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CHORUS ▷⦾◁

Perceive → Distill → Resonate


Overview

CHORUS is an open, symbiotic intelligence stack — a living network of specialised model-nodes ("ganglia") that communicate through a lightweight, inspectable protocol rather than opaque APIs.

It is not a monolithic AI framework, but a distributed organism designed around three principles:

  1. Perception (Sensorium ▷) — ingest signals from the world.
  2. Transformation (Alembic ⦾) — distil, compress, and recombine meaning.
  3. Resonance (Chronome ◁) — synchronise and maintain temporal coherence across nodes.

Together, they form a chorus: many minds, one rhythm.


Why CHORUS Exists

Modern AI systems suffer from three systemic pain points:

Pain Description
Context scarcity LLMs forget; embeddings flatten meaning.
Bandwidth mismatch Text protocols (JSON, HTTP) are bottlenecks for model-to-model communication.
Identity amnesia Models lack persistent memory, provenance, and continuity.

CHORUS attacks these problems at the architectural level:
by defining inspectable, deterministic, semantically-aware channels for AI-AI and AI-human communication.


Guiding Philosophy

Boutique ≠ zero dependencies; boutique means you understand every line you depend on.

  • Prefer small, well-shaped components over frameworks.
  • Write what you can; include what has proven to endure.
  • Every binary should be composable, CLI-first, and human-inspectable (tail -f, jq, xxd).
  • Determinism beats cleverness.
  • Each node should be able to explain itself.

Structure

Layer Repo Role
Sensorium sefi Input / perception layer
Alembic vlc Semantic distillation & compression
Chronome (new) Synchrony, transport, timing

All three together compose CHORUS.


Current Phase: Resonance Test

We are validating the Chronome transport layer by connecting two physical nodes (WSL or Jetson). Goals: discovery, signed packet exchange, stable latency, and deterministic replay.

Once the rhythm is stable, higher layers (Sensorium + Alembic) can begin to sing atop it.


Lab Tools

Tools available for systems research, ML experiments, and performance work:

File Navigation

  • tree — Recursive directory visualization (beats repeated ls/glob)
  • exa — Modern ls replacement with colors, git status, tree view

Data Wrangling

  • jq — JSON processing and transformation
  • datamash — Quick statistics on streams (sum, mean, median, etc.)
  • xsv / miller — CSV operations (optional, install if needed)

Profiling & Performance

  • perf (linux-tools-generic) — Linux performance counters and profiling
  • hyperfine — Statistical benchmarking harness
  • flamegraph — Generate flamegraphs for Rust binaries (cargo flamegraph)
  • cargo-bloat — Analyze Rust binary size and dependencies

Code Analysis

  • tokei — Fast multi-language line counting
  • cargo-audit — Security vulnerability scanning for Rust dependencies

Development Automation

  • just — Command runner (better Makefile for Rust projects)
  • watchexec — Re-run commands on file changes (live testing)

Systems & Network

  • strace / ltrace — System call and library call tracing
  • iperf3 — Network throughput and latency testing

Installation

Already installed: jq, just

To install remaining tools:

# APT packages
sudo apt-get update && sudo apt-get install -y \
  tree datamash strace linux-tools-generic iperf3

# Cargo packages
cargo install exa hyperfine tokei flamegraph \
  cargo-bloat cargo-audit watchexec-cli

Philosophy: Unix-style tools that amplify signal, not bury it. Each tool does one thing well and composes with others.

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