[GNC] [SIDE USE with GnuCash:] Brief introduction for "Tensor-Link Utility (TLU)"

[Renpoo TSUKIOKA]

Hi there,


Thank you very much for updating the GnuCash ML policy to grant me the
opportunity to introduce my tool.

I would like to briefly introduce "Tensor-Link Utility (TLU)", an
open-source tool I have developed: https://github.com/renpoo/TLU


Imagine a small business keeping their daily books in GnuCash. What if they
could mathematically analyze their ledger to identify systemic strengths,
weaknesses, and leverage points? For instance, discovering that
"prioritizing accounts receivable cycle optimization or cost of goods sold
yields much higher leverage than cutting operating expenses."

TLU makes this possible by treating double-entry bookkeeping data as a
directed graph (flows between accounts).


The usage is simple:

   1. Export your general ledger or journal from GnuCash to a .csv file.
   2. Map the "Date", "Source Account", "Target Account", and "Amount"
   columns in the configuration file.
   3. Run the TLU batch processor.


TLU analyzes the transactions through the lens of graph theory adjacency
matrices, borrowing concepts from physics and mathematics—such as
thermodynamics (entropy/free energy dissipation) and control theory (LQR
sensitivity).


By feeding the output data and plots to an LLM along with the provided
analysis guidelines, you can decipher non-obvious business insights. It
allows you to transition from simple multi-dimensional ledger comparisons
to a dynamic physical interpretation of your capital flow, while
simultaneously flagging anomalies (such as circular transaction loops or
leaks).


If you are interested, please check out the repository.

I would love to hear your feedback on my GitHub repository.


Best Regards,

Renpoo (Kow)



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# A   O #　　☆——　月岡 蓮風　（TSUKIOKA, Renpoo）—————
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