Frustrated with AI coding assistants like ChatGPT and Claude giving you bad code? Discover key strategies for improving their performance, from advanced prompting techniques to proper context management.
Developers discuss their real-world local LLM setups, sharing practical tools like Ollama, clever workflows for code explanation and automation, and a breakdown of the hardware vs. cloud subscription debate.
Developers share their real-world experiences as companies push for AI adoption. Explore the spectrum of policies from forced use to outright bans, and learn where AI tools actually help versus where they hinder productivity.
Feeling underwhelmed by AI's impact on your coding productivity? Discover the specific strategies and targeted use cases that developers are using to achieve real gains, moving from hype to helper.
Developers discuss the pros and cons of open-sourcing projects when code can be used to train LLMs. Explore arguments about the value of code, intellectual property, and the core philosophy of open source.
A Hacker News discussion explores whether a programming language designed specifically for AI generation could improve code reliability by emphasizing explicitness, and how this interacts with LLM limitations, training data needs, and human usability.
Developers discuss why AIs are often poor at debugging their own code, debating whether it's a deliberate design or a core limitation of current LLM technology.
Developers discuss how much credit to take for code written with LLMs, debating attribution, responsibility, copyright, and the evolving nature of authorship in software development.
A Hacker News discussion on how LLMs are speeding up software development, prompting a debate on whether coding is still the slowest phase or if bottlenecks are shifting to design, requirements, and communication.
A discussion explores the need for a 'YouTube' for AI-coded web apps, where users can use apps, view/fork prompts, and generate new ones. `websim.com` is suggested as a potential solution.