Curious what made you think the backend uses LLMs for content generation?
To clarify:
1. transcription is local VOSK speech-to-text via WebSocket
2. live transcript post-processing has optional Gemini Flash-lite turned on which tries to fix obvious transcription mistakes, nothing else. The real fix here is more accurate transcriber.
3. backend: TypeGraphQL + MongoDB + Redis
The anti-AI stance isn't "zero AI anywhere", it's about requiring human input.
AI-generated audio is either too bad or too perfect. Real recorded voice has human imperfections.
I'm working on https://X11.Social, a voice-first content creation tool for X.
The initial idea was "call to tweet", the ability to compose posts on the go by having a natural conversation with an AI assistant over a simple phone call. This is useful for turning thoughts from a walk or drive into a polished "brain dump" post, or for engaging with user lists without being at a computer.
It has since evolved into a broader system:
Chrome Extension: A context-aware assistant that lives in the browser. It has a Quake-style console (activated by opt+space) for quick chat and can analyze the content of any page you're on (e.g., YouTube transcripts, articles, other tweets) to help you create relevant content.
Engagement Predictor: A feature that scores tweet drafts in real-time to predict their potential for engagement. It's built on a model trained on my own dataset pulled from the X API and other public dataset from Kaggle[0].
Scheduled AI Calls: The system can call you on a predefined schedule to proactively brainstorm content ideas.
Here is the tech stack:
- Frontend: React, Tailwind, shadcn/ui
- Auth: X OAuth
- Payments: Stripe Subscriptions
- Voice AI: ElevenLabs Conversational AI, Twilio
- Engagement Predictor ML: Python, scikit-learn, XGBoost on a data pipeline from X API v2 and a base dataset from Kaggle.
- Chrome Extension: Same as Frontend and Chrome Extensions API
- Blog: Jekyll
- Infrastructure: Deployed on AWS Fargate using AWS Copilot for orchestration (ECS).
I'm building solo and just got the first trial user after 87 days of building in public. It's a long road but the feedback so far is encouraging.
I found a quick 15 page introduction to Scala and took me about an hour to digest it (of course I didn't dive in very deeply). It gave me a sufficient knowledge to understand the article about monads.
"When an invalid program finally crashes (or enters an infinite loop, or goes to sleep forever), what you're left with is basically the binary snapshot of its state (a common name for it is a "core dump"). You have to make sense of it in order to find the bug."
Reading > listening for consumption.
Talk to create, read to consume.