Spend 1 minute every day to get curated cutting-edge AI information.
The content includes but is not limited to cutting-edge AI news, AI tools, AI painting, open-source projects, and learning tutorials, etc.
Follow AI Daily to stay up-to-date with AI trends, and we hope it helps you. For important information, we will post detailed articles separately.
Here is the latest AI information for July 21.
Cutting-edge News
1. ElevenLabs releases Turbo 2.5 model.
For the first time, it supports Vietnamese, Hungarian, and Norwegian, with a 3x speed improvement in text-to-speech generation for other 27 languages including Hindi, French, Spanish, Chinese, and more.
This will unlock high-quality, low-latency conversational AI for nearly 80% of regions worldwide.
Detailed introduction: https://elevenlabs.io/api
Open-source Projects
1. A useful open-source AI programming assistant VSCode extension: Aide.
The plugin primarily supplements existing programming assistants like Copilot, CodegeeX, or Codeium with practical features, allowing developers to code more efficiently.
GitHub: https://github.com/nicepkg/aide
Features include:
- 🔄 Code Conversion: One-click code language conversion.
- 📖 Code Comments: One-click to add detailed comments, improving readability.
- 📋 Quick Copy: Batch copy files/folders for AI prompts.
- 💬 Custom Commands: Execute custom AI commands on selected files.
- 🔀 Variable Renaming: Rename variables with AI-suggested names.
- 🎛 Prompt Templates: Define flexible AI prompt templates.
- 📁 Multi-file Support: Select multiple files/folders for AI prompts or commands.
- 🚫 Ignore Mode: Exclude files/folders using custom glob rules.
- ⌨️ Shortcuts: Set your preferred function shortcuts.
Additionally, besides supporting mainstream models like ChatGPT and Claude, the plugin also supports domestic models and even local models.
Learning Books
1. An open-source book by MIT: "Understanding Deep Learning" now has a Chinese version!
It covers various aspects of deep learning from basic to advanced content, such as basic concepts, supervised learning, reinforcement learning, linear regression, neural networks, diffusion models, etc.
The content is very comprehensive, and each chapter also provides PPT and notes, along with 68 Python exercise demos for practice.
GitHub: https://github.com/careywyr/UnderstandingDeepLearning-ZH-CN
You can now download the Chinese translated book PDF from the project's Releases.