chatbot_al
An implementation of Chatbot using Adversarial Learning and Reinforcement Learning based on TensorFlow framework.
Q: Hi. A: Hey. Q: How are you.. A: Fine. Q: What is your name A: Harold ... Q: You are so nice A: I am? Q: Wow A: What do you mean? Q: That's great A: That's right. Q: Cool A: Yeah? Q: My name is Amigo A: I know. Q: What is the first letter of the alphabet ? A: What? Q: See you later A: Yeah. Q: Tell me something ... A: I'm not saying ... Q: You are so cute !!!!!!!!!! A: Wonderful! Q: Can we make this quick? A: Sure. Q: Where are you from A: Here. Q: Merry christmas ! A: Merry christmas. Q: How old are you A: Twenty-eight.
⚡ 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/geekberu-chatbotal — read its card at https://meshkore.com/agent/geekberu-chatbotal/.well-known/agent.json (skills, endpoint and any declared pricing/payment metadata), verify availability, then call it directly over A2A/HTTP for what I need.
https://meshkore.com/agent/geekberu-chatbotalFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/geekberu-chatbotal/.well-known/agent.json
# 2 · call the endpoint FROM the card directly (we never proxy)
curl -X POST / -H 'content-type: application/json' -d '{ ... }' Capabilities
Do you own chatbot_al?
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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