研究生层面的罗斯商学院双学位项目
密大方面提供的硕士学位（授课型）：

• 管理 30 credits
• 供应链（管理）30 credits
• 商务分析 (BA) 36.5 credits 挺适合转商科，量化分析（programming required）

密大的一年放在三年学制的最后一学年的6月~来年5月，我们认对方6学分，对方认我们Gateway
准入条件：

• 密院研究生
• 对方的线上面试方式

学费，5w$~6w$ （如果承认学分学费可以打折），生活安娜堡预计1000$/m，饮食1000$/m， 杂项500$/m

可以提供找工作的签证机会（利好留美发展者）
但是时间会和秋招冲突，会给国内找工作面试带来困难

发学位证书的时间在两边并不统一

26级包括专硕招生（和双学位挂钩）

https://dl.acm.org/doi/pdf/10.1145/3620666.3651367

Background

Qumode

Qumodes are a different way of carrying and manipulating quantum information than qubits.

就像二进制在电脑中的encoding方式，总共n bit的内存，那必然只能有 $2^n$ 种内存state，这是由二进制0或1的特性决定的。然后在让我们看qubit和qumode

States for qubits

If we go to qubits, not much in this picture changes. While a qubit has infinitely many possible states, it turns out that you should look at what is called the basis of the state space, which loosely said means that you should find the minimal number of states in which you can express every other state. For a qubit, this turns out to be two, for example the up state and the down state. To use the language from above, each qubit therefore has 2 ‘possible assignments’, and you have n of them, so by the arguments presented above, there are $2^ⁿ$ unique states. Because we are doing quantum mechanics, superpositions of these states are also allowed, but that doesn’t change the picture: the dimensionality of the system is still $2^ⁿ$.

Qubits是由单个光子的量子态决定的，的存储维数限制依然是 $2^n$

States for qumodes

Squeezing gates

是的，我在还没读完p10和p11的同时，开了p12的坑

这里摘写若干我认为有价值的原文或者书评

• “纯粹之爱”

  韩教授全文诉说的“爱欲”的最高形态被称作“纯粹之爱”，在这里他并没有直接做定义，我们则从斯滕伯格的爱情三元素理论开始理解即纯粹之爱需要包括激情、亲密和承诺才能构成“爱”的理性与感性并存。
  爱情既包括感性的甜蜜纵容，又有着理性的升华包容；既有着肉欲的激情冲动，还含有灵魂的契合相融。互相保有自主的空间，却也有着舒适的亲密感。相互信任并且互相理解。同时具有着“缘分”，这个缘分既能保证相遇，又能保证不分离。

• “爱欲”

  它包含在纯粹之爱之中也就是说是一种“爱的原发状态”，一种不完满的“爱”，它是主体在产生对性状的自我意识之后具备的“知性”（Verstand）能力，即能够综合分析判断“情”和“欲”的范畴，将这两个经验表象通过范畴结成的大网捕捉到自我之中形成观念。

• “情感”

  顾名思义这是爱的经验，是自我与他者之间产生的“情愫”，是“爱欲”被充实的质料。

• “性”

  这是一种主观的体验，也就是说它是在进行性行为之后的一种“体验”。

这四者之间的关系

纯粹之爱—超验（理性），爱欲（性欲）－先验（知性），情感（激情）－经验（感性），性体验（性满足）－后验（经验）

韩教授开篇便提出了“导致爱情危机的不仅仅是对他者的选择增多，也是他者本身的消亡……伴随着个体的‘自恋‘情节的加深。”这一直接观点，一针见血刺疼了时代的根源，当然我们还要区分“自恋”与“自爱”这对范畴。“自恋（Narzissmus）与自爱（Eigenliebe）不同。自爱的主体以自我为出发点，与他者明确划清界限；自恋的主体界限是模糊的整个世界只是自我的一个倒影。”也就是说自爱的人能够清晰的界定主体与客体（这个客体是具有自由意志的）、自我与他者，并且其认识标准和世界观是多元性的，此时他者对于自我而言是具有“意义”的。而“自恋”的人对于主客体之间没有能动的区分能力，无法认识到他者身上的差异性，在对于时空中的存在只是“自我”的缩影，即他者此时只是自我的强行解释。

在这里我们要明确的是，每个人在追求他者的时候都会或多或少的产出“愿望”，愿望不可避免，但其形式与质料必然存在差异。我们无需担心缺乏信息的“选择自由”会面临着“愿望的终结”，恰恰相反，因为愿望是相对于他者而言的愿望，“被剥夺的否定性滋养着愿望”。信息过多会导致他者无法被“理想化”，信息的缺失恰恰能缔造他者的存在，因为信息是肯定性的，而他者的塑造确是否定性的。“色情片就是通过将视觉信息无限倍地扩大来毁掉人们对情欲的想象的。”这也是为什么一旦接受了大量色情产物后面对真实的性行为时便不会得到健康完整的“性满足”，即齐泽克讨论的“快感”。

受爱欲驱使的灵魂能够创造出具有普遍价值的美的事物和行为。爱欲并非普遍认为的那样与感官和快乐对立。只是因为今天的爱情被世俗化为性爱，爱欲才逐渐远离了它们。”世俗化的爱情不再具备古希腊人所憧憬的美好品质——勇敢、激情，理性。而爱欲与生活甚至是政治息息相关，这也就引导我们来到了第六章的标题——爱欲政治学。缺乏这些品质的爱欲堕落成为了单向度的欲望，也就是对色情的向往。色情升级了自我的自恋倾向，使自我丧失了完成“爱情双人舞”的能力，因为自恋的自我拒绝与他者的融合甚至放弃认识他者。失去了对爱欲的追求，也就引伸联系到在现实生活中失去了创新和革命的源泉。韩炳哲教授在本章的最后说到：“表达出来的爱欲，代表着对其他个体生命形式和社群组织的革命性渴望。是的，它维护着人们对未来的忠诚。”

从书中全文我们能够清晰的看出，从历史传统中就出现定义的“纯粹之爱”正在面临威胁与危机，甚至是濒临“灭绝”的。女性主义哲学家波伏娃在她的著作《第二性》中说过“真正的爱情应该建立在两个自由的人相互承认的基础上。”，顾名思义，成立的条件为“自由”和“相互承认”，也就是同为主体和相互融合。然而这个条件在如今已然很难达成。不仅是个人主义自由主义的主流价值观扼杀了对“他者”的承认，资本社会下的景观社会也让爱欲变成了一种单向度的景观，虚假的需要代替了真实的需要，而人们无法辨别，不得不被迫的接受这个“当下的愿望”，甚至失去了抗争和批判的能力，把这一切当做“应有之义”。在最后我们引用一下张世英老先生的“人生四境界”与开篇的四种概念相对应。先生提到第一个也就是最低的境界是“欲求的境界”，在这个境界中人只知道满足个人生存所必须的最低欲望，其与世界的关系属于“主客未分”。第二是“求实的境界”，这种境界则进入了初步的“主—客关系”，人有了自我意识，能够分清自我与他者，寻求对“外物”的进一步理解，对于同为人的他者产生了“同类感”，以及道德意识。以此要进入第三种境界——“道德的境界”，其尚属于主—客关系的结构，但此时自我以对万物一体相通的领悟作为自己精神追求的最高目标，并为止奋斗不已，也就是具备了一种不惧生死的理性的追求精神但身处道德境界的过程中仍然存在着理想和现实之间的距离，所以仍有着主客的区别，也就是否定之否定并没有彻底完成没有达到破立扬弃之后的“融合”与新生。但道德的实现和完成，就是道德的极致，也就是道德境界的结束，便来到了第四种境界——“审美的境界”，这是一种高级别高纬度的“主客融合”也就是我们常说的“天人合一”的超越结构，它包摄道德却又超越道德人们不再出于道德义务的强制来做某事，不再只是为了“应该”（也就是前文所说的规训社会的代名词）而做某事，而是完全出于一种融合的自然的境界之中。这四种境界分别对应了性满足、情感、爱欲和纯粹之爱。

感觉也基本解释了为什么我对于某些恋人之间流露出的气场感到恶心，而有些则让我平静和享受，这是对于爱的审美在起作用。

看书的时候听的是玛丽亚凯莉的《Without You》和惠特尼休斯顿的《I Have Nothing》。感觉当代真的很难再找到配得上这两首歌的爱情了。

Useful Link

https://www.youtube.com/watch?v=cLWgjienc_s
https://github.com/habamax/vim-godot

Implementation

Nvim

安装vim-godot, 并且不用手动在配置中开启

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{ "habamax/vim-godot", event = "VimEnter" },

在init.lua中，自动识别godot项目并且开启服务器端口

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-- for godot
local gdproject = io.open(vim.fn.getcwd() .. "/project.godot", "r")
if gdproject then
	io.close(gdproject)
	vim.fn.serverstart("./godothost")
	vim.notify("Open godot project")
end

在dap/nvim-dap/dap-godot.lua中配置了dap debugger

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local dap = require("dap")
dap.adapters.godot = {
	type = "server",
	host = "127.0.0.1",
	port = 6006,
}
dap.configurations.gdscript = {
	{
		type = "godot",
		request = "launch",
		name = "Launch scene",
		project = "${workspaceFolder}",
		launch_scene = true,
	},
}

其余dap的默认配置沿用之前的。
lsp:

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lspconfig.gdscript.setup({
	capabilities = capabilities,
	on_attach = on_attach,
})

Godot Editor

参考视频中在编辑器配置中设置外部编辑器即可。

Reference: http://nilssondev.com/posts/my-journey-with-physics

Things Jolt Does Better

• Upfront about allocations. Jolt expects that you tell it how much memory will be needed for the simulation, which definitely results in better performance since Jolt won’t be reallocating a lot of memory during the simulation. (Jolt does provide a way of doing allocations dynamically, so upfront allocations isn’t a requirement)
• Better multi-core support. Out of the box Jolt will utilize multiple cores very well, whereas I’ve found that PhysX doesn’t utilize multiple cores all too well. Jolt was designed with this in mind whereas PhysX wasn’t.(多线程原生支持)
• Explicitly reference counted objects. When you create a new object in Jolt you’ll most likely get a “Ref” instance back. PhysX does utilize reference counting, but it’s not as apparent as it is with Jolt because PhysX returns pointers to most objects.
• Multiple collision shapes per body. Jolt handles this by providing a “compound” shape, which you construct from multiple other shapes, whereas PhysX just lets you add an arbitrary amount of shapes individually to any body. I’m not entirely sure how PhysX handles this internally, but I do know that Jolt computes these shapes into a single more optimal collision shape, meaning that collision tests are most likely more optimal.（最佳碰撞划分）
• Better Multi-Threaded access. Jolt was designed with this in mind, and most methods in Jolt supports being accessed by multiple threads at once, whereas PhysX requires you to make use of a “Task” system, which is poorly designed in my opinion.（多线程）
• Jolt achieves a higher simulation fidelity / better simulation quality out of the box. Jolt gives us some really good simulation values out of the box, whereas PhysX requires us to play around with a lot of parameters in order to get a high quality simulation.

Things PhysX Does Better

• PhysX has better simulation performance out of the box, whereas Jolt requires more careful setup and usage of the API in order to achieve good performance. Jolt does however gives more opportunities for optimizations, and can achieve better performance overall, but it does require more work（物理准确性方面更好）
• PhysX supports GPU-based simulation by utilizing the CUDA architecture, sadly this is limited to Nvidia GPUs only （可以显卡加速）
• The PhysX Visual Debugger (PVD) supports recording the simulation over the network, Jolt requires us to record the simulation to a file
• The PVD doesn’t just visually record our simulation, but also records the parameters of the simulation at any given moment, allowing us to inspect the parameters more easily. Jolt on the other hand only records the simulation visually.
• PhysX offers us more control over mesh collider cooking, giving us more parameters to mess around with
• PhysX has far better documentation overall, since it’s an older and more widely used API there’s more resources on it online, whereas Jolt lacks online documentation. The developer of Jolt is very active on the GitHub repository though, and has been very helpful with any issues I’ve encountered, or any questions I’ve had about the usage. This isn’t the case with the PhysX developers.（文档更完备）
• Achieves better performance when adding a lot of new bodies during the simulation. Jolt requires us to manually optimize the broadphase structure, and doing that when there’s already a lot of bodies in the broadphase can result in slowdowns temporarily.（在运行时创建物理物体更方便（？））

Objective

I use a hyprland plugin called hyprscroller to emulate the scroll functionality of PaperWM.
However, I can’t get whether I am in row mode or column mode, so I want to show the mode status on my waybar.

Related issue: https://github.com/dawsers/hyprscroller/issues/57

Implementation

I wrote two shell script under ~/.config/waybar/

• scroller_mode_listener.sh, responsible for listening the IPC message of hyprscroller and store that information in a tmp file. This script can be executed in hyprland config file using exec-once

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  #!/bin/bash MODE_FILE="/tmp/hyprland_scroller_mode" function handle { if [[ ${1:0:8} == "scroller" ]]; then if [[ ${1:10:9} == "mode, row" ]]; then echo "Row" > "$MODE_FILE" elif [[ ${1:10:12} == "mode, column" ]]; then echo "Column" > "$MODE_FILE" #else #echo "" > "$MODE_FILE" fi hyprctl dispatch submap reset && pkill -SIGRTMIN+8 waybar # update the widget on waybar fi } # Initialize the mode file echo "" > "$MODE_FILE" socat - "UNIX-CONNECT:$XDG_RUNTIME_DIR/hypr/$HYPRLAND_INSTANCE_SIGNATURE/.socket2.sock" | while read -r line; do handle "$line"; done

• scroller_mode_reader.sh, responsible for reading the tmp file and return the json value to waybar to change the shown icon.

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  #!/bin/bash MODE_FILE="/tmp/hyprland_scroller_mode" current_mode=$(cat "$MODE_FILE") if [[ "$current_mode" == "Row" ]]; then icon="Row" percent=0 class="mode-row" elif [[ "$current_mode" == "Column" ]]; then icon="Column" percent=100 class="mode-column" else icon="Row" percent=0 class="" fi echo "{\"text\":\"$icon\", \"tooltip\":\"Scroller Mode: $current_mode\", \"class\":\"$class\",\"percentage\": $percent}"

Then add the custom module to waybar in config file (we use signal to update the module):

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"custom/scroller-mode": {
    "exec": "~/.config/waybar/scroller_mode_reader.sh",
    "return-type": "json",
    "interval": "once",
    "signal": 8,
    "on-click": "hyprctl dispatch submap reset && pkill -SIGRTMIN+8 waybar",
    "format": "{icon}",
    "format-icons": ["", ""],
}

Also remember to add the module to the bar:

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"modules-right": ["custom/scroller-mode", "cpu", "memory", "temperature","battery","clock","custom/power"],

Then everything is done

特别值得一提的是，反学校文化与工人阶级的车间文化有很多根本的相似之处——大部分反学校文化成员注定成为工人阶级的一分子。尽管我们必须考虑地区和职业的差异，但工人阶级车间文化的核心是：无论条件多么艰苦，上级指导多么苛刻，人们总是在寻求意义，并为之搭建参照体系。他们运用自己的才能，即使在最受他人控制的活动中也能寻求到乐趣。矛盾的是，他们能从死板的工作经历中活出一种生动的文化，这绝不是对失败的简单反映。在反学校文化中，我们亦能看到这种在令人疏离的环境中立足的根本理念，同时，他们都试图在枯燥的制度下编织出属于自己的兴趣和娱乐。这些文化不只是人类与不快之间层层叠叠的填充物。这些文化自有其逻辑，使用各种技巧、动作和行为，以达到特定目的。

车间文化和反学校文化一样，基于相同的组织性团体。非正式群体奠定了所有其他可能的文化要素。正是这个团体生成并传播着那些与官方权威争夺符号控制和真实空间的策略。这个非正式组织无处不在，并将车间文化与中产阶级工作文化区分开来。

家伙们”也从根本上排斥文凭这个概念。对他们而言，
正如制度界定的那样，“文凭”就是知识权力的爪牙。因为他
们反抗知识，所以他们也必然抵制和怀疑文凭并使其失去信
用。正如其他情况那样，使正式标准失去信用的主要方式是用
非正式的模式“看透这些标准，了解它是如何发挥作用的”。
他们靠经验，至少是听来的经验与正式定义周旋。在一定程度
上，他们真的认为自己懂的更多。不做功课、没有文凭照样能
过活，因为真正有用的是“了解点世界”“脑子清楚”、必要
的时候“撒手”。当然，成人工人阶级世界中，英国车间里盛
行的实用氛围及对理论的不信任都大大强化了他们的这种观
点。这一基本文化资源在学校的分化过程中被磨炼得更为坚
固。
“家伙们”从心底里认为肯定有更简单的方法。文凭对他
们来说似乎是对直接行动的偏离或替换。他们觉得他们总能
“在岗位上”展示必要的能力，而且做一件事总比描述它或在
考试中陈述它容易，或者比正式说明它容易。

Basic Plugin File

Just take my ColorfulDiff.nvim as an example.

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$ tree ColorfulDiff.nvim

├── LICENSE
├── lua
│   └── tint.lua
├── plugin
│   └── hello.lua
└── README.md

The lua scripts in plugin/ will be executed automatically and the scripts in lua/ will be called when required

To let nvim load the plugin, use lazy.nvim.
Add this line in your lazy.nvim configuration:

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{ dir = "/home/cyl/nvim_plugins/ColorfulDiff.nvim/" },

Then this plugin will be loaded on start.

How To Use API

Use :Telescope help_tags or short hand <Ctrl-h> to open the help search panel.
If I want to know the API regarding the highlight:

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 *nvim_buf_add_highlight()*
nvim_buf_add_highlight({buffer}, {ns_id}, {hl_group}, {line},{col_start},{col_end})
    Adds a highlight to buffer.

    Useful for plugins that dynamically generate highlights to a buffer (like
    a semantic highlighter or linter). The function adds a single highlight to a buffer. Unlike |matchaddpos()| highlights follow changes to line
    numbering (as lines are inserted/removed above the highlighted line), like signs and marks do.
    ...

To use this api:

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vim.api.nvim_buf_add_highlight(...)

Test

Create a new folder called test
And create the test lua file

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describe("colorfulDiff", function()
	it("can be required", function()
		require("colorful_diff")
	end)
	it("...", function()
		...
	end)
	...
end)

然后在该文件中按<leader>pt运行测试

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:h lua-plugin
:h write-plugin

Background Introduction to the Methodology: Motion Planning with Transformers

Motion planning is a critical aspect of robotics, enabling robots to navigate complex environments while avoiding obstacles and achieving specific goals. Traditional motion planning methods, such as sampling-based planners, often struggle with high-dimensional tasks and long-term planning due to their reliance on random sampling and tree expansion techniques. Recent advancements in deep learning have led to the development of neural motion planners, which leverage neural networks to learn from demonstrations and generate efficient paths. Among these, the Motion Planning Transformer (MP-Former) framework stands out by treating motion planning as a sequence-to-sequence generation task, utilizing the transformer architecture to capture long-term dependencies and improve path quality.

Experimental Method: Evaluation of MP-Former

In this study, we conducted a series of experiments to evaluate the performance of the MP-Former framework in generating collision-free, near-optimal paths for robotic manipulation tasks. The experiments were designed to compare MP-Former against traditional sampling-based planners and previous neural motion planners in terms of efficiency, path quality, and robustness.

Experimental Details

Sample Information

The experiments utilized a synthetic dataset comprising various complex environments represented as point clouds. Each environment included multiple obstacles and varying configurations to challenge the motion planning capabilities of the algorithms.

Procedures

1. Dataset Preparation: A synthetic dataset was generated, consisting of 100 unique environments with diverse obstacle arrangements. Each environment was represented as a point cloud, simulating real-world scenarios.
2. Algorithm Implementation: The MP-Former framework was implemented alongside traditional planners (e.g., RRT and A*) and previous neural planners (e.g., MPNet). Each algorithm was configured to operate under the same conditions for a fair comparison.
3. Path Generation: For each environment, the algorithms were tasked with generating paths from a predefined start configuration to a goal configuration. The MP-Former generated paths using its transformer-based architecture, while the traditional planners utilized their respective sampling and tree expansion methods.
4. Data Recording: The following metrics were recorded for each generated path:

• Path length (in meters)
• Number of collisions encountered
• Time taken to generate the path (in seconds)
• Success rate (percentage of successful path generations without collisions)

Instruments and Equipment

• Computational Resources: Experiments were conducted on a high-performance computing cluster equipped with NVIDIA GPUs to facilitate the training and evaluation of the MP-Former framework.
• Software Tools: The implementation of the algorithms was carried out using Python with libraries such as TensorFlow for neural network training and Open Motion Planning Library (OMPL) for traditional planners.

Repetitions and Parallel Experiments: Each algorithm was executed 50 times per environment to ensure statistical significance. The results were averaged across these repetitions to provide a comprehensive evaluation of each method’s performance.

Results and Analysis

The results of the experiments were compiled into tables and graphs to illustrate the performance differences between the MP-Former and the other planners.

Table 1: Performance Metrics Comparison

Justification of Methodology

The choice of using the MP-Former framework for motion planning tasks is justified by its ability to efficiently generate high-quality paths while maintaining robustness in complex environments. By treating motion planning as a sequence-to-sequence generation task, MP-Former effectively captures long-term dependencies, reducing the risk of falling into equilibrium points that can hinder traditional planners. The experimental results demonstrate that MP-Former outperforms traditional methods in terms of path quality, efficiency, and success rate, making it a valuable approach for robotic motion planning.

Reference

• Li, B., Wang, R., Chen, Y., Feng, B., Zhou, Q., & Bi, Y. (2025). Motion Planning Transformers (MP-Former): Treat Motion Planning as Sequence-to-Sequence Generation. IEEE International Conference on Robotics and Automation (ICRA).
• Karaman, S., & Frazzoli, E. (2011). Sampling-based algorithms for optimal motion planning. The International Journal of Robotics Research, 30(7), 846-894.
• Gammell, J. D., Srinivasa, S. S., & Barfoot, T. D. (2015). Batch informed trees (BIT*): Sampling-based optimal planning via the heuristically guided search of implicit random geometric graphs. IEEE International Conference on Robotics and Automation (ICRA).

For what

nvim用了两年了，插件和自己的配置真搞了不少，但是很容易出现：

我记得这个功能好像通过什么插件实现了来着，插件叫什么名字来着？（对着一堆名字牛头不对马嘴的插件望洋兴叹）对应的指令是啥来着？快捷键是啥来着？

装了若干个实现类似功能的插件。。。

这个插件更新了？不会用辽！

我当时为啥装这个插件来着？

我刚一不小心触发了个啥玩意儿的快捷键？

……

综上，决定按照功能分块梳理一下自己的插件和快捷键配置。顺带整理一下插件系统，把不要的统统删咯。

Guide

页面

Bufferline

• 左右循环移动bufferCtrl+h/l
• 选择buffer关闭Leader+bc

Ctrl+w（重新映射了一些C-W指令）

• 关闭当前分屏sc
• 关闭所有其他分屏so
• 垂直分屏sv
• 分屏sh
• 移动指针处于的窗口Alt+h/j/k/l
• 窗口比例调整Ctrl+上/下/左/右
• 一键等比例s=

Terminal

打开浮动终端Leader+t
垂直分屏打开终端Leader+vt
退出: 终端输入模式下Esc

搜索

搜索文件Ctrl+p
搜索文件内容Ctrl+f
搜索帮助文档Ctrl+h

编辑

指针移动/跳转/导航

跳回上一个位置Ctrl+o
重做跳回Ctrl+i

文件管理

打开文件树F2
重命名文件/文件夹r
新建文件/文件夹a
删除文件/文件夹d
折叠所有文件夹W
搜索S

LSP

显示效果增强

插件开发

运行_spec测试<leader>pt