Projects

### Overview: Macro is a unified workspace for teams, combining email, chat, docs, tasks, agents, calls, and CRM into a single, fast interface with shared AI memory. It aims to provide a seamless, computable experience for any small company or team. ### Key Features: - **Email**: Unified inbox with keyboard shortcuts and shared inboxes. - **Messages**: Channels and direct messages for focused technical discussions. - **Tasks**: Linear-inspired tasks tightly integrated with channels, email, and agents. - **Docs**: Real-time collaborative, markdown-native docs built on CRDTs with @mentions. - **Canvas**: 2D board with embedded @links to tasks, files, and emails. - **Agents**: Unified, team-level memory. - **Calls**: Recorded, transcribed, and logged to team memory for agents. - **File storage**: Auto-imported from email and channels, fully searchable. - **Pull requests**: Linked to tasks, embeddable in channels, available to agents. ### Tech Stack: Rust ### Why it Matters: Macro is a complete redesign of work software from the ground up as a single system, designed by us in NYC and Toronto, dogfooded by our team of ~15 for two years. Built in SolidJS and Rust for speed and reliability. We're focused on building something that any small company or team at a larger company can use as their "operating system." ### Repository: macro-inc/macro ### GitHub Stars: 4775 ### Primary Language: Rust ### GitHub Description: Macro is

### Overview: Infinity is an AI-native database designed for LLM applications, offering fast hybrid search capabilities for dense vector, sparse vector, tensor, and full-text data types. It supports various applications like search, recommenders, question-answering, conversational AI, and content generation, leveraging RAG (Retrieval-augmented Generation) technology. ### Key Features: - **🚀 Incredibly Fast**: Achieves 0.1 milliseconds query latency and 15K+ QPS on million-scale vector datasets, and 1 millisecond latency and 12K+ QPS in full-text search on 33M documents. - **🔮 Powerful Search**: Supports hybrid search of dense embedding, sparse embedding, tensor, and full text, and various types of rerankers including RRF, weighted sum, and **ColBERT**. - **

Overview: Learn Prompting is a comprehensive resource for mastering Generative AI, offering a free Prompt Engineering Guide, 15 courses, on-demand workshops, and a red-teaming competition. It is a go-to destination for individuals and businesses looking to enhance their AI skills. Key features: - Free Prompt Engineering Guide - 15 courses on Generative AI - On-demand workshops and training - HackAPrompt: Largest AI red-teaming competition - Discord community for learning and collaboration Tech stack: The project is primarily written in Markdown (MDX) and uses GitHub for hosting and collaboration. Why it matters: Learn Prompting provides a wealth of resources and opportunities for developers and AI enthusiasts to improve their skills and contribute to the field of Generative AI. Repository: trigaten/Learn_Prompting GitHub stars: 4739 GitHub description: Prompt Engineering, Generative AI, and LLM Guide by Learn Prompting | Join our discord for the largest Prompt Engineering learning community