Codacus
@CodacusStop paying for AI you don't need. Most YouTube AI content is sponsored takes on tools you'll be subscribing to forever. This channel goes the other way. Codacus is about running real AI on hardware you already own — small models, self-hosted stacks, the homelab tools nobody's building thumbnails about. Every demo is on a 5-year-old MacBook or a $150 used GPU, because that's what most people actually have. New video weekly. Sometimes twice. Subscribe if "free and local" sounds like a feature, not a compromise.
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- Subscribers
- 42.7KTop 62%
- Avg views
- 60.1KTop 25%
- Median views
- 20KTop 60%
- Views per subscriber
- 128%Top 9%
- Engagement rate
- 3.0%Top 66%
- Past sponsors
- 12+
Pythagoras
- Last published
- 19d ago
- Est. Rate $
- Reveal
- Audience Score
- 83by Scout
- Sponsor Score
- 65by Scout
Videos
- Llama-server26:49Can You Run Any LLM in Jev Mode Using llama.cpp?75.3K views · 12d ago
- Coderabbit25:15How I Built an End-to-End Local Voice Agent, and Made It Fast65.2K views · 19d ago
- Opus 525:12Is Frontier Class Local AI Finally Practical?177K views · 27d ago
- Coderabbit23:02I Built a 24/7 Local AI Agent for My Team, With Native Browser Control20K views · 1mo ago
- Ryzen 5600x20:32How Fast Can One RTX 3060 Actually Run 35B (llama.cpp enhancement)?45K views · 1mo ago
- Apple Silicon19:24I Split Qwen 3.8 27B Model Across Mac and Nvidia PC (llama.cpp RPC Guide)30.5K views · 1mo ago
- Nvidia23:47Colibrì vs llama.cpp: Running DeepSeek V4 284B on CPU107K views · 1mo ago
- Anthropic14:09Did I Build the Best Local AI Agent Harness? (diched Hermes)22.3K views · 2mo ago
Past Sponsors
Click a sponsor for its videos · Purple = dedicated video · Orange = integrated read · Grey = mentioned, not a paid sponsor
Rate Estimates
by ScoutEstimated from audience performance, not published by Codacus. A creator who claims this profile replaces them with their real rate card.
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Estimated from the people who engage with Codacus. One reveal opens all three for 3 credits, charged only if there is data.
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Sponsorships observed in Babbl’s corpus of paid YouTube placements. Counts are a lower bound. Data as of 2026-10-03.