w1n5t0n/memlnaut-nisps
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions
No description
433 commits 1 branch 0 tags 9.6 MiB
C++ 69.4%
TypeScript 25.4%
Shell 1.6%
HTML 1%
JavaScript 0.8%
Other 1.8%
Find a file
monkey-w1n5t0n 60584606a8 docs(specs): P5 architectural specs — 5a, 5b, 5c, 5d 
Plan §6 says each P5 item is spec-first with its own session. These are the
specs; no implementation is authorised by them and none was written.

  plans/mode-layer-reunification.md   5a — ALIGNMENT defect 1, the largest
                                      architectural gap. Storage-policies the
                                      ModeBase orchestration the way P2 did
                                      MLPCore, rather than binding monolithic
                                      mode objects into WASM (which would
                                      contradict the locked two-instance RT
                                      architecture).
  plans/browser-mode-coverage-spec.md 5b — an audio-topology notion so Manifold
                                      stops cataloguing 4 modes that
                                      structurally cannot run in a browser.
  plans/curated-presets-spec.md       5c — ALIGNMENT defect 2, built on the
                                      operator's §7.6 definition: a curated
                                      preset is configuration only, network
                                      untrained.
  plans/hardware-editor-spec.md       5d — ALIGNMENT defect 3, applying
                                      useq-celium's existing discipline (C
                                      header as wire truth + TS mirror + parity
                                      test) to a MEMLNaut serial protocol.

2434 lines. I spot-checked their path citations mechanically against
git ls-files: of 170 backticked paths, every unresolved one is either a file
the spec proposes to create or a cross-reference to a sibling spec in this same
commit. None describes deleted code as live — which is the failure mode that
made half the existing corpus untrustworthy, and the reason the §8 pass earlier
today had so much to do.

The preset spec is the most valuable byproduct: en route it found five places
where existing docs still describe a deleted world, including
manifold-parity-features-spec.md §1.1 specifying a PipelineLayer over
engine/input-pipeline.ts and output-pipeline.ts, both deleted at one-core P4.

Also carries the doc sync for the telemetry and benchmark work in a77770f:
AGENT-REFERENCE gains the throughput and loss-history entries, and dock-spec
§1.3 records that its long-deferred diagnostics suite shipped PARTLY — the loss
curve and weight-health table are real, GradientFlow is not built and is not
planned as drawn (the core records no per-layer gradient magnitudes, and the
fabricated version was deleted in Phase 1).
2026-07-21 22:03:39 +02:00
.github/workflows feat: curve truth, DriverConfig, real telemetry, engine benchmark 2026-07-21 22:02:23 +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 feat: curve truth, DriverConfig, real telemetry, engine benchmark 2026-07-21 22:02:23 +02:00
docs docs(specs): P5 architectural specs — 5a, 5b, 5c, 5d 2026-07-21 22:03:39 +02:00
firmware feat: curve truth, DriverConfig, real telemetry, engine benchmark 2026-07-21 22:02:23 +02:00
manifold feat: curve truth, DriverConfig, real telemetry, engine benchmark 2026-07-21 22:02:23 +02:00
nisps feat: curve truth, DriverConfig, real telemetry, engine benchmark 2026-07-21 22:02:23 +02:00
schemas feat: curve truth, DriverConfig, real telemetry, engine benchmark 2026-07-21 22:02:23 +02:00
scripts feat: curve truth, DriverConfig, real telemetry, engine benchmark 2026-07-21 22:02:23 +02:00
tests/cpp feat: curve truth, DriverConfig, real telemetry, engine benchmark 2026-07-21 22:02:23 +02:00
vcv docs: the specs disposition pass (plan §8) 2026-07-21 20:17:58 +02:00
.envrc chore: add .envrc + playground package-lock 2026-06-28 04:14:30 +02:00
.gitignore build(firmware): migrate to PlatformIO and vendor memllib (plan §5) 2026-07-21 20:17:58 +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 feat: curve truth, DriverConfig, real telemetry, engine benchmark 2026-07-21 22:02:23 +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 feat: curve truth, DriverConfig, real telemetry, engine benchmark 2026-07-21 22:02:23 +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.)