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The connectome-fly UI now runs the real FlyWire brain end-to-end:
115,151 neurons, 2,676,592 unique synapses (from 3.78M Princeton rows
aggregated per (pre, post)), 2,590 sensory neurons auto-detected.
Changes:
- src/connectome/flywire/princeton.rs: new gzipped-CSV loader for the
Princeton codex.flywire.ai format (neurons.csv.gz +
connections_princeton.csv.gz). Uses serde's #[rename] to map
"Root ID" / "pre_root_id" / "Predicted NT type" / etc. to the
existing NeuronMeta schema. Aggregates per-neuropil rows on the fly
into per-(pre, post) synapse counts. Zero dangling ids on the
shipped dataset.
- src/bin/ui_server.rs: CONNECTOME_FLYWIRE_PRINCETON_DIR env var
selects the Princeton path; falls through to v783 TSV then
synthetic SBM. Observer's detect_every_ms backs off to 500 ms at
N ≥ 10k and CONNECTOME_SKIP_FIEDLER=1 disables it entirely (the
Fiedler eigensolver is O(window_spikes²)–O(n³) and melts the stream
at 115k neurons without one of those mitigations).
- examples/connectome-fly/assets/{neurons,connections_princeton}.csv.gz:
the 2.1 MB + 26 MB Princeton dump, committed under assets/ so the
example is self-contained. Clone size +28 MB.
- Cargo.toml: flate2 1.0 dependency (already pinned elsewhere in the
workspace for ruvector-cli / ruvector-snapshot).
- flywire/mod.rs: pub use princeton::load_flywire_princeton.
Run it:
cargo build --release --bin ui_server
CONNECTOME_FLYWIRE_PRINCETON_DIR=examples/connectome-fly/assets \
CONNECTOME_SKIP_FIEDLER=1 \
CONNECTOME_SKIP_COMMUNITIES=1 \
./target/release/ui_server
cd examples/connectome-fly/ui && npm run dev
Measured on a commodity host:
with CONNECTOME_SKIP_FIEDLER=1 → 49 sim-ticks / 5 s wall, 2.2 M
real spikes after 5 s
with detector default 5 ms → 4 sim-ticks / 10 s wall
(Fiedler λ₂ on the 100 k-spike
co-firing window dominates)
Browser validation (agent-browser): banner reads "engine=rust-lif
substrate=flywire-princeton-csv n=115,151 syn=2,676,592 witness=…",
tick advances past 123, real_spikes_total > 6 M within a few seconds,
zero console errors.
This closes the "can we run the entire fly brain, not just 1024
neurons" question. Open follow-up: raster UI still bins spikes modulo
208 rows — at 115 k neurons that's ~550× overloaded, so the canvas
mostly dims out. Proper per-module binning or downsampling is a UI
task, not an engine task.
Co-Authored-By: claude-flow <ruv@ruv.net>
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| .. | ||
| agentic-jujutsu | ||
| apify | ||
| app-clip | ||
| benchmarks | ||
| boundary-discovery | ||
| brain-boundary-discovery | ||
| climate-consciousness | ||
| cmb-boundary-discovery | ||
| cmb-consciousness | ||
| connectome-fly | ||
| data | ||
| decompiler-dashboard | ||
| delta-behavior | ||
| dna | ||
| docs | ||
| dragnes | ||
| earthquake-boundary-discovery | ||
| ecosystem-consciousness | ||
| edge | ||
| edge-full/pkg | ||
| edge-net | ||
| exo-ai-2025 | ||
| frb-boundary-discovery | ||
| gene-consciousness | ||
| google-cloud | ||
| graph | ||
| gw-consciousness | ||
| health-boundary-discovery | ||
| infrastructure-boundary-discovery | ||
| market-boundary-discovery | ||
| meta-cognition-spiking-neural-network | ||
| mincut | ||
| music-boundary-discovery | ||
| neural-trader | ||
| nodejs | ||
| onnx-embeddings | ||
| onnx-embeddings-wasm | ||
| OSpipe | ||
| pandemic-boundary-discovery | ||
| prime-radiant | ||
| pwa-loader | ||
| quantum-consciousness | ||
| real-eeg-analysis | ||
| real-eeg-multi-seizure | ||
| refrag-pipeline | ||
| robotics | ||
| rust | ||
| ruvLLM | ||
| rvf | ||
| rvf-desktop | ||
| rvf-kernel-optimized | ||
| scipix | ||
| seizure-clinical-report | ||
| seizure-therapeutic-sim | ||
| seti-boundary-discovery | ||
| seti-exotic-signals | ||
| spiking-network | ||
| subpolynomial-time | ||
| temporal-attractor-discovery | ||
| train-discoveries | ||
| ultra-low-latency-sim | ||
| verified-applications | ||
| vibecast-7sense | ||
| void-boundary-discovery | ||
| vwm-viewer | ||
| wasm/ios | ||
| wasm-react | ||
| wasm-vanilla | ||
| weather-boundary-discovery | ||
| bounded_instance_demo.rs | ||
| README.md | ||
| security_hardened.rvf | ||
RuVector Examples
Comprehensive examples demonstrating RuVector's capabilities across multiple platforms and use cases.
Directory Structure
examples/
├── rust/ # Rust SDK examples
├── nodejs/ # Node.js SDK examples
├── graph/ # Graph database features
├── wasm-react/ # React + WebAssembly integration
├── wasm-vanilla/ # Vanilla JS + WebAssembly
├── agentic-jujutsu/ # AI agent version control
├── exo-ai-2025/ # Advanced cognitive substrate
├── refrag-pipeline/ # Document processing pipeline
└── docs/ # Additional documentation
Quick Start by Platform
Rust
cd rust
cargo run --example basic_usage
cargo run --example advanced_features
cargo run --example agenticdb_demo
Node.js
cd nodejs
npm install
node basic_usage.js
node semantic_search.js
WebAssembly (React)
cd wasm-react
npm install
npm run dev
WebAssembly (Vanilla)
cd wasm-vanilla
# Open index.html in browser
Example Categories
| Category | Directory | Description |
|---|---|---|
| Core API | rust/basic_usage.rs |
Vector DB fundamentals |
| Batch Ops | rust/batch_operations.rs |
High-throughput ingestion |
| RAG Pipeline | rust/rag_pipeline.rs |
Retrieval-Augmented Generation |
| Advanced | rust/advanced_features.rs |
Hypergraphs, neural hashing |
| AgenticDB | rust/agenticdb_demo.rs |
AI agent memory system |
| GNN | rust/gnn_example.rs |
Graph Neural Networks |
| Graph | graph/ |
Cypher queries, clustering |
| Node.js | nodejs/ |
JavaScript integration |
| WASM React | wasm-react/ |
Modern React apps |
| WASM Vanilla | wasm-vanilla/ |
Browser without framework |
| Agentic Jujutsu | agentic-jujutsu/ |
Multi-agent version control |
| EXO-AI 2025 | exo-ai-2025/ |
Cognitive substrate research |
| Refrag | refrag-pipeline/ |
Document fragmentation |
Feature Highlights
Vector Database Core
- High-performance similarity search
- Multiple distance metrics (Cosine, Euclidean, Dot Product)
- Metadata filtering
- Batch operations
Advanced Features
- Hypergraph Index: Multi-entity relationships
- Temporal Hypergraph: Time-aware relationships
- Causal Memory: Cause-effect chains
- Learned Index: ML-optimized indexing
- Neural Hash: Locality-sensitive hashing
- Topological Analysis: Persistent homology
AgenticDB
- Reflexion episodes (self-critique)
- Skill library (consolidated patterns)
- Causal memory (hypergraph relationships)
- Learning sessions (RL training data)
- Vector embeddings (core storage)
EXO-AI Cognitive Substrate
- exo-core: IIT consciousness, thermodynamics
- exo-temporal: Causal memory coordination
- exo-hypergraph: Topological structures
- exo-manifold: Continuous deformation
- exo-exotic: 10 cutting-edge experiments
- exo-wasm: Browser deployment
- exo-federation: Distributed consensus
- exo-node: Native bindings
- exo-backend-classical: Classical compute
Running Benchmarks
# Rust benchmarks
cargo bench --example advanced_features
# Refrag pipeline benchmarks
cd refrag-pipeline
cargo bench
# EXO-AI benchmarks
cd exo-ai-2025
cargo bench
Related Documentation
License
MIT OR Apache-2.0