Flit Physics

io.github.brashler由 GitHub 帳號 brashler 發布v0.2.2更新於 Oct 24, 2026

Tiny sphere-physics engine for three.js games: spawn, step, raycast, InstancedMesh-ready positions

已驗證STDIO僅桌面開發者工具媒體與設計

概覽

AI 產生的概覽

本機 MCP 伺服器,封裝給 three.js 遊戲用的輕量球體物理引擎 Flit,讓助理產生粒子、推進模擬並進行射線偵測。

可以做什麼
檢視引擎資訊並重設世界參數,例如重力、彈性、地面、邊界、靜止速度與高度場地形,支援雨滴、爆炸、網格、噴泉等產生預設,也可手動加入粒子。以固定步長推進模擬,並讀回適合 InstancedMesh 同步的扁平 xyz 位置及已靜止數量。在廣相網格與地形上投射射線以找出命中。
適用情境
適合在建立或製作需要球體碰撞、重力、地形或射線偵測的小型 three.js 遊戲或展示時使用,讓助理直接驅動模擬並檢視粒子狀態,而不必先撰寫整合程式碼。
執行需求
以本機 stdio 程序在桌面執行,透過 npx 搭配 vite-node 啟動儲存庫中的 mcp/server.ts,因此需要本機存在 Flit 儲存庫,並安裝 Node.js 及其相依套件。未宣告驗證、環境變數或標頭。
安裝前請注意
此伺服器會變更模擬狀態:重設與產生呼叫會覆寫世界設定和粒子位置,原有狀態會遺失。它是本機開發工具,未宣告憑證或網路存取;README 說明引擎仍在早期階段,功能還不多。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 Flit Physics,將其新增到工作區。
  2. 為需要使用其工具的對話啟用該服務。

Desktop only,透過 STDIO。 STDIO 服務會啟動本機處理程序,因此需要 SourceWeft 桌面主機。

其他 MCP 客戶端

參照 儲存庫 中的啟動說明。

README

Flit

Flit is a tiny little physics engine for 3js. It doesn't do much yet! but I believe!

Spheres, collisions, raycasts, gravity, and a spatial-hash broadphase — nothing more, on purpose. All simulation state lives in flat Float32Arrays (structure-of-arrays), so the CPU path doubles as the reference implementation for a future WebGPU compute backend: same buffers, same kernels, no re-architecting.

What's in the box

  • World — point-sphere particles, semi-implicit Euler integration, impulse + positional-correction contact solver, infinite ground plane. Bodies that stay quiet SETTLE (per-body sleep driven by a stiction threshold), so resting piles cost ~nothing; a fast-enough impact wakes them, a slow push is held by static friction.
  • Heightfield — 2.5D terrain as a grid of height posts, no meshing: bilinear height + central-difference normals sampled directly for collision, and a 2D Amanatides-Woo DDA for raycasts (index: -1).
  • Raycast — Amanatides-Woo voxel walk over the broadphase grid; candidates from the 3x3x3 block around the walked path so corner clips are never missed. Differential-tested against a brute-force oracle.
  • SpatialHash — uniform grid broadphase keyed by Morton (Z-order) cell codes. This is the Euclidean cousin in the LSH family: MinHash buckets documents by Jaccard similarity; this buckets positions so nearby points collide in the same bucket. Morton keys are bijective (no hash-collision pair bloat), invertible (no side table), and locality-ordered for a future sorted-array GPU backend.
  • Distance & bit utilities — squared Euclidean/L1/Minkowski/L∞/Hellinger/ chi-square/KL distances (ported from FLANN), Morton (Z-order) keys, float bit-flips for radix sorting, octagonal approximate distance. See THIRD_PARTY_NOTICES.md for provenance and licenses.

For agents

Building a little three.js game or demo? Two ways in:

  • Skill: skills/flit/SKILL.md — copy the skills/flit/ directory into your agent's skills path (e.g. .claude/skills/, ~/.code_puppy/skills/). It carries the 30-second integration recipe, the MCP option, measured performance envelope, and contributor rules.
  • MCP server (no code needed): npm run mcp, or point your client at it:
json
{  "mcpServers": {    "flit": {      "command": "npx",      "args": ["vite-node", "mcp/server.ts"],      "cwd": "<path-to-this-repo>"    }  }}

Tools: flit_info, flit_reset (gravity/restitution/ground/bounds/ settleSpeed/heightfield), flit_spawn (rain/explosion/grid/fountain presets), flit_add_particles, flit_step, flit_state, flit_raycast — step/state return flat xyz positions shaped for InstancedMesh syncing plus settledCount, so you can watch a pile go quiet.

Usage

bash
npm i flit-physics
ts
import { World } from 'flit-physics';
const world = new World({ restitution: 0.4 }); // gravity and a floor at y=0 included
const ball = world.addParticle({ position: [0, 10, 0], radius: 0.5, mass: 1 });
// fixed timestep, e.g. from your rAF loopworld.step(1 / 60);
// sync to three.js: positions is a live Float32Array, 3 floats per particlemesh.position.set(  world.positions[ball * 3],  world.positions[ball * 3 + 1],  world.positions[ball * 3 + 2],);

Terrain and sleep are one option each:

ts
import { Heightfield, World } from 'flit-physics';
const terrain = new Heightfield({ rows: 64, cols: 64, cellSize: 1, heights });const world = new World({ groundY: null, heightfield: terrain });// bodies collide with the terrain and settle on it;// world.raycast(...) reports terrain hits as index -1
// sleep is on by default (settleSpeed 0.01 m/s); opt out per world:const wideAwake = new World({ settleSpeed: 0 });

Develop

bash
npm installnpm test        # vitestnpm run build   # tsc -> dist/npm run bench   # broadphase + full-step micro-benchmarksnpm run demo    # three.js demo scene (vite dev server)

Benchmarks

Deterministic seeds; numbers from a local dev machine, recorded at commit time (see commit messages for the full series, including rejected designs).

  • bench/broadphase.bench.ts — broadphase only, N=4096: xor-hash 3.4–3.7 ms (444 pairs, ~90% collision bloat) → Morton keys with ordered probing 3.5–3.7 ms (234 pairs, exact).
  • bench/world.bench.ts — full World.step, N=1024: xor-hash 0.85 ms/step → Morton ordered-probing 0.83 ms/step → 0.93 ms/step with the sequential-impulse velocity solver (4 iterations + LUT friction). Exact keys, real contacts, +11%.
  • bench/scaling.bench.ts — two regimes. Fixed box (density rises): pairs scale ~N² from crowding physics. Scaled box (constant spawn density): flat O(N) ≈ 1.2 ms per 1000 through N=8000 while bodies are scattered. With gravity on, everything rains into a dense floor pile over ~2-4s (WARMUP=240 to reproduce) — and since 0.3.0 the pile SETTLES: 34.2 → 9.2 ms/step at N=8000 (3.7x, inside the 60fps budget) as bodies fall asleep bottom-up. Settled bodies are skipped by integration, planes, and mutual contacts until a >1 m/s impact wakes them. (docs/issues/001 — resolved; brief kept for the analysis and the remaining ideas: warm starting, adaptive iterations.)
  • bench/raycast.bench.ts — 2000 seeded rays at N=4096: 63–68 µs/ray (vs ~160 µs for a brute-force O(N) oracle at the same density; the grid walk pulls further ahead as N grows).
  • bench/morton-libs.bench.ts — codec bake-off vs npm libs (npm run bench:libs): ours 3.8 ns/encode, fast-morton MB 26.1, fast-morton LUT 43.8, @thi.ng/morton 539.9. In-house wins; the libs stay as devDependencies purely so the bake-off stays runnable.
  • bench/threaded.bench.ts — WorkerWorld over a real worker thread (SAB ping-pong): kick() costs 0.014 ms on the main thread; with a 4 ms render workload a frame costs 8.84 ms pipelined vs 14.96 ms serial at N=8192 (113 fps vs 67). At N=1024 the 0.8 ms step hides entirely behind render (4.03 vs 4.80 ms).
  • Rejected on measurement (see commits): 63-bit BigInt keys (13× alloc regression); sorted-array + binary-search broadphase (1.4× slower than Map probing in JS — negative result recorded).

Roadmap

  • Morton-ordered broadphase cells — done, measured, shipped
  • three.js demo scene — npm run demo, 220 balls in a box (?terrain for heightfield hills, ?n=8000 for load testing)
  • Worker-threaded stepping — WorkerWorld: kick/waitForUpdate, SAB ping-pong zero-copy rendering
  • Settled-pile solver cost — per-body sleep shipped (34.2 → 9.2 ms at N=8000); issue brief kept for the remaining ideas (warm starting, adaptive iterations)
  • Heightfield terrain — shipped, no meshing; hooks left for more collider shapes
  • Raycast coarse occupancy filter (skip empty space faster) if ray-heavy workloads show up — see commit 3173a6f for the analysis
  • WebGPU compute backend once the CPU reference settles

License

MIT — see LICENSE. Third-party portions and their licenses are listed in THIRD_PARTY_NOTICES.md.

來源:README.md,提交 754e05f

工具

0
SourceWeft 尚未在沙箱中啟動過該軟體包,因此其工具未知。

版本歷史

1
  1. v0.2.2最新Sep 16, 2026

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