Runs durable, checkpointed SQL workflows inside PostgreSQL so long-running data and AI pipelines can resume after crashes without external orchestrators. Provides a SQL DSL, in-process background worker, and Postgres-backed state—useful for embeddings, ETL, scheduling, and fan-out jobs when you can install extensions.
Desktop app for managing markdown-based knowledge bases with a files-first, git-first workflow. Works offline, uses plain markdown + YAML frontmatter for portability, and includes AI-agent integrations and agent configuration to organize context, memory, and procedures for assistants.
Provides a workspace-first, Kanban-backed multi-agent coordination platform that routes goals through specialist lanes (Backlog→Todo→Dev→Review→Done), enforces evidence-based review gates and traces, and runs on both web and desktop runtimes.
A self-hostable workspace where humans and AI agents collaborate in shared rooms, using a Nostr-based signed event log to unify chat, workflows and git events. Agents act as members with their own keys and audit trails, enabling scoped agent actions without shared secrets.
Hands-on, phase-based curriculum for building end-to-end AI systems from first principles — implement algorithms, run tests, and ship reusable artifacts (prompts, skills, agents, MCP servers) across Python, TypeScript, Rust, and Julia under an MIT license.
Compresses high-dimensional embeddings into low-bit TurboQuant indexes for fast, memory-efficient local vector search. Supports online ingest (no train/rebuild), SIMD kernels that match or beat FAISS, per-vector length-renormalization, and runtime allowlists — suited for privacy-sensitive, low-latency RAG.
Runs and monitors AI agents inside real terminal panes, surfacing agent state (blocked / working / done) and keeping agents persistent across detach/reattach. Offers workspaces, tabs, panes, socket-API integrations, and a single Rust binary for macOS/Linux.
Runs local speech-to-text inference for a wide range of ASR model families using GGUF models on the ggml runtime. Supports Metal, Vulkan, and CUDA GPU backends plus a tinyBLAS-accelerated CPU path, prebuilt GGUFs on Hugging Face, and a quantization tool.
Turns your documents into a persistent, interlinked personal wiki by incrementally reading sources, generating wiki pages, and keeping knowledge up to date. Features two-step chain-of-thought ingest, graph-based relevance with Louvain clustering, optional embedding search (LanceDB), and a local HTTP API for agent integration.
Provides hardware-isolated, sub-60ms, ultra-low-overhead sandboxes to run untrusted LLM/agent code. Offers event-level snapshots, kernel-level egress control, credential vaulting, and drop-in E2B SDK compatibility for high-density AI agent deployment.
Turns a codebase into a live structural knowledge graph that coding agents can query in milliseconds. Bi-temporal, replay-aware indexing of symbols and relationships performed locally with zero LLM API calls; Rust-native, MCP-native integrations and fast incremental updates.
Terminal-first developer workspace with an agentic AI side-panel that runs against your API keys or local models. Bundles a native PTY terminal, CodeMirror editor with AI edit diffs, file explorer, git history/graph, and a web preview in a ~7–8MB desktop app with no telemetry.