I was right again

Summary

The creator explores the viral claim that feeding images instead of text to LLMs significantly improves efficiency and lowers costs. After performing extensive testing on his own tower defense game, he compares text-based context against image-based context. While the theory suggests massive performance gains, his results were mixed: images resulted in lower token usage but significantly slower processing times. He concludes that while the technology is promising for the future, current tools aren't fully optimized for this workflow, and developers should verify these claims with their own benchmarks rather than blindly following social media hype.

Chapters

1. 0:00Images Versus Text Performance
2. 0:44DeepSeek Research and Efficiency
3. 2:22Planet Scale Sponsor Message
4. 4:02Optical Compression Theory
5. 10:48Experimental Methodology

Topics covered

• Efficiency of images vs text
• DeepSeek research on token density

+4 more topics (full content when you follow the channel)

Transcript excerpt

You see that face? You know what that face is? That that is the smug face of somebody who was proven correct. And the best part, it's right after caught in 8K. You're even told by your friends you're wrong. So, you're probably thinking, "Okay, what what are we talking about? What are you even trying to say? What were you proven right about?" Well, my friends, it turns out after many hurt feelings and being told I was wrong, images might actually be better for models than text. I knew it. Don't make it do image recognition on >> No, no, you just do this. What are you that, dude. talking about? >> Hey, >> now this isn't just me saying this, of course. This is actually DeepSeek releasing a new paper that suggests that if you provide images, you could pull out 10 text token from a single image token with near 100% accuracy. In other words, a model's internal representation of an image is 10…