I want to be upfront - I'm a solo founder, not a senior engineer. My background is business, not computer science, though I do have a computing degree. I taught myself to code this from scratch over about 3 months and I want to be clear this isn't vibe coded. Every API integration, every webhook, every database rule was researched, tested and implemented properly. I did courses in between commits and generally know my code insight out.
Almost 700 commits later and over a 1000 hours put into this and here's what's actually running under the hood.
I'm running 18 separate API integrations simultaneously:
- OpenAI (GPT-5 Nano, GPT-5.2, GPT-5.2 Pro, DALL-E 3, WebRTC Realtime, Assistants API with vector store)
- Anthropic (Claude 3.5 Sonnet with prompt caching)-
- Google (Gemini Flash, Gemini 3.1 Pro)
- xAI (Grok 4)
- DeepSeek (V3 and R1)
- Luma AI (Dream Machine video generation)
- Kling (1.6, 2.6 and 3.0 UHD)
- Veo 3.1
- ElevenLabs (music generation with custom lyrics, voiceover, voice tuner)
- Flux (pixel-perfect image editing)
- Banana Pro (Nano image generation)
- Meshy (3D model generation)
- Stripe (subscription billing with webhooks)
- Firebase (auth, Firestore, security rules, IAM)
- Sentry (error tracking)
- IPify (IP rate limiting on signup)
The architecture if anyone is interested:
- Deployed on Vercel with serverless API routes
- Firebase Firestore as the primary database with custom security rules
- OpenAI Assistants API with vector store for persistent memory - every message is stored and queryable across any model switch mid-conversation. Even on log out, new device, new chat the memory will be there.
- A credit economy system where every generation type has a cost per token or per request, deducted atomically via Firestore transactions
- Dual payment architecture - Stripe for web and Android, Apple IAP via Cdv Purchase plugin for iOS, both syncing to the same Cloud Run backend
- Custom webhook handlers for Stripe subscription lifecycle events
- Server-sent events for streaming responses across all text models
- WebRTC session management for real-time voice
What it actually does:
- Switch between GPT-5.2, Grok 4, Claude 3.5, Gemini 3.1 Pro mid-conversation with full memory continuity
- Generate HD video via Luma Dream Machine, Kling 1.6, 2.6 and Kling 3.0 UHD with up to 15 seconds of cinematic video and audio
- Cinema grade video via Veo 3.1 with audio
- Full music studio - custom lyrics or have AI generate them for you, pick a genre, get a downloadable MP3 via ElevenLabs
- Real-time 2-way voice conversation via OpenAI WebRTC with animated orb UI
- 2-way podcast mode - have a conversation with AI and export it as a downloadable MP3
- Flux pixel-perfect image editing - change backgrounds, swap objects, relight scenes with plain English
- Vision to Code - upload a screenshot, get live editable code on a split canvas
- Web Architect and Game Engine - describe an app or game, watch it build on an interactive canvas
- 3D model studio powered by Meshy - opens inside the chat window, generates downloadable STL files ready for Unity, Unreal or 3D printing
- Knowledge base - upload documents, build a searchable vector store, query it across any model and device either as a single user or simultaneously on other devices
- Custom memory management - tell it in plain English to remember something specific, overwrite old memories with new information or forget something entirely. No settings menus, no manual tagging, just talk to it like a person and it stores, updates or removes that memory and carries it forward across every model and every future session
-50+ purpose built tools across writing, coding, business analysis and content creation
- 20+ live interactive wallpapers that react to cursor movement, canvas & video based with also custom themes too to change the look of the whole interface
- Runs on web, iOS, Android and Mac desktop via Capacitor-
- 26 languages with RTL support including menu titles etc
Where I'm genuinely unsure..
I keep adding things. The 3D modelling studio was a "why not" decision at 2am that turned into a proper implementation. Veo 3.1 and Kling 3.0 UHD was a recent addition which generates up to 15 seconds of cinematic video with sound - that's genuinely longer and higher quality than most dedicated video generation tools offer as a standalone product.
The memory system has also evolved beyond just storing conversation history. You can tell it in plain English to remember something specific or forget what it knows and replace it with something new and it will carry that forward across every model and every future session. No menus. No settings. Just talk to it.
At what point does adding more actually hurt the product? I genuinely don't know. But then I look at the alternative - users juggling 6 different subscriptions across ChatGPT, Claude, Midjourney, Suno, Runway and ElevenLabs - and I think there's a genuine case for a unified workspace.
Am I getting carried away? This is what I'm building next - and this is actually why I'm posting:
The next feature I'm planning is a proactive memory system. Not reactive like everything else - genuinely proactive.
The idea is simple in concept but the implementation is interesting. You tell it in plain English "remind me tomorrow to email Jane at 9am." It picks up the intent, extracts the key details, creates a timestamped entry in Firebase, and a custom script runs on every login that checks for due reminders. When the time comes it uses the existing WebRTC voice system to actually speak the reminder back to you - not a push notification, not a banner, a spoken reminder from the AI you've already been talking to all day.
Users will have full control - turn the proactive system on or off entirely, dismiss a reminder or snooze it if they're mid-conversation. The AI learns how you respond to reminders over time too.
This is the feature I'm most uncertain about. Everything else I've described is already built, live and working across web, iOS, Android and Mac desktop right now. The proactive memory is next.
But honestly this whole post is because I've reached a point where I don't know if I'm building a genuine solution to a problem people didn't know they had - or whether I'm building something so comprehensive that it becomes its own problem. A platform so capable that it's overwhelming rather than useful.
Would love to hear your feedback because when this whole project started out it was meant to be a comprehensive chatbot thats evolved into a fully fledged platform
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