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deepmind.google·6d ago
AI Engineering
Hacker News·Jul 3·5 min read
60% Fable cost cut by converting code to images and having the model OCR it
TL;DR
Engineers faced high token costs when processing dense text inputs for AI models. The pxpipe tool converts bulky text into compact images, significantly reducing input token counts.
✦ Why It Matters
Engineers can significantly reduce costs by using pxpipe to convert dense text inputs into images.
Key Takeaways
- Implement pxpipe to convert text to images for cost savings.
- Monitor token counts to optimize AI model usage.
- Focus on dense content for maximum savings with pxpipe.
- Evaluate workload types to determine pxpipe's effectiveness.
Full Summary
AI models like Claude Code incur high costs due to the number of input tokens processed, especially with dense text such as code or JSON. To address this, pxpipe was developed as a local proxy that transforms large text inputs into compact PNG images, which are cheaper to process in terms of token count.
By rendering system prompts, tool documentation, and historical context as images, pxpipe reduces the token count from approximately 25,000 text tokens to about 2,700 image tokens. The savings vary based on the workload, but overall, users can expect a 59-70% reduction in costs, with even higher savings for compressed requests.
This approach allows engineers to optimize their requests and manage costs more effectively. The implications are significant for those working with token-dense content, as it enables more efficient use of AI resources.