A fast, local document parser that extracts spatial text with bounding boxes from PDFs and other formats. Bundles Tesseract OCR and supports HTTP OCR servers, multi-language bindings (Rust, Node, Python, WASM) and screenshot generation; best for lightweight local pipelines but less suited to very complex or heavily scanned documents.
Provides a local MCP server that returns precise, symbol-level code (functions, classes, imports) via tree-sitter parsing so AI agents send only the bytes they need—commonly cutting code-reading token usage 95%+ and enabling compact packed responses for further savings.
Runs a persistent, self-modifying AI agent locally with durable identity, memory, and versioned history across tasks. Provides native desktop and headless CLI runtimes, coordinated subagent swarms, configurable remote or local GGUF models, and reviewed self-evolution via Git.
Turns authorized ad-account exports or reads into dated, source-grounded audits, channel and budget plans, creative briefs, experiments, monitoring, and versioned JSON reports. Read-only by default; supports twelve ad platforms and drafts safe account changes behind capability, verification, and rollback gates.
Local LLM inference server for Apple Silicon that exposes an OpenAI-compatible API and a macOS menubar app. Uses continuous batching and a two-tier KV cache (RAM + SSD in safetensors) to persist context across restarts, enabling practical multi-model serving and fast local coding workflows.
Acts as an OpenAI‑compatible local and cloud gateway that routes requests across 100+ LLM providers with smart routing, load balancing, retries and fallbacks. Adds policies, rate limits, semantic caching and observability for reliable, cost‑aware inference in Docker, Electron or npm installs.
Scans a React codebase and produces a 0–100 health score plus actionable diagnostics across state & effects, performance, architecture, security, accessibility, and dead code. Auto-adapts to framework and React version, supports Next.js/Vite/React Native, a CLI, GitHub Action, and agent integrations to teach coding agents.
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.
Provides reusable “skill” instruction bundles that teach AI coding tools how to author, query, and operate Microsoft Fabric workloads via REST APIs, T-SQL, KQL and notebooks. Includes Copilot CLI/Claude/Cursor integrations, workload-focused bundles, and optional MCP configurations for live data access.
Optimizes websites for AI-first search by providing GEO-focused SEO audits: citation-readiness scoring, AI-crawler access checks, schema generation, platform-specific recommendations, and client-ready PDF reports — delivered as a Claude Code skill with CLI commands.
Local-first session analytics for AI coding agents: discover, search, and track token usage and estimated costs across Claude Code, Codex, Forge and 20+ other agents. Single binary / desktop app that runs locally (no cloud accounts) with fast, SQLite-backed queries and optional PostgreSQL/DuckDB sync.