Same story, from first steps to deep dives. Each deck is self-contained — you don't need the one before it. Find yourself below and start there.
You know what an LLM is. You just don't know which tool to open.
You'll leave with A map of the big three labs, a leader per category (video, voice, avatars, decks, research, automation), five real stories, and how to plug AI into Excel & PowerPoint.
02For developers · start here Intro to Agentic AI →You write code and want an agent doing it with you — starting today.
You'll leave with The agent loop, context and why it rots, permissions & safety, memory (CLAUDE.md), skills, plugins and MCP — plus how to work with it well.
03For developers · the deep end Agentic Engineering →You already use agents daily. Now you want to know why they fail — and how to stop it.
You'll leave with Context as the program, token cost & KV-cache economics, model × reasoning-effort routing, the agentic ladder, loops & verifiers (and reward hacking), dynamic workflows.
04Deep dive · one topic Subagents & Prompt Caching →You've done the deep end. Now you want the two primitives that decide what an agent costs.
You'll leave with Subagent isolation and the four context crossings, parallel fan-out and its limits, cache breakpoints, exact-match prefixes, TTL & eviction, break-even math — and the subscription-vs-API-key rules.
05Deep dive · level upWIP Cost & Context →You already use Claude Code every day. Now find out what it actually bills — and stop wasting the window.
You'll leave with Three pricing meters, a which-plan-for-which-team table, the context lifecycle, CLAUDE.md hygiene, /clear vs /compact — and a live bloated-vs-lean demo.
06Deep dive · level upWIP Orchestrating Agents →One agent is table stakes. Run several — safely — and give them the right capabilities.
You'll leave with A map of the orchestration ecosystem, the three failure modes of parallel agents, skills & plugins for teams, and a per-capability MCP-vs-CLI heuristic.
07Deep dive · level upWIP Measuring What Works →Stop arguing about prompts from vibes. Measure — then route models and harnesses by the numbers.
You'll leave with A minimal eval loop you can start this week, trace-reading skills, the July-2026 open-model landscape (Gemma 4, Qwen 3.6, GLM-5.2), and daily habits that compound.
08Conference talk · case study Has this happened before? →Oracle logs tell you what failed. They don't tell you whether you've seen it before, or what fixed it last time.
You'll leave with Logbook: an agent that turns Oracle logs into a git-backed incident wiki, with a worked ORA-04031 case study, the local/cloud boundary and which model gets which job.
09Meetup · agent workflows T3 Code →Coordinating agents across harnesses and machines.
Covered in the talk The thread inbox, delegation, remote machines, previews, PRs and automation in T3 Code.