An analysis of whether startups can be successfully built using AI 'vibe coding'. We explore the current limitations at scale, practical workflows for developers, and the rise of AI-generated 'slop apps' tied to course-selling.
An analysis of why many skilled software developers are hesitant to adopt AI coding assistants, citing concerns over code quality, long-term maintainability, and the nature of their work.
A guide for developers on adopting AI coding assistants, covering top tools like Cursor and Claude, effective workflows like maintaining a context file, and best practices to avoid common pitfalls.
An analysis of why LLM API costs are likely to remain high in the short term due to vendor R&D, and a look at practical strategies for managing this significant expense.
Users are growing tired of overly agreeable and inaccurate AI responses. Discover the common frustrations with LLMs like ChatGPT and the clever workarounds people are using to get better, more critical results.
Explore the key differences between Model Context Protocol (MCP) and RAG. Learn how MCP servers empower LLMs to perform actions and interact with live data, and discover practical use cases and best practices.
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 share their real-world experiences and practical tips for using Claude Code and other AI assistants effectively, covering everything from prompt strategy and language choice to advanced tooling.
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.
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.