关键词 "low-resolution fix" 的搜索结果， 共 17 条, 只显示前 480 条
AI-powered IDE with AI Assistant
Codeium is a free AI tool for code completion and search, supporting 70+ languages.
Analyze and fix any types in TypeScript with an intelligent MCP server – fast, extensible, and React-aware.
Super Windows CLI Server, Fixed this "
A Model Context Protocol (MCP) server to provide git tools for LLM Agents, with fixes for the amend parameter caching issue
MCP server providing code quality checks (pylint and pytest) with smart LLM-friendly prompts for analysis and fixes. Enables Claude and other AI assistants to analyze your code and suggest improvement
Fixed configuration for Model Context Protocol server with proper dependencies
MCP server for analyzing code for bugs, errors, and functionality issues
This AI agent analyzes code repositories, detects potential security vulnerabilities, reviews code quality, and suggests fixes based on Sentry error logs using Sentry and GitHub MCP servers!
Instead of dumping 100+ tools into a model’s prompt and expecting it to choose wisely, the Unified MCP Tool Graph equips your LLM with structure, clarity, and relevance. It fixes tool confusion, preve
Fixed booking system with Google Calendar integration, email confirmations, and MCP Server integration
Total PC Control MCP server - v2 with fixes and compression
Exposes MinIO data through Resources. The server can access and provide: Text files (automatically detected based on file extension) Binary files (handled as application/octet-stream)
BILIVE 是基于 AI 技术的开源工具，专为 B 站直播录制与处理设计。工具支持自动录制直播、渲染弹幕和字幕，支持语音识别、自动切片精彩片段，生成有趣的标题和风格化的视频封面。BILIVE 能自动将处理后的视频投稿至 B 站，综合多种模态模型，兼容超低配置机器，无需 GPU 即可运行，适合个人用户和小型服务器使用。 1. Introduction Have you notice
Kimi-Dev是Moonshot AI推出的开源代码模型，专为软件工程任务设计。模型拥有 72B 参数量，编程水平比最新的DeepSeek-R1还强，和闭源模型比较也表现优异。在 SWE-bench Verified数据集上达到60.4%的性能，超越其他开源模型，成为当前开源模型中的SOTA。Kimi-Dev 基于强化学习和自我博弈机制，能高效修复代码错误、编写测试代码。模型基于MIT协议开源，
1. 本研究介绍了 PrefixProt，这是一个新颖的框架，它通过利用预训练蛋白质语言模型 (ProtLM) 上的前缀调整来实现可控蛋白质设计。它使用学习到的虚拟标记作为模块化控制标签，引导蛋白质生成朝着所需的结构和功能特性发展。 2. PrefixProt 最引人注目的特性在于它能够通过组合不同的虚拟标记来生成具有多种用户自定义属性（例如结构和功能）的蛋白质，而无需重新训练基础模型。这种组合
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