标签：#c

"I think it's fair to say that Phase 3 did not contemplate this."

GitHub热门项目 | A beautiful, private, local-first personal finance tracker. Investments, net worth, spending, and simulations. | Stars: 8,043 | 201 stars today | 语言: Rust

GitHub热门项目 | Accelerating new GitHub Actions workflows | Stars: 11,793 | 4 stars today | 语言: TypeScript

GitHub热门项目 | Traycer: Nerve Center for Agentic Coding | Stars: 409 | 71 stars today | 语言: TypeScript

GitHub热门项目 | A terminal for a more modern age | Stars: 73,127 | 93 stars today | 语言: TypeScript

GitHub热门项目 | An advanced guide which might benefit you a lot 🎉 . 人生进阶指南 离谱的人生 离谱的英语学习指南/英语学习教程/英语学习/学英语 | Stars: 55,583 | 148 stars today | 语言: JavaScript

GitHub热门项目 | Open-source intelligence for the global theater. Track everything from the corporate/private jets of the wealthy, and spy satellites, to seismic events in one unified interface. Hook an AI agent up to have it parse through data and find previously unseen correlations. The knowledge is available to all but rarely aggregated in the open, until now. | Stars: 9,650 | 30 stars today | 语言: Python

GitHub热门项目 | Source code for Unturned, a free open-world zombie survival sandbox game. | Stars: 1,776 | 541 stars today | 语言: C#

GitHub热门项目 | A collection of DESIGN.md files analysis by popular brand design systems. Drop one into your project and let coding agents generate a matching UI. | Stars: 99,081 | 1,569 stars today | 语言:

GitHub热门项目 | A collection of notebooks/recipes showcasing some fun and effective ways of using Claude. | Stars: 46,869 | 194 stars today | 语言: Jupyter Notebook

GitHub热门项目 | Fully autonomous AI Agents system capable of performing complex penetration testing tasks | Stars: 19,091 | 454 stars today | 语言: Go

GitHub热门项目 | An evolving how-to guide for securing a Linux server. | Stars: 28,853 | 399 stars today | 语言:

Learn how GPT-5.6 powers Microsoft 365 Copilot with stronger AI capabilities across Word, Excel, PowerPoint, Chat, and Cowork for faster, higher-quality work.

Last year, Image Line introduced Gopher for FL Studio, an AI chatbot that was basically a glorified instruction manual. You asked it how to do something, and it would serve up the relevant instructions. It's the kind of thing I actually use AI for on a semi-regular basis. But in the new release, Gopher can […]

Benchmark-backed Ollama has amassed 176,000 stars, and nearly 17,000 forks on Github by helping developers easily run AI on their PCs.

In an interesting twist that takes advantage of the company's core product, users can chat with these shows' characters, ask them questions, and even roleplay different storylines.

One of the things I do when approaching a new codebase is to find the entry points and start exploring down the call paths. This gives a good overview of the different components in the code and how they're connected. I wanted to translate that to a visual experience, similar to how you would use call graphs, but there's a couple of problems with classical call graphs. One, call graphs represent flow at the function level, so the architectural context is lost. And call graphs tend to get very la

OpenSuperWhisper 评测：macOS 上最被低估的开源语音转文字工具？ 30秒结论 ：OpenSuperWhisper 是一个基于 OpenAI Whisper 模型的 macOS 原生听写（dictation）应用。如果你受够了 macOS 自带听写的间歇性抽风，或者不想每月交钱给 Otter.ai，这个免费开源项目值得一试。 但别期待开箱即用 ——你需要自己配置模型、处理依赖，而且目前只支持 macOS。 适合人群：macOS 重度用户、需要离线语音转文字、对隐私敏感、愿意折腾配置的开发者。 不适合：Windows/Linux 用户、不想碰终端的人、需要实时流式转写（目前不支持）。 核心功能：代码实操 1. 安装部署 # 克隆仓库 git clone https://github.com/Starmel/OpenSuperWhisper.git cd OpenSuperWhisper # 安装依赖（需要 Python 3.10+） pip install -r requirements.txt # 直接运行 python app.py 坑点1 ： requirements.txt 里没写版本号，我踩了 numpy 版本冲突的坑。建议手动指定： pip install numpy == 1.26.0 torch == 2.1.0 whisper == 20231117 坑点2 ：macOS 14 Sonoma 上需要手动授权麦克风权限。第一次运行会 crash，因为没处理 PermissionError 。workaround：在 System Settings > Privacy & Security > Microphone 里手动勾上终端或 Python 的权限。 2. 基本使用 启动后会在菜单栏出现一个小图标（类似 macOS 原生听写）。快捷键是 Option + Space （可自定义）。 核心逻辑：按下快捷键 → 录音 → 松开 → 调用 Whisper 转写 → 结果写入当前光标位置。 代码层面 ，核心函数在 whisper_handler.py 里： # 简化版核心逻辑 import whisper import sounddevice as sd import numpy as np class WhisperHandler : def __init__ ( self , model_size = " base " ): self . model = whisper . load_model ( model_size ) self . sample_rate = 16000 def transcribe_from_mic ( self , duration = 5 ): # 录音 recording = sd . rec ( int ( duration * self . sample_rate ), samplerate = self . sample_rate , channels = 1 ) sd . wait () audio = recording . flatten (). astype ( np . float32 ) # 转写 result = self . model . transcribe ( audio , language = " zh " ) return result [ " text " ] 实测 ：默认 model_size="base" 时，中文准确率约 85%。换成 "large-v3" 能到 92%，但首次加载要 2GB 内存，转写一条 10 秒语音需要 8-12 秒（M1 Pro 芯片）。 3. 自定义快捷键 config.yaml 里可以改： hotkey : modifier : " option" key : " space" model : size : " base" # 可选: tiny, base, small, medium, large-v3 device : " cpu" # 或 "mps" (Apple Silicon) output : paste_delay : 0.3 # 转写后粘贴延迟，防止焦点丢失 注意 ： device: "mps" 在 macOS 14.2 上会报 MPS backend not available 。需要安装 PyTorch 的 MPS 版本： pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cpu 性能测试 测试环境：MacBook Pro M1 Pro (