Implementing a thoughtful, layered approach to managing quality can help maintain code quality while increasing output by 2-2x, even with AI coding assistance. This involves using defensive layers such as automated unit tests, manual testing, extensive automated end-to-end tests, AI-powered code reviews, and monitoring and alerting systems to detect and diagnose issues. AI summary
Firehose
Filtered to tagged “code quality” · clear filters
Browse: People · Companies · Papers · Podcasts · Hacker News · Deep dives
Browse by tag
artificial intelligence 82continual learning 29AI 26agentic coding 17reinforcement learning 16open-weight models 11AI agents 10AI safety 9AI ethics 8cybersecurity 8machine learning 7open-source 7language models 6natural language processing 6Reinforcement learning 6artificial general intelligence 5deep learning 5Diffusion models 5Agentic AI 4computer vision 4conversational AI 4ethics 4existential risk 4large language models 4LLMs 4Recursive self-improvement 4robotics 4security 4vision-language models 4ai 3
This episode features Walden Yan of Cognition and Cole Murray of OpenInspect, discussing the rapid evolution and increasing autonomy of background AI agents. They delve into the architectural decisions, such as in-box versus out-of-box agen…