take 2026-07-26 by Stephan Schielke / f59bf179 / llm.md

Fable 5 with omniglyph context compression

#ai #llm #context #compression

A picture is worth a thousand words

That old proverb was a lossy compression claim all along! A thousand words of prose collapse into one image your brain decodes in a glance. Large Language Model (LLM) can do the same trick, and the savings are absurd.

I have been squeezing agent context for a while and in my Compress Yourself Token Rich article I hinted at the possibilities of finding a better standard for lossless text compression. Although not lossless, omniglyph takes the next step and compresses whole context blocks into an image and feeds that to a vision-capable model:

@omniglyph example rendering of context as an image.

Why the image vector space packs more in

A block of API docs, config, and log records is expensive as text: every word, brace, and UUID becomes tokens. Render that same block as a compact image and a vision model reads it from a much denser latent space, because the image is projected into a handful of vision embeddings instead of hundreds of text tokens. Same information, a fraction of the token budget.

The screenshot below shows my compression pipeline and token savings using @omniglyph:

OmniRoute omniglyph dashboard showing a full tool-spec, config, and ledger-record page compressed to roughly 84 tokens — Omniglyph context compression logs from my OmniRoute gateway. A whole context page rendered as one image, ≈ 84 tokens.

Going from 150k tokens per request to just 50k really is not bad at all.

The library doing the heavy lifting

The text-to-image encoding leans on pxpipe, a tidy library for packing text into images (and back) so the pixels carry the payload. Pair it with a vision model on the read side and you have a working context-compression pipeline: text in, image on the wire, meaning out.

Same idea as caveman, one layer deeper: caveman shrinks the words, omniglyph shrinks the representation. Stack both and the machine-to-machine layers stop paying human-language prices.

Want these savings on your stack?

Omniglyph is not a toy. It is real token budget back in your pocket, faster inference, and more agents per subscription. If you want image-vector context compression wired into your own agents and gateways, that is exactly the kind of work I do.

Book my services and let us cut your context bill the same way. A picture really is worth a thousand tokens.