Tag
Explore by tags
Build internal web apps, dashboards, workflows and AI agents with a visual low-code builder that connects to databases, APIs, SaaS and object storage. Includes AI app generation, AI query builder and AI debugging plus self-hosting and enterprise features like RBAC and audit logs.
Runs AI-generated code in secure, isolated cloud sandboxes you control via Python or JavaScript SDKs; supports self-hosting (Terraform) and AWS/GCP, enabling agents and code-interpreting workflows to execute real-world tools safely.
Declarative CLI and library to evaluate and red-team LLM apps: run test cases against prompts and models, compare providers side-by-side, and scan for jailbreaks, prompt injection, and data leaks — with CI/CD and pull-request code scanning built in.
Probes LLMs for failure modes — prompt injection, jailbreaks, data leakage, toxicity, hallucination — the way nmap scans a network. Ships 20+ attack probes that run against Hugging Face, OpenAI, Bedrock, Cohere, or any REST endpoint.
Runs LLM-generated Python in a Rust sandbox that starts in tens of microseconds (~60µs), with no container overhead. Filesystem, network, and environment access are blocked, and state serializes for pause/resume with per-run resource limits.
AI-assisted database client that generates, explains and optimizes SQL by connecting to your own model. Local-first, cross-platform GUI with metadata browsing, dashboards, 30+ database connectors, and an open-source CLI for automation.
Detects file content types with a compact deep‑learning model that runs in milliseconds on a single CPU. Trained on ~100M samples across 200+ content types; offered as a Rust CLI plus Python, JS, and Go bindings for large‑scale security and file‑routing use.
Runs reproducible evaluations of large language models through a Python API with built-in solvers, scorers, and model-graded grading. Ships 200+ ready-to-run evals spanning capability and safety testing, and connects to most major model providers.
Packs a Git repository into a single AI-friendly file for easy ingestion by LLMs. Offers per-file and total token counts, optional Tree-sitter compression, secret scanning, and multiple interfaces (CLI, web, browser extension, Docker, MCP) for AI-driven code review and analysis.
Runs untrusted workloads inside hardware-isolated local microVMs with Docker-like workflows; cross-platform and OCI-compatible, embeddable via SDKs and optimized for fast startups (typical guest boot <100 ms).
Extends the Wand (WeMod) desktop client’s local configuration and UI with a remote web panel, injected renderer scripts, automated compatibility patches and client-side AI features; runs entirely locally and does not publish official executables (build your own).