w1n5t0n
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ba0fcab2a2
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feat(vcv,playground): complete Phases 8, 9, 10 — all phases done
Phase 8 — Companion webapp bridge:
- NISPS-FORMAT.md: full .nisps JSON schema with validation rules
- Webapp iml.js: exportState() / importState() with bias handling
- osc_server.hpp: minimal UDP OSC server (cross-platform, no deps)
- VCV module: OSC toggle + port selection in right-click menu
- osc-client.js: WebSocket client with auto-reconnect
- Bridge scripts updated for bidirectional VCV↔webapp relay
Phase 9 — Panel layout variants:
- MEMLNaut.svg: 30HP standard panel (matches widget positions)
- MEMLNaut-wide.svg: 44HP with expanded display and 8 input slots
- MEMLNaut-expander.svg: 8HP with 6 extra inputs and LINK LED
Phase 10 — Polish & distribution:
- README.md: 267-line user guide (install, quick start, RL workflow,
presets, OSC, technical details)
- BUILDING.md: build prerequisites, SDK setup, local install
- Makefile.dist: platform-stamped zip packaging
- SPEC.md: performance characteristics (1060 MADs/pass, ~46KB/instance)
- SPEC.md: v1 compatibility assessment (v2-only recommended)
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2026-03-28 01:48:44 +02:00 |
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w1n5t0n
|
74c52fadc7
|
feat(nisps-core,vcv): complete Phases 6 + 7 — persistence, derived outputs
Phase 6 — State persistence:
- Full state serialization: version, weights (3D), examples (features+labels),
mlpConfig, noiseLevel, slewMs, output/input ranges
- Validation on load: version check, graceful missing field handling
- .nisps preset save/load via right-click menu (osdialog file dialogs)
- Param values included in preset files
Phase 7 — Derived outputs:
- Mean, STD, delta computed on audio thread (trivial cost)
- Novelty/confidence: nearest_example_distance() computed on background
thread after each training/perturbation job, cached for audio thread
- Defaults with 0 examples: novelty=10V, confidence=0V
nisps-core IML additions:
- get_weights() / set_weights() for MLP weight serialization
- get_example_features/labels() / load_examples() for dataset serialization
- nearest_example_distance() for novelty/confidence metric
- get_example_count() / get_max_examples() for UI display
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2026-03-28 01:27:19 +02:00 |
|
w1n5t0n
|
6a76f15736
|
feat(vcv): complete Phases 3, 4, 5 — RL feedback, display, configurability
Phase 3 — RL feedback system:
- Background worker thread with job queue, condition variable, atomic flags
- Thumbs up/down buttons + CV trigger inputs (Schmitt triggers)
- Learn enable toggle + gate input (OR logic)
- Noise level tracking (decay on +, increase on −, spread-dependent cap)
- Post-change output crossfade (configurable slew, default 10ms)
- Rapid feedback queueing with coalescing (max depth 1)
- Graceful thread shutdown (shouldStop flag, joins in destructor)
Phase 4 — Visual feedback:
- NanoVG bar graph display (12 hue-coded bars, noise level, TRAIN indicator)
- 12 output level LEDs, LEARN LED (green), training LED (yellow)
Phase 5 — Configurability:
- RATE knob: exponential decimation from block-rate to audio-rate
- Per-output range: unipolar (0-10V) / bipolar (±5V) via context menu
- Per-input range: unipolar / bipolar via context menu
- 12 attenuverter trimpots (-1 to +1)
- SPREAD CV input for knob modulation
- CLEAR button with 1-second long-press guard
- Output slew configurable via context menu (0-100ms)
- State serialization (ranges, noise, slew) via dataToJson/dataFromJson
Note: double-buffering uses direct IML access (not shadow copy) pending
IML weight get/set API (filed as meml-ft7).
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2026-03-28 01:04:07 +02:00 |
|
w1n5t0n
|
e52b800a92
|
feat(vcv): complete Phase 2 — core engine wired up
- IML<float> with [16,24,16] hidden layers as module member
- CV inputs read, clamped 0-10V, normalized to [0,1]
- MLP inference in process(), 12 outputs scaled to 0-10V
- SPREAD knob (0-1, default 0.6) controls weight initialization
- RAND button randomizes weights using current spread value
- Panel: knob + button + 2 inputs + 12 outputs in 2x6 grid
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2026-03-28 00:38:58 +02:00 |
|
w1n5t0n
|
d0ba1faaea
|
feat(nisps-core,vcv): complete Phase 0 + Phase 1
Phase 0 — spread-aware API ported to nisps-core C++:
- MLP::DrawWeightsSpread(T spread) — interpolate uniform↔Xavier per layer
- MLP::MoveWeightsSpread(T speed, T spread) — per-layer noise + weight decay
- IML::randomise_weights(Float spread) and IML::move_weights(speed, spread)
- 5 unit tests (10/10 total pass)
Phase 1 — VCV Rack 2 plugin skeleton:
- Makefile with C++20, nisps-core include path
- plugin.json manifest
- Empty MEMLNaut module: 2 inputs, 12 outputs, placeholder SVG panel
- static_assert verifies nisps-core headers resolve
- C++20 confirmed working in VCV SDK (8 existing plugins use it)
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2026-03-28 00:36:46 +02:00 |
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