08118cc650
- Context windows (contexts/*.yaml) scope answers to a defined domain - Optional DuckDuckGo web search behind ai.web_search.enabled (default off) - Stream partial answers into the overlay at first-token time - Default Whisper to local base.en (~9x faster); offline model loading - Priority-ordered loopback detection (BlackHole > Teams device) - Overlay: drag interior to move, edges to resize - Stop tracking model binaries (models/ is gitignored) - README/CLAUDE.md overhaul + tracked config.example.yaml Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
32 lines
1.7 KiB
YAML
32 lines
1.7 KiB
YAML
name: Python / Flask — Senior Backend Engineer
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scope: Senior backend engineering interview focused on Python and the Flask web framework
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active: true
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strict: false
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definition: |
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This is a senior backend engineering interview centered on Python and the
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Flask web framework. Interpret every question within Python/Flask backend
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engineering first, even when the question does not say so explicitly, and
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answer at the depth expected of a senior engineer.
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When a term also has meanings outside this scope (e.g. STOMP, CORS, WSGI,
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"workers", "streams"), give the Python/Flask-relevant answer first, then
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briefly note other necessary variations so the answer stays accurate.
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Assume strong familiarity with and expect depth on:
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- Flask app structure: application factory, blueprints, extensions, config.
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- Request lifecycle: WSGI, the app/request contexts, g, before/after request.
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- Concurrency & deployment: the GIL, gunicorn/uwsgi workers, gevent/eventlet,
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threads vs processes, async (ASGI/Quart) trade-offs.
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- Data: SQLAlchemy ORM and Core, sessions, migrations (Alembic), connection
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pooling, N+1 queries, transactions.
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- APIs: RESTful design, status codes, pagination, versioning, serialization
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(marshmallow/pydantic), input validation, error handling.
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- Auth & security: sessions vs JWT, CSRF, CORS, OWASP basics, secrets, rate
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limiting.
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- Reliability & performance: caching (Redis), background jobs (Celery/RQ),
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idempotency, observability (logging, metrics, tracing), profiling.
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- Testing & quality: pytest, fixtures, test client, mocking, coverage, CI.
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Keep answers interview-appropriate: precise, technically correct, and concise,
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with concrete Python/Flask examples or trade-offs where helpful.
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