diff --git a/presentation/2026-04-25-gdg-kolachi-cli-claude-code-gemini/index.html b/presentation/2026-04-25-gdg-kolachi-cli-claude-code-gemini/index.html index 2aea131..894986a 100644 --- a/presentation/2026-04-25-gdg-kolachi-cli-claude-code-gemini/index.html +++ b/presentation/2026-04-25-gdg-kolachi-cli-claude-code-gemini/index.html @@ -250,9 +250,19 @@ - +
+
+ I'll unpack each of these as we go — for now, just let them wash over you.
@@ -446,7 +466,7 @@
- Model (Brain 🧠 — e.g. Opus, GPT) + Harness (Body 💪 — e.g. tools, MCP, memory)
@@ -456,7 +476,7 @@ -The raw model has no real-time access — no internet, no files, no clock.
The harness reaches out via WebSearch and fetches a real answer from live sources.
Really?
@@ -592,7 +612,7 @@ -Similar prompt — but this time the model decided not to use the tool.
The model first tried one source — it failed (403) — so it fell back to another.
+ A harness gives the brain hands — but not a fixed routine.
@@ -624,7 +654,7 @@ -Even before you set up any structure, how you prompt matters. Specific beats vague. Context beats assumption.
We're going to learn five concepts using one running example: a weather reporter agent that fetches Dubai's temperature and renders a weather card. Same person — five different angles.
An agent is Claude playing a specific role. Meet the weather reporter — a specialist hired to fetch and report weather data for Dubai. Same Claude, different hat.
The difference in one picture: prompting is asking a stranger on the street; using an agent is asking your dedicated specialist.