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Verified against live UniFi controller on 2026-03-28. Controller firmware: UniFi Network Application (Dream Machine Pro).
The original spec and data-model.md assumptions were partially wrong. Key corrections:
- DNS v2 API supports PUT — the bash scripts use delete+recreate but that's a choice, not a constraint
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| # Ignore log directories and files | |
| */.logs/ | |
| */logs/ | |
| *.log | |
| # Ignore temporary and cache files | |
| *.tmp | |
| *.swp | |
| .DS_Store | |
| */.DS_Store |
Based on the signal research in your spec directory, here are the 9 major categories with the best-fit examples for a Backstage plugin:
- Dependabot Alerts - CVEs in npm/pip dependencies
- Container Image Vulnerabilities - Trivy/Grype scan results
- EPSS Scores - Exploit prediction (likelihood of exploitation within 30 days)
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| BEGIN:VCALENDAR | |
| VERSION:2.0 | |
| PRODID:-//Munhall 2025 Waste & Recycling (generated by ChatGPT)//EN | |
| CALSCALE:GREGORIAN | |
| METHOD:PUBLISH | |
| BEGIN:VEVENT | |
| UID:munhall-holiday-newyear'sday-2025@chatgpt | |
| DTSTART;VALUE=DATE:20250101 | |
| DTEND;VALUE=DATE:20250102 | |
| SUMMARY:New Year's Day (Collection Delayed) |
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| { | |
| "$schema": "http://json-schema.org/draft-07/schema#", | |
| "title": "Learning Practice Deck Schema (v4 - Deck Object)", | |
| "description": "A schema for representing a deck of learning practice content (flashcards, quizzes) including metadata about the deck itself and a hierarchical topic structure.", | |
| "type": "object", | |
| "properties": { | |
| "deckId": { | |
| "type": "string", | |
| "description": "Unique identifier for the learning deck (e.g., UUID).", | |
| "format": "uuid" |
Legend: ★ = must-know, ◇ = nice-to-have.
- ★ Arrays & Dynamic Arrays (indexing, resizing, cache locality)
- ★ Linked Lists (singly/doubly, cycle detection)
- ★ Stacks & Queues (array vs. linked, deque)
- ★ Hash Tables (hashing, collisions, load factor, open addressing vs chaining)
- ★ Trees (binary tree traversals; BST invariants)
- ★ Heaps/Priority Queues (binary heap ops; k-way merge; top-k)
Legend: ★ = must-know, ◇ = nice-to-have.
- ★ Logistic Regression — linear decision boundary; well-calibrated probs, fast baseline.
- ◇ k-Nearest Neighbors — vote of nearby points; non-parametric, can be slow at scale.
- ★ Naive Bayes (Gaussian/Multinomial) — assumes feature independence; great for text.
- ★ Support Vector Machines (SVM) — max-margin hyperplane; kernels handle nonlinearity.
- ★ Decision Tree — if-then rules; interpretable, prone to overfit without pruning.
- ★ Random Forest — bagged trees; strong default, robust to noise, handles mixed types.
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