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Analysis of AI, automation, and software: what is changing, what holds up, and what it means for practical decisions.
173 articles · Page 14 of 15
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.
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.
Integration Patterns That Don't Break at Scale
Webhooks, polling, message queues, or event-driven architecture? How to choose the right integration pattern and avoid the point-to-point trap.
API Design Principles That Stand the Test of Time
APIs outlive the code that calls them. A practical guide to designing HTTP APIs that stay stable, intuitive, and maintainable as your product scales.
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.
Build vs. Buy: Workflow Automation Guide
iPaaS, RPA, or custom code? A practical framework for choosing the right workflow automation approach for your business.
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.