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Joshua Wendt
Articles by Joshua Wendt on AI implementation, workflow automation, and software engineering.
189 articles · Page 15 of 16
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.
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.