原型 · 预览版:收录官方注册中心里的全部服务器,每 30 分钟同步一次。评价和账户功能将在后续阶段推出。
PopNx MCP为使用 AI 的人准备的 MCP 服务器 · 用大白话讲清楚

Flit Physics

作者:brashler版本 0.2.2i★ –i在你的电脑上运行
注册名称io.github.brashler/flit

安装

概览

作者提供的说明(英文原文)

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

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

  • 连接方式: 在你的电脑上运行
  • 传输方式: stdio
  • 分类: 其他

安装

选择你使用的 AI 应用,然后复制设置。

安装之前

  • 基础检查:1 项中通过 1 项
  • 在你的电脑上运行,拥有你的用户账户的权限(文件、网络、应用)。
  • 注册中心没有列出需要填写的密钥。

你需要

  • npm已安装 Node.js(用于 npx 命令)

你用的是哪个应用?

在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "flit": {
      "command": "npx",
      "args": [
        "-y",
        "flit-physics"
      ]
    }
  }
}

在终端中运行这条命令

claude mcp add flit -- npx -y flit-physics

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "flit": {
      "command": "npx",
      "args": [
        "-y",
        "flit-physics"
      ]
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "flit": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "flit-physics"
      ]
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上,无需账户。

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。

它能做什么

它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日

  • ✓ 通过

    作者与仓库一致

    注册名称已通过 GitHub 账户 brashler 验证,该账户拥有此仓库

  • … 尚未检查

    仍在维护

    我们还没有从 GitHub 读取这个仓库

  • … 尚未检查

    有许可证

    我们还没有从 GitHub 读取这个仓库

  • – 不适用

    端点有响应

    它在你的电脑上运行,没有互联网端点

  • – 不适用

    使用 OAuth 登录

    在你的电脑上运行,改用本地设置

  • … 尚未检查

    工具描述中没有隐藏指令

    它在你的电脑上运行;我们只能读取互联网端点的工具列表

与“其他”中的其他服务器比较i

  • 星标数多于此分类中其他 32545 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 32545 个服务器的 34% 更新
  • 基础检查结果等于或优于此分类中其他 32545 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。

常见问题

Flit Physics 安全吗?

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。

它需要哪些密钥或代码?

注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。

它在我的电脑上运行还是在云端?

它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。

它还在维护吗?

GitHub 仓库最后更新于–,最新版本(0.2.2)于2个月前进入注册中心。

哪些应用可以使用它?

支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js。

有什么可以替代它?

同一分类中有:Spec Driven Development, Remote Desktop Commander, Python MCP

替代选择

同一分类中其他作者的服务器,按星标数排序。

如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Spec Driven Development

作者:formulahendry

MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.

在你的电脑上运行★ 440MIT14个月前更新
安装

Remote Desktop Commander

作者:desktop-commander

Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

在云端运行★ 211昨天更新
安装

Python MCP

作者:Files-com

Local-only Python MCP server for the Files.com API.

在你的电脑上运行今天更新
安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

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:
{

"mcpServers": { "flit": { "command": "npx", "args": ["vite-node", "mcp/server.ts"], "cwd": " " } } }

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

npm i flit-physics
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 loop world.step(1 / 60);

// sync to three.js: positions is a live Float32Array, 3 floats per particle mesh.position.set( world.positions[ball * 3], world.positions[ball * 3 + 1], world.positions[ball * 3 + 2], );

Terrain and sleep are one option each:

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

npm install

npm test # vitest npm run build # tsc -> dist/ npm run bench # broadphase + full-step micro-benchmarks npm 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.

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