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feat: interactive network map, IOC highlighting, AUP hunt selector, type filters
- NetworkMap: hunt-scoped force-directed graph with click-to-inspect popover - NetworkMap: zoom/pan (wheel, drag, buttons), viewport transform - NetworkMap: clickable IP/Host/Domain/URL legend chips to filter node types - NetworkMap: brighter colors, 20% smaller nodes - DatasetViewer: IOC columns highlighted with colored headers + cell tinting - AUPScanner: hunt dropdown replacing dataset checkboxes, auto-select all - Rename 'Social Media (Personal)' theme to 'Social Media' with DB migration - Fix /api/hunts timeout: Dataset.rows lazy='noload' (was selectin cascade) - Add OS column mapping to normalizer - Full backend services, DB models, alembic migrations, new routes - New components: Dashboard, HuntManager, FileUpload, NetworkMap, etc. - Docker Compose deployment with nginx reverse proxy
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SKILLS/25-algorithms-performance.md
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SKILLS/25-algorithms-performance.md
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# Algorithms & Performance
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Use this skill when performance matters (large inputs, hot paths, or repeated calls).
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## Checklist
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- Identify the **state** you're recomputing.
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- Add **memoization / caching** when the same subproblem repeats.
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- Prefer **linear scans** + caches over nested loops when possible.
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- If you can write it as a **recurrence**, you can test it.
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## Practical heuristics
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- Measure first when possible (timing + input sizes).
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- Optimize the biggest wins: avoid repeated I/O, repeated parsing, repeated network calls.
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- Keep caches bounded (size/TTL) and invalidate safely.
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- Choose data structures intentionally: dict/set for membership, heap for top-k, deque for queues.
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## Review notes (for PRs)
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- Call out accidental O(n²) patterns.
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- Suggest table/DP or memoization when repeated work is obvious.
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- Add tests that cover base cases + typical cases + worst-case size.
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