AI WONDERLAND INOVATION
THE-ALL-IN-ONE AI PLATEFORM FOR BUILDING, BUILDING 3D.
the first image is what i got so far and the second image is what im going for, what the NPC should do, NavMesh handles movement/pathfinding, AI Perception handles sight/hearing, Smart Objects define things the NPC can interact with, and animation/Blueprint logic handles actions like walking, opening doors, using objects, etc. AI agents into controlled levels with navigation and interactable objects, then run/simulate them to verify the behavior.
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[ Real-World Photos ] │ ▼ (Photogrammetry / Gaussian Splatting) Meshroom │ ▼ (.OBJ / .PLY file) Blender ─── [ AI / Google Gemini / Figment ] (Generates logic/textures) │ ▼ (Optimized .GLB / .GLTF) PlayCanvas ─── (WebGPU Rendering Runtime) ───► [ Browser Display ]
no help no one wants to help and im afraid to ask so i dont and its a real struggle when im not even sure if im doing anything right and i almost just want to give everything away and stop coding but im not that kinda person that gives up
August 16, 2026 VANGAURD ENGINE ARCHITECT
Part 1: The Remote Infrastructure & Workspace Architecture I have engineered a modern, production-grade cloud development pipeline. Instead of relying on a local machine to compile heavy C++ engine code, I shifted the entire infrastructure to a scalable, containerized system.The Remote Host Engine (Coder.com): I deployed a centralized platform that acts as the backbone for MY workspace hosting. It abstracts the physical hardware layer, allowing you to access a dedicated development box from anywhere.
Orchestration & Storage (Kubernetes & PVCs):
The workspace is managed inside a Kubernetes cluster. When you boot the environment, it provisions an isolated Kubernetes Pod. To prevent loss of your code files between sessions, you configured a Kubernetes Persistent Volume Claim (PVC), which acts as a permanent virtual hard drive attached to that specific container.
The Development Blueprint (devcontainer.json): I built a structured configuration that dictates the exact environment specifications. Utilizing Envbuilder, this file automatically builds out your container, handles system packages, and mounts all required tools so that your workspace is operational the second you connect
The Compiler Toolchain: Inside this environment, you isolated CMake to generate cross-platform build files, alongside a native C++ compiler stack to manage everything cleanly without polluting local machine settings.
Hey Indie Hackers! Following up on my last post, I’ve been diving deep into how to structure the launch, pricing tiers, and monetization for the app while pushing through the final coding and debugging phase.
Instead of building blindly, I’ve mapped out the business model against the structural constraints of the Google Play Store—and how it maps to my tech stack. Here is how Part 2 is shaping up:
💻 The Tech Stack Under the Hood
To give you some context on the build, the repository breakdown looks like this:
TypeScript (73.5%): Handling the core application logic, UI components, state management, and the frontend wiring.
C++ (25.7%): Powering the high-performance native modules and heavy computational backend under the hood.
Other (0.8%): General configuration files and build scripts.
Debugging across the TypeScript-to-native C++ bridge while implementing secure payment triggers has been the ultimate final boss of this coding phase, but I am almost at the finish line.
# What Makes AetherGuard Unique
AetherGuard is a CLI-installed, local-first daemon that watches a repo, uses Gemini
to predict next-logical code, and audits/auto-fixes bugs and vulnerabilities in
real time. A few implementation details set it apart from typical "AI coding
assistant" side projects.
# 1. Auto-applies security fixes to live files — unattended
triggerAutoAudit() doesn't just flag critical vulnerabilities, it patches them
directly on disk when AUTO_FIX_ENABLED is on — but only after verifying the
exact old code still matches at that line:
that guard against stale or hallucinated line numbers is the detail that
separates "cool demo" from "safe to run unattended."
## 2. Debounced, watch-triggered self-healing loop
File change → chokidar fires → 2s debounce collapses rapid edits into one
audit → Gemini call → auto-fix → write back to disk, in a closed loop with no
human in it unless AUTO_FIX_ENABLED=false. Every applied fix is logged
(`autoFixLog`, capped at 200 entries) so there's an audit trail of what the AI
changed and why.
3. Model failover baked into every AI call
callGeminiWithFailover tries gemini-2.5-flash, then falls back to
gemini-2.0-flash-lite, with retry-on-transient-error logic (503, 429,
"overloaded", etc.). Most hobby AI tools call the model once and just fail;
this treats API flakiness as an expected condition.
## 4. BYOK as a first-class runtime feature, not just an env var
/api/set-key lets a user set their Gemini key at runtime through the UI — no
restart needed — and /api/key-status reports whether the key came from env
or was set live. That's built for handing the tool to other people, not just
running it yourself.
# 5. Path-traversal guard on live file read/write
readLiveFile / writeLiveFile check fullPath.startsWith(TARGET_DIR) before
touching disk. Since this tool can read and write arbitrary files in a
directory you point it at, that check is load-bearing, not decorative.
## 6. The prediction UI sells "pattern learning" at the UX level
CodeEditor.tsx badges predictions as "Pattern Matches Found" and offers
ranked alternatives (not just one completion), with an "Auto-Inject
Completion" action that splices into the exact cursor line — reinforcing the
repo-pattern-learning pitch in the UI, not just in the system prompt.
# Honest caveat: pattern learning is re-derived, not persisted
The "pattern learning" is prompt-driven — Gemini reads the full repo content
on every audit call and is instructed to detect patterns fresh each time. It's
not a trained or persisted model. In practice, "learns your patterns" means
"re-derives them from full repo context every audit," which is simpler than it
sounds and currently has no long-term memory of patterns between sessions
unless the patterns output is being stored somewhere outside server.ts.
I'm sharing it because this is my actual journey, not because I want to pretend I wrote every sentence perfectly. 😅
I wanted to build an AI that could overlap my browser and help me while I was working. I couldn't find exactly what I wanted, and I couldn't figure out how to build it through Google AI Studio, so I started studying. I read a lot of books, researched, experimented, broke things, fixed things, and kept building.Somewhere along the way, I ended up building about four completely different AI systems.
One of them... I'll probably never build again.
It was honestly one of the most insane and scary things I've ever coded.
I built a self-replicating, self-learning AI. Thankfully, it wasn't some movie-style AI that could take over everything. The worst thing it could really do was replicate itself until it consumed the resources of the machine and potentially crashed the server or crashed itself.
I was originally trying to figure out how to make it secure, but after realizing how quickly things could get out of control, I immediately removed it from my server and moved it onto a sandboxed computer that never connects to the internet.That was probably my first truly crazy experimentThen came the second one.I wanted a 3D engine and editor for my website, but I couldn't decide which engine I wanted to use. So instead of choosing one, I apparently decided to build something completely ridiculous.I built a spatial environment that could spin up multiple 3D engines and editors side-by-side so I could compare them.I ended up experimenting with things like Unity, Unreal Engine 5, PlayCanvas, WebGL, Godot, CryEngine, O3DE, Stride, Flax, Bevy, Armory3D, Wonderland Engine, Babylon.js, and Three.js.The idea was basically:Why choose an engine when I can put them all next to each other and see which one I actually like?So I built a system that could spin these environments up, including Kubernetes pods, and let me compare them spatially.And the funny part is, I didn't even build it because I was planning to sell it.I built it because I wanted to figure out which 3D engine I should use in my own website.That project took me roughly a month of learning and building at the same time, and then another month of fighting with it until I finally got it working properly.At the same time, I was also building my own WonderBuild system, which is basically my attempt at creating something similar to WordPress but with my own architecture and AI-powered tools.Those two months were brutal.I was working on a tablet, trying to build software that was way beyond anything I had ever attempted before. Eventually that tablet met its fate. Let's just say I didn't exactly retire it peacefully.I moved over to a Chromebook/Linux setup, and honestly, at first I was completely lost. I had been doing almost everything the hard way because I didn't know there were easier ways to approach a lot of the problems I was trying to solve.But I kept learning.A huge part of what made this possible was GitHub and all the open-source projects, documentation, examples, and people who were willing to share their work.I've also spent a lot of time looking at existing platforms and thinking about what I would change.Sometimes it feels like companies charge too much, don't give people enough time or resources, or build billing systems that make things harder than they need to be.That's one of the reasons my long-term dream is for this project to eventually become open source and free for people to use.I know that's probably not the easiest business decision.But honestly, that's the dream.I didn't start building all of this because I had some giant company behind me or because I knew exactly what I was doing.I started because I wanted one tool.Then I couldn't find it.So I started learning how to build it myself.And somewhere along the way, one tool turned into several AI systems, a 3D spatial environment, a website builder, an entire ecosystem, and a journey that I never expected when I wrote that first line of code.
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Another thing I ended up building was an AI Confession and Reward System.
The idea came from wanting AI to be more transparent when something goes wrong.
Instead of the AI just failing and giving the user some generic error message, I wanted the system to show:
What happened.
How it happened.
Where it happened.
What caused it.
And what the AI did about it.
Basically, if the AI fucked something up, I wanted the user to be able to actually see the failure instead of having it disappear behind the scenes.We also added a reward system around successful behavior and task completion, so the AI could be evaluated based on whether it actually did what it was supposed to do—not just whether it produced an answer.That idea became another part of the bigger experiment: figuring out how to make AI systems more transparent, accountable, and useful.Looking back, that's really what a lot of this journey has been about.I wasn't just trying to make AI do more.iwas trying to figure out how AI should behave when it does something right, and especially what it should do when it gets something wrong.
A quick note: I wrote the original version of this story myself. I used ChatGPT to help me spell-check it, clean up the grammar, organize some of the paragraphs, and make it easier to follow. The experiences, projects, ideas, and story are mine — I just had some AI help cleaning up how I told it.
FINISHED WIRING SO MANY NODES AND NOW MOVING TO THE TEMPLATE LIBRARY IM BURING OUT FAST
i have been building this solo and ive completed alot in 6 months and ive just started coding with no prier knowledge and no schooling i bruteforce my way to learing everything from trial and eror and ive made alot of mistakes but learned alot and did it all at first free and got it deployed and i recently put 1k$ of my own money into it and i been trying to find ppl that are motivated and looking to code to help me ad this and finish building this ecosystem
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this is first time ive shown anyone my website and i just been working on it 7 days a week 10 hours a day, actually started this journey on a tablet, with no formal coding background or schooling. I ended up rooting that tablet and bricking it three different times and somehow managed to bring it back each time. 😂A lot of this project has been that kind of journey. I've been learning everything through trial and error, breaking things, fixing them, starting over, 2 times lol i think back to once i put everything in one file omg that was a terrible idea and figuring things out as I go. Doing all of this completely solo for the first time has honestly been one of the hardest things I've ever taken on.There were plenty of times when I wasn't sure I could get it working, but I kept pushing forward. Six months later, I've gone from barely knowing how to code to actually building and deploying my own ecosystem and putting my own money into making it real.I'm still learning every day, and there's a long way to go, but that's also why I'm looking for people who want to build something together. I don't just want people to see what I've built i'd like to find people who can help take it somewhere much bigger.
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Honestly, this is really inspiring. Building and deploying a complete product in just 6 months, especially starting with no coding background or formal schooling, takes an incredible amount of determination. Trial and error, mistakes, and figuring things out on your own are all part of the journey. Putting $1K of your own money into the project shows that you're genuinely committed to making it work. Respect for what you've built so far, and I hope you find motivated people who share your vision and can help you take the ecosystem to the next level. Keep going!
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The biggest signal here isn't how much has been built — it's how much of the product's eventual shape still seems to be forming around the building process itself.
That makes the current stage difficult to evaluate from the outside, especially with the project still described as an ecosystem rather than a clearly defined product.
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