Unknown
Software Engineer · mid
46e954b8-c70e-44b1-82d9-6390c1f16b9c
May 21, 2026, 3:41 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
Multiple integrity signals detected, particularly window focus loss and multiple faces at start; moderate dishonesty risk requiring human verification.
- 0:00Multiple faces detected (3 ID faces + 1 selfie face, similarity 0.25): suggests possible third-party presence or environment setup irregularity at interview start.
- 0:00–0:22Repeated window focus loss (4 events total): 2 medium-severity window blurs, 1 medium tab-switch, and 1 high-severity window blur suggest candidate attention divided between interview window and other applications or tabs.
The combination of multiple faces at entry (3 ID faces) and sustained window-focus loss throughout the opening 22 seconds suggests either environmental issues or divided attention. The tab-switch event is particularly notable. These signals together fall in the moderate-risk range; however, window blur can be triggered by system notifications or accidental clicks, and multiple-face detection may reflect poor camera positioning or reflections. Human review should clarify the environment setup and candidate's explanation for attention shifts.
5 signals · AI-generated · human review required
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