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Analysis

This project addresses the perceived flaws of traditional software engineering interviews, particularly the emphasis on LeetCode-style problems. It leverages AI (Whisper and GPT-4) to provide real-time coaching during interviews, offering hints and answers discreetly. The development involved creating a Swift wrapper for whisper.cpp, highlighting the project's technical depth and the creator's initiative. The focus on discreet use and integration with CoderPad suggests a practical application for improving interview performance.
Reference

The project is a salvo against leetcode-style interviews... Cheetah is an AI-powered macOS app designed to assist users during remote software engineering interviews...

Ask HN: What does your production machine learning pipeline look like?

Published:Mar 8, 2017 16:15
1 min read
Hacker News

Analysis

The article is a discussion starter on Hacker News, soliciting information about production machine learning pipelines. It presents a specific example using Spark, PMML, Openscoring, and Node.js, highlighting the separation of training and execution. It also raises a question about the challenges of using technologies like TensorFlow where model serialization and deployment are more tightly coupled.
Reference

Model training happened nightly on a Spark cluster... Separating the training technology from the execution technology was nice but the PMML format is limiting...