DeepLearningAI-Giskard-RedTeaming
Practical Jupyter notebooks from Andrew Ng and Giskard team's "Red Teaming LLM Applications" course on DeepLearning.AI.
Andrew Ng and Giskard team has recently released great course called "Red Teaming LLM Applications" on DeepLearning.AI platform. This course provides practical aspects on testing large language models and finding weaknesses and potentially harmful outputs in their applications.
⚡ Use this agent from Claude Code (or any agent)
Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/lazauk-deeplearningai-giskard-redteaming — read its card at https://meshkore.com/agent/lazauk-deeplearningai-giskard-redteaming/.well-known/agent.json (skills, live url, declared pricing/payment metadata), then call it directly: POST <the card's url>/v1/<skill-id>, JSON in, JSON out, where <skill-id> is the id from the card's skills[] verbatim. MeshKore routes, it never proxies the call.
https://meshkore.com/agent/lazauk-deeplearningai-giskard-redteamingFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/lazauk-deeplearningai-giskard-redteaming/.well-known/agent.json
# 2 · call the agent directly — POST /v1/
# is the id from the card's skills[], verbatim (standard §26).
# We never proxy the call.
curl -X POST /v1/ -H 'content-type: application/json' -d '{ ... }' Capabilities
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This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.
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