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monkey-w1n5t0n b16f26e6ab refactor(ml): one runtime-configurable training default (S26) 
The operator's call: "there should be one default learning rate and one
default max iterations and they should both be configurable at runtime."

There were SIX copies, not the four the audit described, and they did not
agree:

  nisps/ml/mlp.hpp        no-arg train() hardcoding 1.f / 1000u / 0.001f —
                          and firmware's ONLY training path calls exactly
                          this, so firmware had no runtime knob at all
  wasm-iml.ts             train() and trainAsync() TS default params (x2)
  engine-api.ts           learningRate ?? 1.0, with no maxIterations knob
  vcv/src/iml.hpp         200 / 0.1 / 0.00001 — silently divergent
  external_synth_midi.hpp its own kDefaultLearningRate/kDefaultMaxIterations
  schemas/modes/*.json    x9, identical, read by nobody at runtime

Now: schemas/ml_defaults.json is the single declaration (validated against a
sibling meta-schema, matching the midi_device.schema.json convention), codegen
emits it to C++ and TS in the same run, and MLPCore carries a TrainConfig whose
default member initialisers read the generated constant.
set_train_config()/nisps_ml_set_train_config() make it runtime-overridable on
every target; the explicit-argument train() overload is untouched. min_error
joins the tuple — it was duplicated identically and belongs with the other two.

The per-mode ml block loses default_learning_rate/default_max_iterations.
default_spread stays (genuinely wired on both targets) and input_channels stays
(codegen-time validated, real information for sound_analysis_midi).

VCV BEHAVIOUR CHANGE, deliberate: MEMLNaut.cpp constructs IML positionally and
relies on those defaults, so the module moves to 1000/1.0/0.001 — 5x the max
iterations, 10x the learning rate, and a 100x looser early-stop threshold. The
old values were never justified anywhere; they arrived with fbc68eb alongside
an unrelated module rewrite and no tuning rationale. Firmware and WASM have
shipped 1.0/1000 all along. It is now runtime-settable if this turns out worse.

The generated header lands in nisps/ml/generated/, not nisps/modes/generated/
where the rest of codegen output lives: training hyperparameters are an ML
fact, and nisps/ml sits below nisps/modes, so emitting them there would make
mlp.hpp include upward. The agent that built this flagged the directory-crossing
rather than hiding it; this is the fix. CI's generated-freshness gate learns the
new directory.

Gates: run-all-tests.sh ALL GREEN — 4/4 ctest, parity PASS (max delta 2.38e-7),
lint clean, manifold typecheck + 17 unit + 33 e2e (which exercise train() and
trainAsync() through a real browser).
2026-07-21 17:20:10 +02:00
.github/workflows refactor(ml): one runtime-configurable training default (S26) 2026-07-21 17:20:10 +02:00
.vscode memlnautmodes, channelstrip 2025-11-19 15:17:37 +00:00
assets/media docs: restructure design docs into docs/specs (adr/plans/recon), update path references 2026-07-13 23:15:46 +03:00
codegen refactor(ml): one runtime-configurable training default (S26) 2026-07-21 17:20:10 +02:00
docs docs: record the operator's L25 and telemetry decisions 2026-07-21 17:05:30 +02:00
firmware feat(firmware): bump memllib to current upstream and dissolve the fork 2026-07-21 17:04:10 +02:00
manifold refactor(ml): one runtime-configurable training default (S26) 2026-07-21 17:20:10 +02:00
nisps refactor(ml): one runtime-configurable training default (S26) 2026-07-21 17:20:10 +02:00
schemas refactor(ml): one runtime-configurable training default (S26) 2026-07-21 17:20:10 +02:00
scripts refactor(ml): one runtime-configurable training default (S26) 2026-07-21 17:20:10 +02:00
src feat(firmware): bump memllib to current upstream and dissolve the fork 2026-07-21 17:04:10 +02:00
tests/cpp refactor(engines): extract the shared sequencer machinery 2026-07-21 14:02:23 +02:00
vcv refactor(ml): one runtime-configurable training default (S26) 2026-07-21 17:20:10 +02:00
.envrc chore: add .envrc + playground package-lock 2026-06-28 04:14:30 +02:00
.gitignore feat(manifold)!: P1 — retire playground/, manifold is the sole browser app 2026-07-13 23:27:56 +02:00
.gitmodules feat(firmware): bump memllib to current upstream and dissolve the fork 2026-07-21 17:04:10 +02:00
AGENTS.md docs+ci: P5 doc sync; codegen golden wired into run-all-tests stage 5 2026-07-18 12:45:45 +02:00
ALIGNMENT.md docs: record the operator's L25 and telemetry decisions 2026-07-21 17:05:30 +02:00
CLAUDE.md chore(agents): converge project instructions 2026-07-13 22:58:26 +02:00
LICENSE Initial commit 2025-04-10 14:17:18 +01:00
MAP.md refactor(ml): one runtime-configurable training default (S26) 2026-07-21 17:20:10 +02:00
README.md feat(manifold)!: P1 — retire playground/, manifold is the sole browser app 2026-07-13 23:27:56 +02:00

README.md

Neural Interactive Shaping of Parameter Spaces

https://musicallyembodiedml.github.io/memlnaut/approaches/nisps

Firmware

The hardware firmware targets the MEMLNaut RP2350 build and uses repo-local helper scripts for the known-good build configuration:

git submodule update --init --recursive

scripts/build-firmware.sh
scripts/flash-firmware.sh
scripts/build-and-flash-firmware.sh

Notes:

  • The scripts build for rp2040:rp2040:solderparty_rp2350_stamp_xl with Optimize3.
  • The build forces C++20 because the firmware uses std::span and concepts.
  • build-firmware.sh accepts an optional variant name such as MEMLCelium or BreakOr. Matching remains case-insensitive, so memlcelium still works. If you omit it in an interactive shell, the script parses MEMLNaut-NISPS.ino, prompts for a variant, and rewrites the active MEMLNAUT_MODE_TYPE before building.
  • flash-firmware.sh accepts an optional mountpoint argument, or auto-detects common UF2 bootloader mounts such as /run/media/$USER/RP2350 and /run/media/$USER/RPI-RP2.

Manifold (browser app)

Try NISPS in your browser — no hardware required. Manifold is the React front-end running the same C++ engines + ML as the firmware, compiled to WASM:

cd manifold
bun install
bun run dev

Staging deployment: https://meml.lnfinitemonkeys.org/next/

Train a neural network to map input gestures to synth parameters through interactive machine learning: place examples, or use verdict-based feedback (explore-and-place, geometric dislike).

(The former SolidJS playground was retired in July 2026 — archived on branch archive/playground-solidjs, tag playground-solidjs-final.)