Working smarter to fight cancer: using dunder special methods to automate
summary
Anocca builds an immune-therapy data platform with Django, Celery, PostgreSQL, Graphene-Django GraphQL, TypeScript React/Apollo, Docker/Kubernetes and MinIO, and automates its huge API by procedurally generating schema from Django models at import time. The codebase uses __init_subclass__ and type() to emit query, list and mutation types at runtime, producing a GraphQL surface of thousands of objects and tens of thousands of fields, cutting boilerplate and letting a small team scale schema maintenance successfully.
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chapters (22)
- 0:00Introduction and speaker intro
- 1:19Speaker background and Anoka overview
- 2:02Adaptive immune system explained
- 4:29T cells and therapeutic potential
- 7:39Knockers platform overview
- 9:46Precision functional assays and automation
- 10:27Anokaros software platform details
- 11:50Tech stack and architecture
- 12:14GraphQL API introduction
- 14:45Schema generation challenges
- 15:28Core object type and mixins
- 16:32Code examples and automation
- 19:21Runtime class modification with __init_subclass__
- 21:47Droid example and standardization
- 24:38Benefits and trade-offs of the approach
- 24:59Team acknowledgments and call to action
- 25:25Q&A session begins
- 26:08Challenges with abstraction and debugging
- 27:53Cancer treatment application explained
- 29:44CAR-T therapy and future outlook
- 30:28Meta classes vs __init_subclass__ discussion
- 31:09Closing remarks and thanks
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