(11-02-2026, 08:09 PM)Typpi wrote: You are not allowed to view links. Register or Login to view.

(11-02-2026, 07:48 PM)KamCode wrote: You are not allowed to view links. Register or Login to view.

(11-02-2026, 07:43 PM)Typpi wrote: You are not allowed to view links. Register or Login to view.

(11-02-2026, 05:12 PM)KamCode wrote: You are not allowed to view links. Register or Login to view.Please read the paper and the github i

The GitHub is 99% AI slop.

Can you just summarize your points and research here?

The GitHub is extremely verbose with little substance.

yeah the github readme is ai generated, alot of this project aswell as the Voynich manuscript is how to use ai effectively as a tool and not a chatbot,  guilty. ill clean it up.
short version:
i think voynich is medieval sinhala (elu) written phonetically. i visited sri lanka, noticed old sinhala letterforms look like voynich glyphs. built a decoder that maps EVA characters to 14 sinhala phonemes. about 200 lines of python.
the evidence that its not random:

• decoded keywords cluster by manuscript section. plant words on plant pages, preparation terms on balneo pages. the decoder doesnt know which section its on. Z=31.81 against random shuffles, none of 1000 shuffles came close.
  
• EVA reads better right to left, decoded text reads better left to right. sinhala is left to right. the encoding flips directionality which is what you'd expect from an abugida.
  
• cross-modal: decoder outputs medical terms that match what the illustrations show, spatially. it cant see the pictures.
  
• ablation: remove rules from the decoder and results degrade. random decoders dont reproduce the clustering.
  

what it doesnt do: produce fluent readable sinhala. dictionary hit rate is around 50%. entropy doesnt reach natural language levels. i need a medieval sinhala scholar to evaluate the output properly.
the decoder itself is in h12_decoder.py if anyone wants to skip the readme and just look at the code.

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pick a couple of folios i can put them through the decoder and give you the raw output

That's much easier to follow.

Can you decode this?