AliceDarkest
38 lines
1.5 KiB
Markdown
38 lines
1.5 KiB
Markdown
# Profile Matching - Public Code Sample
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This is a small, dependency-free portfolio excerpt adapted from **Cloudy**, an
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AI-assisted profile and matching service I built for a collaboration platform.
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The production repository is private, so this project intentionally contains
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only the self-contained scoring layer and its tests. It includes no user data,
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credentials, API code, or project-specific infrastructure.
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## What it demonstrates
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- transparent, explainable matching across skills, interests, location, and
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shared project tags;
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- input normalisation and explicit handling of incomplete profile data;
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- optional affinity weights that personalise recommendations without changing
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existing behaviour when no preferences are set;
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- pure business logic separated from database and API concerns;
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- executable tests for normal cases, edge cases, and scoring invariants.
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The actual application combines this score with an asynchronous PostgreSQL
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repository layer and uses it to rank potential collaboration matches.
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## Run the tests
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The sample uses only the Python standard library:
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```bash
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python -m unittest -v
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```
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## Why this is good code
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The model is deliberately small and deterministic. Each function has one
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responsibility, is type annotated, and can be tested without a database, LLM,
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or network access. Missing location data never creates a false positive, and
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the weighted extension remains backwards-compatible with the original Jaccard
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similarity model. That keeps the recommendation logic understandable for users
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and safe to evolve in a larger product.
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