Skip to page content
SAVRN
Search Plan a project

Open-weight model · Other

fly-instinct

by Z roblitz/fly-instinct

fly-instinct is an open-weight model for other from Z, released under MIT License. Its published files total 25.9 MB.

A frozen, non-learning recursive network built on the real fruit-fly MaleCNS connectome (Janelia 2024). Feed any stimulus, get an organic, structured "instinctive" signal that is more alive than noise, less predictable than a script. Not AI. Not a brain.

Parameters—
Context—
Weights25.9 MB
Licensemit
AccessOpen weights
Monthly Downloads—

Model Card

By Z, published under mit, revision 92b87d3bb99c.

A frozen, non-learning recursive network built on the real fruit-fly MaleCNS connectome (Janelia 2024). Feed any stimulus, get an organic, structured "instinctive" signal that is more alive than noise, less predictable than a script. Not AI. Not a brain. Not learning. Just real neural wiring, frozen. - Real MaleCNS connectome subset (1,587,930 edges, ~25 MB) - 4 tuned presets: escape / sugar / startle / explore - Zero training. Weight-frozen. Deterministic per seed. https://roblitz-spec.github.io/fly-instinct/ - Full 1.05GB connectome + commercial license: https://robloxer31.gumroad.com/l/Fly-Instinct

Read Z's full model card

Fly-Instinct - Real Connectome "Instinct" Signal Engine

A frozen, non-learning recursive network built on the real fruit-fly MaleCNS connectome (Janelia 2024). Feed any stimulus, get an organic, structured "instinctive" signal that is more alive than noise, less predictable than a script.

Not AI. Not a brain. Not learning. Just real neural wiring, frozen.

What you get

  • Real MaleCNS connectome subset (1,587,930 edges, ~25 MB)
  • 4 tuned presets: escape / sugar / startle / explore
  • Bonus: C. elegans (309-neuron) connectome + 4 presets
  • Zero training. Weight-frozen. Deterministic per seed.

Quick start

pip install fly-instinct
fly-instinct fetch-data
fly-instinct run --preset escape --duration 5 --out signal.wav

Browser demo (no install)

https://roblitz-spec.github.io/fly-instinct/

Links

  • GitHub: https://github.com/roblitz-spec/fly-instinct
  • PyPI: https://pypi.org/project/fly-instinct/
  • Full 1.05GB connectome + commercial license: https://robloxer31.gumroad.com/l/Fly-Instinct

Data provenance

  • Fruit-fly: Janelia MaleCNS (CC-BY 4.0), https://male-cns.janelia.org
  • Roundworm: White et al. 1986 + Wang et al. 2024 neurotransmitter polarity

License

Code: MIT | Data: CC-BY 4.0 (attribution required)

Identity and Version

Repository
roblitz/fly-instinct
Publisher
Z
Task
Other
Modality
Other
Library
custom
Parameters
Not stated by the source
Languages
en
Revision
92b87d3bb99c53d29ff1b5d2f7e9c2d7c8872377
First published
2026-09-27
Last updated
2026-09-27

Files and Weights

5 files, 25.9 MB in total.

Documentation1 file · 1.6 KB
Other3 files · 25.9 MB
Repository1 file · 1.6 KB
Every file
FileTypeSizeSHA-256
README.mdDocumentation1.6 KB —
data/annotations.csvOther283.1 KB —
data/edges.csvOther25.2 MB 1893782015bc
data/neurotransmitters.csvOther364.2 KB —
.gitattributesRepository1.6 KB —

License and Download

License
mit
Access
Open weights, no gate
Download from Z

Released by Z through its official repository on Hugging Face. Read the license.

Questions About fly-instinct

Can I use fly-instinct commercially?

Yes. fly-instinct is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.

Similar Models

Model · Other

JLD

Shreshth

Official fitted Jacobian lens for JLD and JLD-fast, by Shreshth Saini, Balu Adsumilli, and Alan C. Bovik. JLD measures the perceptual distance between an aligned reference image and a distorted image. Lower scores mean more similar images. It projects early DINOv2 patch features onto directions selected by the encoder's output sensitivity. The lens is fitted from unlabeled images without human quality ratings. This installs the versioned release wheel and records it in uv.lock. The distribution is named jacobian-lens-distance; the Python import remains jld. The package already includes this lens. The encoder weights are downloaded separately through timm on first use. Python 3.10 or newer…

Open weights mit jld

ONNX conversion of cua-ai/cua-s1-forms, prepared for lightweight local inference experiments with ONNX Runtime, ONNX Runtime Web, WebAssembly (WASM), and WebGPU. Please read the original model card before using this conversion. It contains the authoritative description of the model's architecture, training setup, evaluation, limitations, and intended scope. CUA-S1 Forms is a small specialist "System One" model for form-oriented computer-use tasks. Unlike an autoregressive LLM, it does not generate text token-by-token. For each UI element, it receives: 1. a short structured context describing the task, form, and element, and 2. a set of candidate options such as extracted document values…

Open weights mit

Model · Other

weather-sculpt

Z

Sculpt real ECMWF ERA5 atmospheric state into publish-ready video, GIF, and sound. Not animation. Not simulation. Real observed weather, rendered through 4 tuned presets. - Global 0.25 deg x 7 days (5 variables: t2m, u10, v10, tp, msl) - China region 0.25 deg x 30 days (5 variables) - 4 presets: temperature waves / wind streamlines / rain curtain / pressure vortex - Weather sonification (WAV) - "hear" the atmosphere - Pre-downloaded, zstd-compressed, SHA-256 checksummed https://roblitz-spec.github.io/weather-sculpt/ - Full dataset + commercial license: https://robloxer31.gumroad.com/l/Weather-Sculpt ECMWF ERA5 reanalysis (CC-BY 4.0). The same dataset used by meteorologists worldwide.

Open weights mit custom

Pretrained DiT decoders for TT-VidT (Decoupling the Temporal Axis for Efficient Motion-Centric Video Pretraining, NeurIPS 2026). Paper · Each TT-VidT encoder pretraining run initialises its decoder from one of these; the training code downloads them automatically through DECODERPRETRAINED, e.g. "KBlueLeaf/TTVidT-decoders/pretrainvideoSqknorm". The decoders work in the 32x32x4 latent space of the frozen f8 frame VAE KBlueLeaf/latentmaid-vae and are conditioned on DINOv3 ViT-B/16 features (768-d). - ImageNet decoders reconstruct an ImageNet image from its DINOv3 class token. - Video decoders reconstruct a later video frame from its class token plus, through cross-attention, the spatial…

Open weights apache-2.0

Model · Other

nys-sft-public

Dakuwon Moody

NYS is not a transformer. It is a coupled-oscillator organism: phases \(\thetai\), amplitudes, natural frequencies \(\omegai\), and a sparse coupling graph \(K\). Language, compiler bits, and speech-frame IDs live on one graph (\(N = 5{,}000{,}000\) nodes, \(k = 512\) edges per node). The native graph trains by Equilibrium Propagation (free RK4, then same-moment free/nudged branches, then a contrastive \(K\) update), without backprop or a cross-entropy objective. A separate sparse context head now provides retrieved language inputs. The GPU Qwen teacher uses PyTorch/Transformers; that does not make the native graph a transformer. Dakuwon Moody (YNSScarSaiyan) · Saiyan Corp The…

Open weights mit nys-thermodynamic

Reference Machine: Apple M3 Max (16 CPU cores, 40 Metal GPU cores, 16 Apple Neural Engine cores, 48 GB Unified Memory) - Unlocking the Apple Neural Engine for Physical AI: While Apple's 16-Core NPU (38 TOPS) was designed primarily for vision and speech, physical PDE operators historically failed to run on it due to unsupported tensor primitives. By algebraically reformulating complex Fourier spectral convolutions and dynamic graph scatter into native Apple Machine Intermediate Language (MIL) representations, all 8 foundation networks achieve up to 98.4% ANE execution with 0 CPU fallbacks. - Extreme Energy Efficiency (0.071 J / step for ANE aerodynamic solver to 10.8 J for global weather at…

Open weights apache-2.0 coreml