NitMonk · 2026-08-23 · 1,300 views · 🔥 1,300/day
MCP stops being hand-wavy once you see the wire: hosts, clients, and servers speak JSON-RPC while the LLM only chooses from exposed capabilities. The real production challenge isn’t tool calling; it’s discovery, orchestration, and security when hundreds of tools, resources, and prompts compete for context. That matters because badly designed MCP stacks waste tokens, hide risk, and fail at scale.
- Map host, client, server responsibilities clearly
- Design progressive tool discovery early
- Secure tools, resources, and prompts separately
Krish Naik · 2026-07-25 · 9,789 views · 🔥 326/day
AI apps don’t fail at demos; they fail when security and observability are bolted on later. This bootcamp focuses on guardrails, LLM gateways, monitoring, and governance to defend against prompt injection, jailbreaks, data leakage, and unsafe outputs. That matters if you want production AI that survives real users, audits, and incidents.
- Threat-model prompts before shipping
- Add gateways, logging, evals early
- Treat observability as security control
Aikido Security · 2026-08-13 · 1,790 views · 🔥 162/day
Prompt injection isn’t a chatbot prank; it’s a structural weakness in how LLMs prioritize instructions and tool access. The sharp bit: current architectures can’t reliably solve it, so attackers can abuse agents, override intent, and leak secrets—as shown in a Gemini CLI CI/CD case. That matters because any AI pipeline with tools, tokens, or automation expands your blast radius.
- Threat-model every tool, token, and prompt boundary.
- Treat agent inputs as hostile by default.
- Isolate secrets from AI-accessible workflows.
