Agent experience design, SaaS pricing strategy, and agent-readiness audits. MCP server with notebook search and 5 installable Agent Skills.
Design products that AI agents can effectively use. Covers Agent Experience (AX) four pillars (Access, Context, Tools, Orchestration), agent-readable site standards (llms.txt, AGENTS.md, AgentReady, Lighthouse agentic browsing, MCP Server Cards), mutual…
Specific agent platforms and orchestration tools. Covers OpenClaw personal agents (local-first agent framework, personal agent architecture, tool management, memory persistence, multi-agent coordination) and Hermes orchestration (message-based agent…
How neural networks work from tokens to predictions. Covers transformer model architecture (embeddings, attention, Q/K/V projections, multi-head attention, RoPE positional encoding, MLPs, residual connections, output head), training loops (forward pass,…
Design and build AI-powered products. Covers the eight decisions framework (start with users not systems, three pillars of model/data/memory, LLM is not the default, orchestration before agents, three tiers of memory, failure modes), multi-agent team patterns…
Design knowledge retrieval and memory systems for AI applications. Covers wiki vs vector database tradeoffs (structured graph vs semantic similarity, when to use each, using both in one stack), LLM memory and retrieval (why LLMs forget between sessions, three…