Thirty days. From class Item: to a Protocol-typed library with parametrised tests, generators, properties, and slots. Same six prompts you saw on day 1 — rate yourself again.
Where does this leave me in the v1 arc?
You've completed the Python series — Foundations, Patterns, Mastery. You can now read any Python codebase without flinching. The shapes you've seen — class, dataclass, decorator, generator, context manager, test, module, property, ABC, Protocol — are the vocabulary every Python codebase uses.
From here:
Automation Mastery if you want to apply this kit to real-world side effects — Composio, Gmail/Sheets/Slack/Notion at scale, idempotency patterns, durable scripts.
AI Mastery if you want to build agentic AI scripts — multi-step LLM pipelines, structured output, tool use, retrieval, evaluation.
Both Automation Mastery and AI Mastery assume you have the abstractions you learned here — they don't re-teach classes, dataclasses, decorators, or generators.
What did this track NOT teach?
Async/await — that's still deferred to a v2 specialty. Pandas/NumPy — out of scope for the script-writing arc. Web frameworks — different platform identity. Performance profiling beyond the generators-vs-lists pattern. C extensions, Cython, the GIL — each big enough to be its own deep dive. None of these are required to write idiomatic Python or to compose primitives into Automation/AI scripts. You have enough.
You wrote 24 small Python programs across four weeks. The kit you have:
| Capability | Lessons that taught it |
|---|---|
| Define a named shape | L2 (motivation), L3 (class), L4 (methods), L5 (@dataclass), L6 (type hints), L7 (when not to) |
| Wrap behaviour | L9 (functions as values), L10 (decorators), L11 (decorators with args) |
| Lazy iteration | L12 (generators), L13 (pipelines) |
| Guaranteed cleanup | L14 (@contextmanager) |
| Verify correctness | L16 (why test), L17 (unittest), L18 (parametrise) |
| Organise code | L19 (modules and packages) |
| Write idiomatic Python | L20 (EAFP, truthiness, is None) |
| Refine and constrain | L23 (@property), L24 (ABC), L25 (Protocol), L26 (__slots__) |
| Optimise iteration | L27 (generators vs lists) |
| Compose all of it | L21, L28 (synthesis) |
Any Python codebase you'll work in from here uses some combination of these.
Deferred to v2 / future specialised tracks:
async / await and concurrency primitivespandas, numpy, scientific computingTypeVar, generic Protocols, ParamSpec)None of these are required to compose Composio tools or call LLMs. You have enough.
→ Automation Mastery — Composio at scale, idempotency, durable scripts, multi-tool orchestration.
→ AI Mastery — agent loops, tool use, retrieval, structured output, evaluation harnesses.
Rate the prompts below as honestly as you did on day 1. Then pick the next track.
Thirty days. From class Item: to a Protocol-typed library with parametrised tests, generators, properties, and slots. Same six prompts you saw on day 1 — rate yourself again.
Where does this leave me in the v1 arc?
You've completed the Python series — Foundations, Patterns, Mastery. You can now read any Python codebase without flinching. The shapes you've seen — class, dataclass, decorator, generator, context manager, test, module, property, ABC, Protocol — are the vocabulary every Python codebase uses.
From here:
Automation Mastery if you want to apply this kit to real-world side effects — Composio, Gmail/Sheets/Slack/Notion at scale, idempotency patterns, durable scripts.
AI Mastery if you want to build agentic AI scripts — multi-step LLM pipelines, structured output, tool use, retrieval, evaluation.
Both Automation Mastery and AI Mastery assume you have the abstractions you learned here — they don't re-teach classes, dataclasses, decorators, or generators.
What did this track NOT teach?
Async/await — that's still deferred to a v2 specialty. Pandas/NumPy — out of scope for the script-writing arc. Web frameworks — different platform identity. Performance profiling beyond the generators-vs-lists pattern. C extensions, Cython, the GIL — each big enough to be its own deep dive. None of these are required to write idiomatic Python or to compose primitives into Automation/AI scripts. You have enough.
You wrote 24 small Python programs across four weeks. The kit you have:
| Capability | Lessons that taught it |
|---|---|
| Define a named shape | L2 (motivation), L3 (class), L4 (methods), L5 (@dataclass), L6 (type hints), L7 (when not to) |
| Wrap behaviour | L9 (functions as values), L10 (decorators), L11 (decorators with args) |
| Lazy iteration | L12 (generators), L13 (pipelines) |
| Guaranteed cleanup | L14 (@contextmanager) |
| Verify correctness | L16 (why test), L17 (unittest), L18 (parametrise) |
| Organise code | L19 (modules and packages) |
| Write idiomatic Python | L20 (EAFP, truthiness, is None) |
| Refine and constrain | L23 (@property), L24 (ABC), L25 (Protocol), L26 (__slots__) |
| Optimise iteration | L27 (generators vs lists) |
| Compose all of it | L21, L28 (synthesis) |
Any Python codebase you'll work in from here uses some combination of these.
Deferred to v2 / future specialised tracks:
async / await and concurrency primitivespandas, numpy, scientific computingTypeVar, generic Protocols, ParamSpec)None of these are required to compose Composio tools or call LLMs. You have enough.
→ Automation Mastery — Composio at scale, idempotency, durable scripts, multi-tool orchestration.
→ AI Mastery — agent loops, tool use, retrieval, structured output, evaluation harnesses.
Rate the prompts below as honestly as you did on day 1. Then pick the next track.
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