Thirty days. From your first webhook payload parse to a 6-primitive reliable pipeline. Same six prompts you saw on day 1 — rate yourself again.
Where do I go next?
AI Foundations, if you haven't done it. The Automation series taught you the production-shaped layer beneath any reactive system. AI tracks build on top — LLMs running inside the same reliable, observable, rate-limit-aware shells you just wrote. The webhook handler stays; the process step gets a smarter brain.
If you've done all three Automation tracks (Foundations + Patterns + Mastery) and the AI series, you have everything needed to build any production automation: tool calls, side effects, idempotency, retries, dedup, state, structured logs, metrics, alerts, queues, dead-letters, replay safety, rollback, and LLM-driven decisions on top.
What does "production" actually mean now?
A script is production-shaped when running it unattended for a year wouldn't surprise you. You know how it'll fail (rate limits, network drops, bad payloads, schema drift) and how it'll recover (retry, dead-letter, dedupe, replay-safe state). You've thought about how to debug it (structured logs, dashboard, threshold alerts) before things go wrong, not after.
Not every script needs all of this. A one-off processes-and-emails-once script doesn't need a webhook receiver. The skill is recognizing which patterns apply and reaching for them when they do.
24 small Python scripts across four weeks plus three syntheses. Your kit:
| Capability | Lessons |
|---|---|
| Webhooks | L1-L7 (push vs pull, payload, HMAC, dispatch, idempotency, state) |
| Direct HTTP | L8-L13 (requests, rate limits, secrets, per-env config, versioning) |
| Observability | L15-L17 (structured logs, metrics, threshold alerts) |
| Long-running | L18-L20 (status checkpoints, queue-style, synthesis) |
| Patterns at scale | L22-L27 (dead-letter, replay safety, dashboards, consistency, rollback, final synthesis) |
Any webhook-driven, scheduled, or long-running automation you'll read or build composes these.
Deliberately out of scope:
None of these are required to write reliable, observable automations on a single machine.
→ AI Foundations — LLM calls, prompting, structured output, the four task verbs (summarize / classify / transform / extract). The LLM becomes a step in the same pipelines you wrote here.
→ Or write your own first reliable automation. Pick a webhook source you already use (Stripe, GitHub, Calendar) and build the smallest handler that:
That's a real production shape. The kit you have now can build it.
Rate the prompts below as honestly as you did on day 1.
Thirty days. From your first webhook payload parse to a 6-primitive reliable pipeline. Same six prompts you saw on day 1 — rate yourself again.
Where do I go next?
AI Foundations, if you haven't done it. The Automation series taught you the production-shaped layer beneath any reactive system. AI tracks build on top — LLMs running inside the same reliable, observable, rate-limit-aware shells you just wrote. The webhook handler stays; the process step gets a smarter brain.
If you've done all three Automation tracks (Foundations + Patterns + Mastery) and the AI series, you have everything needed to build any production automation: tool calls, side effects, idempotency, retries, dedup, state, structured logs, metrics, alerts, queues, dead-letters, replay safety, rollback, and LLM-driven decisions on top.
What does "production" actually mean now?
A script is production-shaped when running it unattended for a year wouldn't surprise you. You know how it'll fail (rate limits, network drops, bad payloads, schema drift) and how it'll recover (retry, dead-letter, dedupe, replay-safe state). You've thought about how to debug it (structured logs, dashboard, threshold alerts) before things go wrong, not after.
Not every script needs all of this. A one-off processes-and-emails-once script doesn't need a webhook receiver. The skill is recognizing which patterns apply and reaching for them when they do.
24 small Python scripts across four weeks plus three syntheses. Your kit:
| Capability | Lessons |
|---|---|
| Webhooks | L1-L7 (push vs pull, payload, HMAC, dispatch, idempotency, state) |
| Direct HTTP | L8-L13 (requests, rate limits, secrets, per-env config, versioning) |
| Observability | L15-L17 (structured logs, metrics, threshold alerts) |
| Long-running | L18-L20 (status checkpoints, queue-style, synthesis) |
| Patterns at scale | L22-L27 (dead-letter, replay safety, dashboards, consistency, rollback, final synthesis) |
Any webhook-driven, scheduled, or long-running automation you'll read or build composes these.
Deliberately out of scope:
None of these are required to write reliable, observable automations on a single machine.
→ AI Foundations — LLM calls, prompting, structured output, the four task verbs (summarize / classify / transform / extract). The LLM becomes a step in the same pipelines you wrote here.
→ Or write your own first reliable automation. Pick a webhook source you already use (Stripe, GitHub, Calendar) and build the smallest handler that:
That's a real production shape. The kit you have now can build it.
Rate the prompts below as honestly as you did on day 1.
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