AI Orchestration
Agents, tools, delegation, and reliable execution workflows.
AI Engineering & Orchestration
AI Engineering and Orchestration
Design LLM applications, retrieval, model routing, agent orchestration, evaluation, and production AI workflows.
~21 min reading
AI Coding Tools and MCP
Understand coding-agent workflows, Claude Code, Codex, reusable instructions, tools, and MCP security.
~8 min reading
Token Optimization and Context Engineering
Manage model context, token budgets, retrieval, caching, routing, and cost-quality tradeoffs.
~20 min reading
AI Security and Agent Governance
Secure AI agents with trust boundaries, prompt-injection controls, least privilege, validation, and governance.
~7 min reading
Interview Paths: Mobile, Backend, and AI Systems
Practice short role- and level-based interview responses, coding prompts, code reviews, and separated solutions.
~22 min reading
Agents, Plugins & SDLC Automation
AI Agents, Plugins, and Subagents
Understand agent hosts, plugins, skills, MCP tools, subagent task contracts, permissions, and verification.
~7 min reading
AI Model Selection and Benchmark Scores
Compare every named model by task fit, benchmark configuration, quality, latency, cost, hardware, licensing, and privacy.
~10 min reading
Build a Private Local AI Assistant
Build a local AI assistant with a model runtime, scoped agent tools, plugin review, and testable privacy boundaries.
~10 min reading
Build an AI Plugin: Skills, Tools, and MCP
Create an AI plugin with a portable manifest, a scoped skill, typed MCP tools, authentication boundaries, and testing.
~13 min reading
Define a Code Review Agent That Engineers Trust
Define a code review agent with pinned diffs, scoped repository rules, evidence-backed findings, and evaluation.
~9 min reading
Connect Jira to AI Workflows Across the SDLC
Design Jira Cloud AI workflows across the SDLC with OAuth, verified events, draft artifacts, review gates, and privacy controls.
~10 min reading