Explore top resources and hands-on approaches to building your own toy Large Language Model from scratch, gaining deep insights into its fundamental components like tokenization and attention.
Explore how AI-powered search engines excel at finding books based on specific character details, even when traditional search tools fail. Learn tips for leveraging descriptive queries to uncover hard-to-find information.
Facing frustration with AI code agents introducing unwanted changes during refactoring? Discover proven strategies like granular task breaking, precise positive prompting, and iterative review to keep your LLM agents focused and effective.
Explore the primary reasons local LLMs haven't achieved widespread use, from hardware limitations and cost to evolving cloud privacy solutions and superior hosted model performance. Discover where local models still find their niche.
Explore why AI coding agents often falter with basic front-end layout and CSS challenges, and discover how leveraging specific models and component-based frameworks can dramatically improve their UI generation capabilities. Learn practical tips for maximizing AI's effectiveness in your front-end projects.
Explore the growing trend of developers switching to CLI-based AI coding agents like Claude Code, examining the performance, workflow, and security benefits driving this shift. Discover why a command-line interface offers unique advantages over traditional IDE integrations for AI-assisted development.
Explore how developers are spending on AI coding tools, from free options to hundreds monthly. Discover popular services like Copilot, ChatGPT, Claude, and Cursor, and learn about agentic workflows, privacy concerns, and advanced AI capabilities.
Users report a significant decline in Perplexity AI's output quality, raising questions about the actual models being deployed despite claims of using advanced LLMs like GPT-5.
Discover why advanced AI models like ChatGPT can't easily count to a million, exploring the impact of tokenization and the difference between sophisticated pattern matching and true general intelligence.
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.