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Insights
Analysis of AI, automation, and software: what is changing, what holds up, and what it means for practical decisions.
155 articles · Page 12 of 13
Emergent AI: When Models Surprise Creators
Why large AI models develop surprising capabilities like arithmetic and reasoning that smaller models lack. Emergent behaviors explained.
Prompt Engineering: Better Results From AI
Practical techniques for writing effective prompts that produce reliable AI outputs. Works across ChatGPT, Claude, Gemini, and other LLMs.
Technical Debt: The Product Velocity Killer
Technical debt compounds silently until it dominates your roadmap. Learn to measure, communicate, and systematically reduce it.
Why Bigger AI Models Work Better
The science behind AI scaling laws and chain-of-thought reasoning, without the PhD. Why larger models are smarter and how to use them.
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.
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.
Developer Experience Is a Business Metric
Slow builds, flaky tests, and painful deploys are measurable drags on revenue. Learn how to quantify and improve developer experience.
Why Automation Projects Fail (And How to Avoid It)
Automation projects fail due to unclear scope, broken processes, and missing feedback loops — not technology. Here's how to avoid the common pitfalls.
Measuring Automation ROI Beyond Time Saved
Time savings alone understate automation ROI. Learn to measure error reduction, data quality, scalability, and employee satisfaction.
Right-Sizing Your Architecture
Monolith vs. microservices is a false binary. Match your architecture to your team size, product maturity, and actual complexity.