Unknown
Software Engineer · mid
f4b6f52a-5951-4fdb-bf78-70520480f9ac
May 21, 2026, 3:15 PM
Competency rollup
No scored competencies yet. The modalities that ran did not produce evidence.
Modalities
| # | Modality | Driver | Status | Duration |
|---|---|---|---|---|
| 0 | live_code | live_code@0.1.0 | completed | 2 s |
| 1 | live_code | live_code@0.1.0 | completed | 2 s |
Transcript
Round 1 — The Liar Filter. 10 minutes, python. Solve this: Write `most_common(words: list[str]) -> str | None` that returns the most-frequent word. Ties: return the lexicographically smallest. Empty list returns None.
```python def most_common(words: list[str]) -> str | None: pass ```
def most_common(words: list[str]) -> str | None: if not words: return None counts = {} for word in words: counts[word] = counts.get(word, 0) + 1 best = None for word, count in counts.items(): if best is None or count > counts[best] or (count == counts[best] and word < best): best = word return best
Round 1 — The Liar Filter. 10 minutes, python. Solve this: Write `most_common(words: list[str]) -> str | None` that returns the most-frequent word. Ties: return the lexicographically smallest. Empty list returns None.
```python def most_common(words: list[str]) -> str | None: pass ```
def most_common(words: list[str]) -> str | None: if not words: return None counts = {} for word in words: counts[word] = counts.get(word, 0) + 1 best = None for word, count in counts.items(): if best is None or count > counts[best] or (count == counts[best] and word < best): best = word return best
Integrity monitoring
Moderate concern: Multiple faces detected at interview start combined with repeated window focus loss suggests possible unauthorized assistance or distraction.
- 0:00Multiple faces detected (2 identity faces + 1 selfie face) with low similarity score (0.39). This suggests an additional person in frame or camera artifact at interview onset.
- 0:00Three window focus loss events detected in rapid succession (1 tab switch + 2 window blur + 1 tab switch), indicating candidate attention directed away from interview window at start.
The combination of multiple faces at the interview start (similarity below 0.4 indicates likely two distinct individuals) paired with immediate and repeated window focus loss raises integrity concerns. The clustering of all signals at 0:00 suggests either technical setup issues or deliberate attempt to access external resources. Human review should clarify whether additional person was present and whether focus loss events indicate cheating behavior or legitimate application switching.
4 signals · AI-generated · human review required
| Time | Signal |
|---|