boundaries I had carefully established.
BUT it was still miles ahead of writing code line-by-line.
Projects that previously took me 1 to 2 months were suddenly getting wrapped up in 1 week with significantly higher overall quality.
My Role: I shifted my focus almost entirely to high-level requirements, system architecture, and code review.
The Agent’s Role: The agent filled in all the tedious gaps I usually skipped when trying to push out a quick prototype—plugging memory leaks, polishing UI components, and wrangling complex CSS layouts.
The Quotas, aether.ing, and the Quest for Local Autonomy
As I began juggling 2 to 3 active projects at a time, Google was incredibly generous with gemini-cli. But good things in tech rarely stay unchanged for long. Google eventually transitioned to Antigravity 2.0, introducing strict quota limits even for Workspace users on Pro plans.
Meanwhile, the AI arms race between OpenAI, Anthropic, and Google accelerated wildly. Model after model hit the market. While everyone was raving about flagship cloud models like Opus, I tried to stay optimistic about local AI to avoid accumulating monthly SaaS subscriptions.
I even started building my own custom agent platform, aether.ing, aiming to empower my workflow using local LLMs hosted on my home server.
However, as the generous gemini-cli tier ended and Antigravity 2.0 proved too restrictive for heavy architectural sessions, I found local LLMs struggling under the weight of complex agentic demands. I experimented extensively with HuggingFace community fine-tunes of Qwen 3.5 and 3.6, but running local 27B or 120B parameter models felt like bringing a knife to a gunfight when compared to the massive remote models being deployed. When Alibaba released Qwen 3.8 at 2.4 Trillion parameters and Kimi launched K3 at 2.8 Trillion parameters, I had to admit reality: my local hardware couldn't keep pace for deep multi-file refactoring. I postponed heavy vibe coding objective on aether.ing, paring its role down to creative co-writing.
The Turning Point: Chipping In for Fable 5 & Opus 5
After hearing endless chatter about the architectural reasoning capabilities of Anthropic's latest models, the release of Fable 5 (with a trial for Pro subscribers) pushed me over the edge. I decided to chip in $20 to test the waters.
The results were staggering.
Case 1: jWebDBAdmin
I had an old, unpolished internal tool named jWebDBAdmin. It had been used by me and a few colleagues for a long time, carrying hundreds (if not thousands) of lines of legacy code. I wanted to add new features, but I was too tired to sink hours into manual refactoring.
I fed jWebDBAdmin to Fable/Opus with a wishlist of major new requirements. In just two 5-hour sessions, the model completely rebuilt the project from the ground up—adding auto-complete, sleek UI polish, and intelligent AI-assisted query suggestions.
Case 2: Applikasi KTG & Vibrefy
- Applikasi KTG: Complex feature sets that used to take 1–2 weeks of manual vibe coding were added and verified in just 2 days.
- Vibrefy: A personal project I had sitting in a drawer for months was completely polished, structured, and uploaded to GitHub directly with the help of the agent.
The Hidden Perk: Automated Git Plumbing
As a developer who never enjoyed dealing with the tedious overhead of Git branching, merge conflicts, and commit management, using agents like Claude Code took that pain away entirely. The agent handles all the Git plumbing while I focus on design decisions.
Going Wild: From Web Apps to 3D Spatial Computing
Once you realize you can build outside your traditional domain knowledge, things get exciting fast.
The Android Media Problem (Vibrefy Mobile)
I had media files encoded in AC3 audio that couldn't play natively inside Android's Google Chrome. I wanted a custom player, so I decided to build a dedicated Android client for Vibrefy.
The catch? I had zero prior experience in native Android development.
With the AI agent handling the framework specifics, we pushed far beyond a standard player. Why stop at standard 2D video? We transformed it into a full 3D and VR media player with support for Nreal / Xreal Air AR glasses—something impossible inside a standard web browser.
The Ultrawide Debugging Need (HoloFrame)
Using the Xreal Air glasses with a Mac presented another roadblock. While Xreal offers Nebula for Mac with virtual screens, the resolution limits made reading long, dense log files during debugging nearly impossible. I needed a single wide canvas where I could read an entire log line from left to right without truncation.
My goal: A virtual desktop canvas running at 7680 × 2160 resolution.
That project became HoloFrame, giving me the exact high-density virtual display real estate I needed for heavy debugging sessions.
The Ultimate Challenge: Spatiand (A 3D Desktop from Scratch)
Eventually, relying on existing host operating systems and window-capture software felt too limiting. Most AR/VR desktop setups use window-capture: rendering an app to a 2D desktop buffer, capturing the pixels, and re-projecting the frame into 3D space. It is inefficient and clunky.
I wanted true spatial computing: an lightweight OS environment where applications directly draw inside 3D space.
After brainstorming hardware options—and guided by ChatGPT's memory of my previous setups—I settled on my target rig: a mobile Mini-PC setup combined with my SteamDeck (running SteamOS) and Xreal Air glasses.
Then came the craziest prompt I've ever thrown at Opus 5: Build a 3D desktop environment from scratch with minimal external dependencies, targeting SteamOS and Xreal Air.
This wasn't a project solved in 2 days, or even a week. I’ve had to fight through strict 5-hour messaging limits and weekly quota caps while balancing side projects. But step by step, byte by byte, it's coming together.
Conclusion: Focus on Architecture, Let the Vibe Flow
Vibe coding isn't about letting an AI blindly write code while you sit back and sleep. It’s about elevating your role as a software builder.
By letting high-tier LLM agents handle syntax, framework boilerplate, UI styling, edge-case memory safety, and Git operations, I am free to spend 90% of my time doing what I love: system architecture, creative hardware integration, and solving real user problems.
Stay tuned—Spatiand will be landing on GitHub soon!
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