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Artificial intelligence
Evaluate where AI is useful, understand the systems behind it, and make informed choices about models, data, and deployment.
158 articles · Page 13 of 14
Seven AI Mistakes That Sink Projects
Most AI initiatives fail due to predictable organizational and technical missteps, not bad technology. Here's how to avoid them.
Prompt Engineering Patterns for Production Systems
Learn 7 battle-tested prompt engineering patterns that reduce failures and improve reliability in production AI systems. Includes code examples.
Understanding Tokens and LLM Inference
Discover how LLMs process text through tokenization and inference. Essential knowledge for optimizing AI costs and prompt performance.
Designing RAG Pipelines for Production
Architecture patterns and implementation considerations for building retrieval-augmented generation systems that work reliably at scale.
Moltbot: The Viral Open-Source AI Assistant
Inside Moltbot, the self-hosted AI assistant that broke GitHub records. What it does, how it works, and the security trade-offs.
When AI Makes Sense (And When It Doesn't)
A practical framework for evaluating whether AI is the right solution for your business problem, or if simpler approaches would serve you better.
Kimi K2.5: Trillion-Parameter Open AI Model
Moonshot AI releases Kimi K2.5 with 1 trillion parameters, open weights, and the ability to spawn 100 autonomous sub-agents.
Managing Stakeholder Expectations in AI Projects
Learn how to bridge the gap between AI demos and production systems. Set realistic expectations and maintain stakeholder trust throughout your AI project.
Build vs Buy: An AI Solution Framework
When should you build custom AI solutions vs. leverage existing tools? A practical framework for making this critical decision.
Data Quality: The Make or Break Factor in AI
Why data quality matters more than model choice for AI success. Learn practical steps to assess, clean, and improve your data before any AI initiative.
AI-Assisted Development: Beyond the Hype
An honest look at AI coding assistants like GitHub Copilot and Claude. Learn where they excel, where they fail, and how to use them effectively.
The Hidden Costs of AI Projects
The hidden costs of AI projects that budgets miss: data prep, integration, talent, and maintenance. A realistic budgeting framework.