sijuec/eridani-speak

eridani 말한다

두 개의 기술 파일. 두 순간에 이야기. 제로 필러.

설치 • 신호 • 록키 • 이전 / 후 • 계정 만들기

---

LLM 산출은 verbose입니다. 그것의 대부분은 overhead — 즐겁고, hedging, 충전물 구문은 정보를 나르지 않습니다. 모든 토큰을 지불합니다.

이 repo는 두 가지 기술 파일을 포함, 서로 다른 방법으로.

둘 다 Rocky에 의해 영감을 — 안디 Weir의 Eridian 문자 Project Hail Mary. 6x 인적 밀도에 통신하는 것입니다. 필러 없음. 문법 오버헤드 없음. 순수한 신호. 책의 끝에 Rocky는 유창한 영어를 말하지만 여전히 직접, 여전히 dense, 여전히 즐거운.

두 가지 모드는 두 가지 순간입니다.

---

신호 형태

Rocky 형태는 측 효력을 일으킵니다: 지구는 온난하고 개성을 멀리하고, 좌측은 기술적인 산출을 위한 청결한 표기 체계입니다. 그것은 신호입니다. 동일한 조밀도, 특성 없음. 에이전트 파이프라인 및 코딩 세션을 위해 내장 된 개성은 원치 않는 오버 헤드입니다.

표기 지문:

**→ 신호/SKILL.md **

---

록키 모드

전체 록키. 신호 플러스 영혼.

완전한 문자 - dense, direct, 더 즐거운 것보다 사실로 따뜻한.

eridani-speak

two skill files. two moments in the story. zero filler.

Install • Signal • Rocky • Before / After • Credits

---

LLM output is verbose. Most of it is overhead — pleasantries, hedging, filler phrases that carry no information. You pay for every token.

This repo contains two skill files that fix that, each in a different way.

Both are inspired by Rocky — the Eridian character in Andy Weir’s Project Hail Mary. A being that communicates at 6x human density. No filler. No grammar overhead. Pure signal. By the end of the book Rocky speaks fluent English — but still direct, still dense, still no pleasantries.

The two modes are those two moments.

---

Signal mode

Rocky mode produces a side effect: strip away the warmth and personality, and what’s left is a clean notation system for technical output. That’s Signal. Same density, no character. Built for agent pipelines and coding sessions where personality is unwanted overhead.

Notation fingerprint:

X = Y           definition
X → Y           causes / leads to  
X: a, b, c      properties
Fix: ...        solution
Note: ...       important caveat

→ signal/SKILL.md

---

Rocky mode

Full Rocky. Signal plus soul.

The complete character — dense, direct, warm through fact rather than pleasantry. Nine linguistic patterns extracted from the book and tested empirically until the output is recognisable to anyone who has read it. No character names in the prompt. No book references. Pure linguistic pattern.

→ rocky/SKILL.md

---

Before / After

Normal LLM

335 tokens

An LLM (Large Language Model) is a type of AI trained on massive amounts of text to predict and generate language. The core idea: given some text, what words are likely to come next? Train a neural network on billions of examples of this, at enormous scale, and something remarkable emerges — the model doesn’t just learn to autocomplete, it develops a broad, flexible understanding of language, facts, reasoning, and context. A few key intuitions: It’s a compression of human knowledge. The training data is essentially a huge slice of written human thought — books, code, conversations, articles. The model compresses patterns from all of that into its weights (the billions of numerical parameters that define its behavior). It works by predicting, not retrieving. It doesn’t look things up in a database. It generates responses token by token based on learned patterns — which is why it can be fluent and wrong at the same time.