Relayed
Pricing model
Upvote
0
Relayed is an AI-driven video conferencing solution aimed at assisting teams in managing remote work, hectic schedules, and meeting fatigue. It offers flexible video meetings, asynchronous discussions, automatic summarizations, seamless sharing via a secret link with restricted access, and a unified communication platform that allows revisiting and sharing of conversations at any time.
Similar neural networks:
Dify is a user-friendly LLMOps platform that enables teams to build AI applications utilizing models like GPT-4 and manage them through a visual interface. It allows for the rapid creation of AI-driven apps, using documents, webpages, or Notion content as contextual sources for the AI, and enhances applications' functionality with plugins. Additionally, it offers APIs for integration, supports two categories of applications (dialogue and text generation), and includes features for ongoing enhancement and management.
Feta is a productivity tool for meetings, tailored for product and engineering teams. It optimizes the meeting workflow, from planning to debriefing, by automating activities such as creating agendas, taking notes, and tracking action items. Through AI, Feta produces context-sensitive summaries, connects with major project management tools, and delivers real-time updates. Teams may opt for Feta to reduce time spent, enhance meeting effectiveness, and boost collaboration by ensuring all participants remain aligned and concentrated on critical tasks.
LM Studio is a software app enabling users to execute large language models (LLMs) on their personal devices without requiring an internet connection, thus providing improved privacy and data security. It accommodates various models like LLaMa, Falcon, MPT, Gemma, and others from Hugging Face repositories. Users can engage with models via an in-app Chat UI or a local server that is OpenAI's API compatible. LM Studio is perfect for individuals aiming to utilize LLMs for personal projects, research, or development while keeping full control of their data. This approach to locally running machine learning models is particularly appealing to those who prioritize privacy or handle sensitive information.