orrbenyamini

3 ms·

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by orrbenyamini 9mo ago
Good question! Dataclasses were actually pretty easy - Python's introspection tools made them straightforward. The tricky parts were: - Type hints - Mapping __init__ params to attributes, especially with complex types - Preserving type
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by orrbenyamini 9mo ago
Appreciate the feedback, the formatting completely broke when pasting the code snippets into Medium. I fixed the article formatting and some of the feedback i got for it. Thanks for investing time reading !
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Show HN: Control Your Telegram with AI(medium.com)
2 points by orrbenyamini 9mo ago|0 comments
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by orrbenyamini 9mo ago
Pydantic is great lib and and has many advantages over Jsonic, I think main use cases for Jsonic over Pydantic are: - You already have plain Python classes or dataclasses and don’t want to convert them to BaseModel - You prefer minimal intr
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by orrbenyamini 9mo ago
Hi HN - I’m the author of Jsonic. I built it after repeatedly running into friction with Python’s built-in json module when working with classes, dataclasses, nested objects, and type hints. Jsonic focuses on: - Zero-boilerplate serializati
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Show HN: Jsonic – Python JSON serialization that works(medium.com)
24 points by orrbenyamini 9mo ago|14 comments
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by orrbenyamini 9mo ago
Author here. This came from a production incident where renaming a field broke our rolling deployment. The cache became a shared contract between versions we hadn't versioned. The solution: automatically derive version identifiers fr
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Version your cache keys to survive rolling deployments(medium.com)
4 points by orrbenyamini 9mo ago|1 comments