AI Hiring and Ghost Jobs Are Making the Job Search, Labor Market Weird
The AI enshittification continues:
Job seekers may virtually interview with or be prescreened by an artificial-intelligence program such as HireVue, Harver, or Plum. After someone applies to a job at a company that uses this software, they may receive an automated survey asking them to answer inane personality-assessment questions like “Which statement describes you best? (a) I love debating academic theories or (b) I adopt a future emphasis.” […] And these AI-moderated processes might not be fair, either. Researchers at the University of California, Berkeley, say that AI decision-making systems could have a 44% chance of being embedded with gender bias, a 26% chance of displaying both gender and race bias, and may also be prone to screening out applicants with disabilities. In one notorious case, an audit of an AI screening tool found that it prioritized candidates who played high-school lacrosse or were named “Jared.”
(tags: jared ai enshittification future jobs work hirevue harver plum ghost-jobs hiring)
Erasure Coding versus Tail Latency – Marc’s Blog
A very neat trick via Marc Brooker to improve tail latencies using erasure coding: ‘Say I have an in-memory cache of objects. I can keep any object in the cache once, and always go looking for it in that one place (e.g. with consistent hashing). If that place is slow, overloaded, experiencing packet loss, or whatever, I’ll see high latency for all attempts to get that object. With hedging I can avoid that, if I store the object in two places rather than one, at the cost of doubling the size of my cache. But what if I wanted to avoid the slowness and not double the size of my cache? Instead of storing everything twice, I could break it into (for example) 5 pieces .. encoded in such a way that I could reassemble it from any four pieces .. . Then, when I fetch, I send five get requests, and have the whole object as soon as four have returned. The overhead here on requests is 5x, on bandwidth is worst-case 20%, and on storage is 20%. The effect on tail latency can be considerable.’
(tags: architecture cache storage tail-latencies performance marc-brooker lambda erasure-coding algorithms latency)
Container Loading in AWS Lambda
Some lovely details in this writeup of a new system in AWS Lambda, via Marc Brooker:
This system gets performance by doing as little work as possible (deduplication, caching, lazy loading), and then gets resilience by doing slightly more work than needed (erasure coding, salted deduplication, etc). This is a tension worth paying attention to in all system designs.
(tags: architecture aws lambda marc-brooker performance storage caching containers caches)