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Vim是一款强大的文本编辑器，可以通过适当的配置成为一个功能强大的集成开发环境（IDE）来支持各种编程语言。在本文中，我们将介绍如何使用Vim来配置C/C++和Erlang的IDE开发环境，以便您可以更高效地进行编码和调试。
安装Vim
首先，您需要安装Vim编辑器。根据您的操作系统，您可以通过包管理器或从Vim官方网站(https://www.vim.org/)下载安装程序进行安装 ↗下载安装程序进行安装)。
配置Vim
一旦Vim安装完成，我们可以开始配置它以支持C/C++和Erlang开发。
为了使Vim支持C/C++开发，我们需要安装并配置一些插件。以下是一些常用的插件：
您可以使用插件管理器来安装这些插件。在这里，我们将使用Vim插件管理器vim-plug
编写c语言的vim插件cvim
vim的脚本插件，编写c程序的好助手，打造vim的IDE！！
Stm32L0 STM32CUBE中UART和使用LPUART1以及串口不进中断问题解决
/* 开启串口1中断 */ LL_USART_EnableIT_RXNE(LPUART1); /* 开启串口2中断 */ LL_USART_EnableIT_RXNE(USART2);
告别卡顿！用U盘给你的MacBook Pro 2015+来一次macOS Monterey的‘大扫除’式安装
每次系统升级后，老款MacBook Pro用户最常抱怨的就是"怎么比之前更卡了？"——这不是错觉。系统迭代过程中积累的缓存文件、残留配置和隐形服务，就像房间角落堆积的灰尘，拖慢了整台机器的响应速度。而通过U盘进行的macOS Monterey全新安装，本质上是一次彻底的"数字大扫除"：它不仅替换系统文件，还会重建所有索引、清理磁盘碎片，甚至能解决某些玄学般的卡顿问题。对于2015款及之后的MacBook Pro用户，这种安装方式能让老设备重获新生。
uniapp 开发微信小程序请求问题 , 真机调试正常,开发工具正常,但是使用扫码预览接口都是不通的,发布到体验版也是请求失败
uniapp开发小程序体验版接口请求失败（真机可以）
【环境搭建】使用openSfM+MVSNet重建稠密点云
本帖详细介绍了如何使用openSfM+MVSNet对真实采集的数据进行稀疏重建与稠密重建，并针对一些真实场景下可能遇到的问题进行了整理。
CTF实战：Wireshark数据包分析从入门到精通（附经典题型解析）
本文详细介绍了Wireshark在CTF比赛中的数据包分析技巧，从基础抓包到高级协议解析，涵盖HTTP流量分析、USB键盘数据还原等实战案例。通过经典题型解析，帮助读者掌握精准过滤、文件提取等核心技能，提升CTF竞赛中的网络流量分析能力。
三种 绘制奈奎斯特曲线 的方法
如何绘制Nyquist曲线 提供三种绘制方法，其中使用最广泛的是概略图绘制方法 ①解析法绘制 ②概略图法绘制 ③MATLAB绘制
从零做出可发布的2D冒险游戏！Unity全流程实战指南：从资源搭建到打包发布，新手也能跟着做
项目配置→资源导入→核心模块开发→关卡设计→调试→打包发布；玩家控制→敌人AI→道具系统→UI系统→游戏管理器→音效系统；新手开发重点：先实现核心玩法（移动、跳跃、攻击），再补充UI、音效、存档等辅助功能；调试是关键：每实现一个功能就测试，避免最后一堆bug难以定位。从零做一款完整的2D冒险游戏，能快速提升你的Unity综合能力——不仅能掌握C#编程、2D物理、UI系统，还能理解游戏开发的完整流程。先做出基础版，再慢慢迭代优化，你会发现游戏开发并没有想象中那么难。
【OpenClaw从入门到精通】第63篇：A股智能分析实战——用OpenClaw搭建个人量化研究助手（2026实测版）
2026年，OpenClaw已成为量化投研圈的现象级工具，八家头部券商密集发布专题研报力证其价值。传统量化投研面临技术门槛高、重复劳动多、策略迭代慢、非结构化数据处理弱四大痛点，而OpenClaw通过“AI智能体+金融Skill+量化API”的融合，实现了从数据获取、策略生成、回测验证到持仓监控的全流程自动化。本文基于2026年最新实测经验，完整呈现个人量化研究助手搭建：从阿里云部署、阿里云百炼API配置（含解析失败解决方案）、Tushare数据源接入，到PB-ROE价值选股、研报复现、自动化回测、持仓监控
温度PID控制PLC程序
温度PID稳定控制程序，适用于欧姆龙PLC，对其他PLC也具有很大参考价值
深度学习工具包 DeepLearningToolbox
深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[](https://bitdeli.com/free "Bitdeli Badge")
老版QQ原生表情gif动态
这是老版的QQ表情，不过全是动态的的，Windows照片查看器是不会动的，可以右击用浏览器打开查看，再说一遍是老版的，只是分享给宁愿用老版也要动态的人。
精雕软件(JDpaint) 5.5 绿色版
精雕软件(JDpaint) 5.5 绿色版精雕软件(JDpaint) 5.5 绿色版精雕软件(JDpaint) 5.5 绿色版
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