AI Agent2025
Packages an AI agent's memory — data, embeddings, search indexes, and metadata — into one portable .mv2 file, replacing multi-service RAG stacks. Combines BM25 and HNSW search with temporal queries and sub-millisecond local reads, fully offline.
AI Agent2025
Wraps Claude Code and Codex with an execution harness that turns one coding agent into coordinated swarms. A single init command adds ~98 agents, an MCP tool server, cross-session vector memory, and cross-machine federation.
Practical, full-stack tutorial for building Retrieval-Augmented Generation (RAG) systems—covers data preprocessing, vector embedding and indexing, hybrid and multimodal retrieval, generation integration, evaluation and production-ready engineering. Includes hands-on projects and examples for developers with Python experience.
AI Coding2025
Provides semantic code search for AI coding agents by making an entire codebase available as context via hybrid BM25 + vector retrieval, reducing token costs. Uses incremental indexing, AST-based chunking, and Zilliz/Milvus-backed vectors for large-codebase and IDE workflows.
A code-first collection of runnable tutorials for building production-ready generative-AI agents — step-by-step guides covering stateful workflows, vector memory, RAG, tool integrations, Docker/AWS/RunPod deployment, security guardrails, observability, and multi-agent patterns.
AI Client2025
Continuously screenshots your screen, feeds the captures to a vision-language model, then pushes back daily summaries, weekly recaps, and todos on its own. Local-first desktop app: data stays on your machine; runs on OpenAI-format or local LLMs.
AI Infra2025
Offline-first knowledge server that bundles local AI chat (Ollama + vector RAG), offline Wikipedia/education/maps, and utility tools behind a Dockerized management UI — designed to keep searchable knowledge available without cloud access.
AI Infra2025
An open-source memory layer that turns agent runs and conversations into structured, persistent state recallable across sessions. Captures facts, events, preferences, and relationships automatically; LLM-agnostic with SDK and MCP integration.
AI Agent2025
Compiles an agent's raw chat logs, documents, and tool traces into three persistent layers — index, learned skills, and user memory — so context survives sessions. Claims 92% Locomo-benchmark accuracy and up to 95% lower token cost than replaying history.
AI Client2025
Indexes any repo into a knowledge graph of dependencies, call chains, and execution flows, then feeds it to AI coding agents via MCP so they stop missing context. Ships as a CLI plus a zero-install browser graph explorer with chat.
AI Agent2025
Provides hierarchical, versioned semantic memory for AI agents with Git-like branching, commits, and rollbacks—using semantic paths and cryptographic provenance instead of opaque vector stores. Designed for branch-aware, auditable memory in multi-agent and production workflows.
AI Dataset2025
A large multi-config collection of query–document pairs assembled to reproduce and extend the mGTE/LateOn data recipe for pre-training text embedding models. Data come in source-specific configs and include per-row drop/duplicate flags and guidance for using cleaned subsets for training.