In explorers you wrote functions that hit two APIs and returned both counts. This week you start from that same skill and push it toward production — pure data functions that aggregate, combine, filter, and rank across real Gmail, Calendar, and Tasks.
What makes a function production-ready versus just working? The explorer versions ran fine.
The explorer version returned two counts. The production version turns noisy API responses into a clean, typed result that the rest of your pipeline can rely on. Same APIs, tighter contracts. No async. Every call is toolset.execute_action(Action.X, {...}) against your real inbox.
So the APIs stay the same, but the functions get smarter about shape and defensiveness?
Exactly. This week sets up Week 2 (grouping, dedup, try/except), Week 3 (idempotent writes, retry), and Week 4 (checkpointing, orchestration). Clean data is the foundation every later layer stands on.
Goal: Every function is a pure data transform on live API output.
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In explorers you wrote functions that hit two APIs and returned both counts. This week you start from that same skill and push it toward production — pure data functions that aggregate, combine, filter, and rank across real Gmail, Calendar, and Tasks.
What makes a function production-ready versus just working? The explorer versions ran fine.
The explorer version returned two counts. The production version turns noisy API responses into a clean, typed result that the rest of your pipeline can rely on. Same APIs, tighter contracts. No async. Every call is toolset.execute_action(Action.X, {...}) against your real inbox.
So the APIs stay the same, but the functions get smarter about shape and defensiveness?
Exactly. This week sets up Week 2 (grouping, dedup, try/except), Week 3 (idempotent writes, retry), and Week 4 (checkpointing, orchestration). Clean data is the foundation every later layer stands on.
Goal: Every function is a pure data transform on live API output.