VQV Signal

AI AT WORK WATCH TECHNICAL

Anthropic engineer Felix Rieseberg removes coding interview from his team's hiring loops

According to a post on X by @felixrieseberg, the coding interview no longer gives useful signal, so he removed it from his team's hiring loops this month. He sees an opening for junior engineers. The same thread cites @Yuchenj_UW, who says young AI talent now skips companies that still use LeetCode-style tests, and @rakyll, who would rather watch how candidates use coding agents. The snippet is cut off, so the full context is not visible.

Source: Scobleizer News Pulse (X) · x.com Found via Robert Scoble’s Scobleizer News Pulse · a post on X, not independently verified Published 2026-10-28T00:35:00+00:00 Detected 2026-10-28T05:20:46+00:00
View original source

According to a post on X by @felixrieseberg, the coding interview no longer gives useful signal, so he removed it from his team's hiring loops this month. He sees an opening for junior engineers. The same thread cites @Yuchenj_UW, who says young AI talent now skips companies that still use LeetCode-style tests, and @rakyll, who would rather watch how candidates use coding agents. The snippet is cut off, so the full context is not visible.

AI-assisted summary based on the listed source.

Anthropic's @felixrieseberg says the coding interview no longer gives useful signal, so he removed it from his team's hiring loops this month, and he sees an opening for junior engineers. @Yuchenj_UW says young AI talent now skips companies that still use LeetCode-style tests, and @rakyll would rather watch how...

Hiring managers and recruiters who rely on LeetCode-style coding tests may need to rethink how they assess candidates. Junior engineers could benefit if teams follow this view, since the post points to an opening for them. Watch whether other teams drop these interviews and whether evaluations shift toward how candidates use coding agents.

Teams using AI at work may want to compare this against current productivity and review workflows.

Signal Strength 81% Technical label WATCH Public Interest 29 Category AI AT WORK Reader Depth TECHNICAL

Signal Strength reflects source quality, relevance, freshness and evidence. Public Interest helps organize discovery; it is not proof of truth.

Public Interest components
Recognizable Entity Score 0 Practical Impact Score 26 Novelty Interest Score 70 Consequence Score 22 Curiosity Score 16 Shareability Score 46

VQV surfaced this signal because it is recent, relevant to AI Coding Tools, connected to Scobleizer News Pulse (X).