关键词 "career guidance" 的搜索结果, 共 21 条, 只显示前 480 条
Turn your business idea into reality!
AI-driven platform for efficient remote job searching.
Master your interviews with InterviewBoss!
AI-powered paper writing guidance.
Learn Salesforce, Get Certified & Launch Career with saasguru
Wonsulting helps non-traditional job seekers find dream jobs with coaching and resources.
Automate job search with AI-powered application automation.
AI career platform for job seekers offering various support tools.
AI-guided stock market insights.
Efficient technical interview platform
AI tool for enhancing CV quality and job application success.
Dezgo is an AI image generator that produces high-quality images from text descriptions.
Build lasting habits with AI guidance
这是一个使用 Python 语言从 0 到 1 搭建 MCP server 的代码学习教程
MCP server that provides tools and resources for integrating Hugeicons into various platforms. It implements a Model Context Protocol (MCP) server that helps AI assistants provide accurate guidance fo
An interactive web tutorial for learning how to build MCP (Model Context Protocol) servers. This educational website provides step-by-step guidance for implementing and understanding MCP servers.
Sensei MCP is a Model Context Protocol (MCP) server that provides expert guidance for Dojo and Cairo development on Starknet.
昆仑万维正式开源(17B+)Matrix-Game大模型,即Matrix-Zero世界模型中的可交互视频生成大模型。Matrix-Game是Matrix系列在交互式世界生成方向的正式落地,也是工业界首个开源的10B+空间智能大模型,它是一个面向游戏世界建模的交互式世界基础模型,专为开放式环境中的高质量生成与精确控制而设计。 空间智能作为AI时代的重要前沿技术,正在重塑我们与虚拟世界的
CareerSwift是人工智能驱动的求职助手,帮助求职者高效完成求职过程。通过AI分析简历内容提供改进建议,使简历更符合行业标准和职位要求;提供面试辅导资源,助力求职者提升面试技巧,支持智能匹配推荐适合的职位,简化申请流程;帮助用户管理求职进度,及时了解申请状态。为用户提供个性化的求职建议,确保简历通过雇主的申请人跟踪系统(ATS),提高求职成功率。 CareerSwift的主要功能
MMaDA(Multimodal Large Diffusion Language Models)是普林斯顿大学、清华大学、北京大学和字节跳动推出的多模态扩散模型,支持跨文本推理、多模态理解和文本到图像生成等多个领域实现卓越性能。模型用统一的扩散架构,具备模态不可知的设计,消除对特定模态组件的需求,引入混合长链推理(CoT)微调策略,统一跨模态的CoT格式,推出UniGRPO,针对扩散基础模型的统
全新的生成模型MeanFlow,最大亮点在于它彻底跳脱了传统训练范式——无须预训练、蒸馏或课程学习,仅通过一次函数评估(1-NFE)即可完成生成。 MeanFlow在ImageNet 256×256上创下3.43 FID分数,实现从零开始训练下的SOTA性能。 图1(上):在ImageNet 256×256上从零开始的一步生成结果 在ImageNet 256×25
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