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 integrity concern due to multiple faces detected combined with repeated window focus loss events during interview onset.
- 0:00Multiple faces detected (2 ID faces + 1 selfie face, similarity 0.39): suggests another person may be present or visible, raising collaboration/impersonation risk.
- 0:00Three window focus loss events in rapid succession (1 tab switch, 1 window blur, 1 tab switch): indicates candidate attention diverted away from interview environment at critical start moment.
The combination of multiple faces at interview start with immediate repeated window focus loss (3 events) elevates concern beyond either signal alone. The low similarity score (0.39) suggests the detected faces may be distinct individuals rather than lighting artifacts. Human review should verify whether a second person was legitimately present and whether the focus losses correlate with any suspicious activity or content access.
4 signals · AI-generated · human review required
| Time | Signal | Severity |
|---|