
local-llm
A comprehensive guide for building and configuring a high-end local machine to run state-of-the-art LLMs, with detailed hardware choices, BIOS tuning, and Docker-based model serving.
The Floor is an MVP AI debate opponent that runs without paid APIs: you pick a category, spin for a topic, choose a stance, research for 5 minutes while Groq and DuckDuckGo build a research brief in parallel, then debate live by voice with a talking animated SVG avatar.
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2026-07-30
It shows how to build a fairly complete voice-based AI debate experience using only a free Groq API key, a no-key DuckDuckGo search library, browser built-in speech APIs, and a custom SVG avatar, with a simple FastAPI backend and a no-build static frontend.
https://ai-debater-seven.vercel.app/ check this out
A local, no-paid-API-required AI debate opponent: pick a category, spin for a topic, take a stance, research for 5 minutes while the AI researches in parallel, then debate live by voice with a talking avatar.
duckduckgo-search — no key required.speechSynthesis. No key.backend/, create a file called .env:GROQ_API_KEY=your_key_here
(Optional: set GROQ_MODEL=llama-3.3-70b-versatile or another Groq-hosted
model — see https://console.groq.com/docs/models for current free models.)
cd backend
python3 -m venv venv
source venv/bin/activate # Windows: venv\Scripts\activate
pip install -r requirements.txt
uvicorn main:app --reload --port 8000
Check it's alive: open http://localhost:8000/api/health — you should see
"groq_key_set": true.
The frontend is plain HTML/JS, no build step. Simplest option:
cd frontend
python3 -m http.server 5500
Then open http://localhost:5500 in Chrome (Web Speech API support is best there). Allow camera access when prompted.
backend/topics.py (edit freely to add your own).build_research_brief() — DuckDuckGo search for both sides of the
topic, summarized by Groq into a compact brief (key arguments, likely
opponent points, pre-drafted rebuttals). This brief is what the AI draws
on live, so it isn't reasoning from scratch mid-debate. Meanwhile you get
a notes panel to do your own research in.Similar projects matched by category, topics, and programming language.

A comprehensive guide for building and configuring a high-end local machine to run state-of-the-art LLMs, with detailed hardware choices, BIOS tuning, and Docker-based model serving.
A 28.9 million parameter language model runs on an $8 ESP32-S3 microcontroller entirely on-device, generating simple stories at about 9.5 tokens per second.
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