Tip: neither the "30 second TL;DR" nor the intro paragraph above it really explain to anyone unfamiliar with your (possibly novel?) jargon what it does
“Semiotic awareness” is not standard ML terminology. The dictionary definition of semiotic simply means “relating to symbols” so it’s a bit grandiose to say you have Qwen “awareness of symbols” when in reality it’s a marginal improvement if even true.
Also to say that a philosopher that died 100 years ago inspired a new attention head is another instance of GPT off his rocker again. You don’t need MAH to contextualize “freedom” in a sentence. Attention already does that.
Thank you, I would appreciate additional feedback on how I can improve that?
Edit: its not GPT nor off rocker. This repo empirically proved computational semiotics with the reference to C.S. Peirce, Paul Kockelman, and many other respected contemporary semioticians.
Just try to explain why I should use it and why it's different or better than alternatives - in terms of some qualities of the results rather than how it's implemented
The technical implementation details are also useful to have, but they're a bit hard to parse into "what is this?"
FWIW I'm sympathetic to vibe-coded docs as I'm doing it myself a bit lately, but the agents are bad at it by default because all their context is the how and why of technical decisions made while coding with you
they need specific coaching to get them to try to write for the perspective of a new user
The main reason to use it is the output quality. SRT steers the model toward a consistent target voice or discourse style more reliably than prompting or basic steering, while keeping the base model frozen. The results feel more coherent in tone and perspective across longer outputs, especially when the target style comes from a specific corpus or community.
On the sympathetic point about vibe-coded docs: exactly.
Thanks for the feedback … rough and precise equally appreciated. Computational semiotics was empirically proven with this repo. I will work hard to make the findings and content more accessible for everyone.
It’s not as if they were one shot. 5 repos prior, two published pre-prints on SSRN and thousands of hours back my research that is right there for you to peer review and use freely.
It seems to be assuming a familiarity with logic algebras in general. It's main operator is just the common math symbol for logical conjunction (∧) [0] and how familiar it feels versus how obscure it feels depends on your mathematical background (and how long it has been). But yeah, most programming languages tend to prefer operators like & or && or `and` for logical conjunction, so Prela chose the mathematical choice over the programming language choice. Which is perhaps easily explainable by Prela starting as trying to be a pure syntax of Relational Algebra [1] which does usually prefer the mathematical symbols, given it is the mathematical theory (underpinning things like SQL) for academic/mathematical discussion more than a working programming language. Though we live in a time of Unicode where that distinction starts to get blurry again and mathematical symbols are easier to use than prior eras without dedicated APL keyboards or things like that.
Yes, it's python-inspired. Some notable differences are:
- no return keyword
- match/if are expressions
- it's functional
- =? is used for early returns or binding, depending on the variant of an Option or Result that is returned
There's a lot of other differences -- it's a smaller language surface than Python overall.
> LLM - picks a Claude backend. The default uses your local Claude Code session, so no API key is required. You can also use an Anthropic API key or Vertex AI.
I'm skim reading the docs but I just want to know a bit more about the architecture
Does it produce artefacts that I can commit to version control and share with my team? Is it a tool that everyone runs locally in the project? Or there's a component (the wiki?) that I should deploy as an internal service?
When you install ktx, you'll have to initialize the project directory and ktx initializes a git repo there to assure the version controls.
ktx project directory is self-contained. The main 2 parts inside are :
- wiki: a collection of .md files
- semantic-layer: a collection of .yaml files
typically all these files are created/edited automatically during the ingestion, but you can also edit them by hand or even sync with a remote git.
ktx treats these files as sources and builds internal indexes in a sqlite db, located in projectDir/.ktx
The main way to use ktx is to start an mcp server by calling `ktx mcp start`
it'll start an http server and multiple people will be able to connect it to their agents.
Hope this helps, happy to answer any other questions!
We currently support anthropic models for the setup (whether through claude pro/max plan) or through API. Adding support for openai API / codex should be pretty straightforward - would love to get you in the community slack to get more details on your copilot setup
Absolutely! Maybe also Jonathan Kreisberg for guitar.. oh and on piano how could I forget Benny Green! I kind of feel he's not as big as he should be. Another pianist who I really love and took a few lessons with in NYC is Bruce Barth, also sorely underappreciated.
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