Results for “echidna”

Agent E3 - Mixed Methods Integration Specialist - Qual-Quant data integration and meta-inference. Covers joint display creation, integration strategies, and legitimation techniques.

Curates a set of Ethereum development tools for 2026, including Foundry, Scaffold-ETH 2, abi.ninja, Blockscout MCP, x402 SDKs, RPC providers, and block explorers.

Generate videos using Pruna's optimized P-Video and WAN models via the inference.sh CLI, supporting text-to-video, image-to-video, audio input, and multiple resolutions.

Read-only EVM client: wallets, tokens, gas across 8 chains.

Read-only EVM client: wallets, tokens, gas across 8 chains.

Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.

Translate and dub audio/video into 29 languages while preserving speaker voice using the inference.sh CLI.

Cheshire Skills Store index — curated Agent Skills packages for Cheshire Terminal (API, PostHog, Google Agent Registry, Skill Hub onchain, Stripe, Solana common errors, NOXA) plus the community skills collection. Use when installing store skills, browsing the store catalog, or wiring cheshireterminal.ai/skills-store.

Store and query embeddings with metadata filtering, vector search, and full-text search using an open-source database that scales from notebooks to production.

Routes a research topic through a structured two-phase workflow: generate an extensible outline, then fan out parallel web-search agents to investigate each item into validated JSON, producing a complete markdown report with table of contents.

Store and query embeddings with metadata, vector and full-text search, and filtering. Integrates with LangChain and LlamaIndex for RAG and semantic search applications.

Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.