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- Every page under /software rewritten or tightened: 165 em-dashes removed, download stats corrected against live PyPI numbers, broken kjvstudy/httpbin links fixed (homepage too) - Full rewrites: sample pack, clint, autoenv, osx-gcc-installer, games and legacy indexes; Responder promoted to Major Projects - New essay documenting today's changes, the Obsidian sync pipeline (kr-vault plugin + sync-repo.py), and uv; added to essays index Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
35 lines
2.2 KiB
Markdown
35 lines
2.2 KiB
Markdown
# StrainsDB: AI-Generated Cannabis Database
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[strainsdb.org](https://strainsdb.org/) is a Django web application that catalogues cannabis strains, built as an early experiment in AI-powered content generation. Every strain description, terpene profile, and data point was generated using large language models and structured with Pydantic models for consistency.
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## What It Does
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- **Strain Browser**: Browse hundreds of cannabis strains with detailed descriptions, effects, and growing information.
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- **Search**: Find strains by name, effects, or characteristics.
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- **Terpene Profiles**: Detailed terpene breakdowns for each strain.
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- **Structured AI Content**: All data generated by GPT-3.5, validated through Pydantic models to ensure consistency.
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## Why I Built It
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StrainsDB was a proof of concept: could you build a content-rich website almost entirely with AI-generated data, and could that data be structured and reliable enough to be useful?
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The answer turned out to be yes, with the right tooling. The key insight was using the [Instructor](https://python.useinstructor.com) library to constrain LLM outputs to Pydantic models. Instead of hoping the AI would return well-formatted data, I defined exactly what "well-formatted" meant and let the library enforce it. This produced a database of hundreds of strains with consistent, structured information.
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## Technical Stack
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- **Django**: Web framework and admin interface.
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- **GPT-3.5**: Content generation for strain descriptions and profiles.
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- **Instructor + Pydantic**: Structured output validation, ensuring every AI-generated entry matches the expected schema.
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- **SQLite**: Lightweight storage, perfect for a proof of concept.
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## What It Demonstrated
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This project predated the widespread adoption of LLMs for content generation. It showed that AI could produce not just plausible text, but structured, validated data suitable for a real web application, as long as you had the right constraints in place.
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The pattern of "AI generates, Pydantic validates" has since become common practice. StrainsDB was one of the early demonstrations of why that pattern works.
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## Visit
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- [strainsdb.org](https://strainsdb.org/)
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- [Source Code on GitHub](https://github.com/kennethreitz/strainsdb)
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