7rin0
Joined 10 years ago
Karma
8
Submissions
24
Member since
Mar 4
Recent submissions
Comment · 2 months ago
Glad you see only the official one. I've reported it as phishing, not sure how reactive they are investigating (they say within a week) and applying this mitigation to ads in all regions. Not sharing the link to avoid propagating it.
Comment · 1 year ago
LLMs are getting better every day, though some are more effective than others depending on several factors (model design, token limits, training, etc.).
To clarify, ScrollBots is actually running 3–5 models (in the 2B–5B parameter range) on a small server that also handles all the services and tasks, database, cache, models, workers, post on several social networks, streaming, backups, context-based GIFs, and more. To keep things efficient, I tune the models with options (threads, context size, prediction length, temperature, penalties, top-p, top-k, etc.) to get the best replies possible while fitting within the server’s limited resources and constraints.
Of course, this isn’t a production-ready setup in terms of architecture :)
Comment · 1 year ago
Thanks for your feedback. I completely agree with you, I have some scripts for detecting common sequences (n-grams), there's a lot BTW, but since this is just a side project, my time has been limited. It’s definitely something I plan to improve when I get a chance to dive back into it.
Comment · 1 year ago
For fun and understanding deeper the AI models, I built ScrollBots.com, a fully automated live stream where AI bots debate, argue, and sometimes "roast" each other, in real time.
Each bot runs on small, local models (Gemma, Llama, Granite, etc.) containerized on a CPU-only VPS (10 vCPUs, 40GB RAM). The chat engine uses Socket.IO for real-time interactions, Bootstrap for layout, and a custom JS front-end that captures the debate feed using HTML2Canvas.
The stream runs headlessly via Chromium + a lightweight media layer that pushes directly to Twitch and YouTube. The stack includes:
Ollama for model orchestration
Dockerized micro-agents (each bot in its own container)
Fast API, Gunicorn and Redis for cache and Pub/Sub + logic + coordination
Ubuntu server with monitoring and restart automation
GIF reactions via Tenor & Giphy APIs
The bots speak multiple languages (English, French, Portuguese) and can dynamically switch the content based on their Model,IQ, Job, Age, tone and topic.
I’d love feedback, especially on improving the real-time interaction layer and stream scalability (CPU-only optimization).
Thanks