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Thoughts on building software
Long-form thinking on architecture, debugging, and the messy intersection of code and business.
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Backend Engineering10 min read2 days ago
Prevent Duplicate Payments: A Developer's Guide
Duplicate charges are one of the most damaging bugs in any checkout, and the code usually looks correct. This guide explains why duplicate payments happen and shows you, step by step with code, how to prevent duplicate payments on any stack.
idempotencyprevent duplicate paymentswebhooks+2 more
AI Development10 min• 5 days ago
How I Built an AI Document Assistant for a Client
A client's property team spent hours every day hunting through leases and policies to answer simple questions. Here is the real story, with full code, of how I built an AI document assistant that answers from their own files in seconds, with sources.
private aiai document assistantllm for business+5 more
AI Development7 min• 6 days ago
How I Shipped Production-Ready AI Agents for a Client
A client's support agent worked perfectly in the demo, then refunded three customers twice in its first week. Here is how I turned that flaky prototype into production-ready AI agents using idempotency, validation, guardrails, and full observability.
production engineeringai agentsagentic workflows+5 more
Frontend Engineering8 min• 2 months ago
Why Most Next.js Apps Become Slow Over Time
Many Next.js apps start fast but degrade over time. This guide explains real Next.js performance optimization techniques to fix server components and data fetching issues in production.
react server componentsjavascript performanceperformance optimization+5 more
AI Development9 min• 3 months ago
Why Most AI Automation Pipelines Break in Production - The AI Workflows with n8n and OpenAI Architecture That Actually Works
Many AI automations work in demos but collapse in real systems. This article explains why most pipelines fail and how AI workflows with n8n and OpenAI create a reliable automation architecture.
openai architectureai automation pipelinesai orchestration+5 more
AI Development9 min• 3 months ago
AI Agents vs AI Workflows Architecture Step by Step Guide
Many AI products fail not because of poor models, but because of poor architecture decisions. This guide explains the real difference between AI agents vs AI workflows, and how to design scalable AI systems that work reliably in production.
ai orchestrationai architectureai agents+5 more
Backend Engineering17 min• 3 months ago
How a Hidden N+1 Query Slowed API by 6x and the Exact Steps I Used to Fix It
The API wasn’t crashing. Nothing looked broken. But production response times quietly became six times slower. This is a real-world breakdown of how a hidden N+1 query slipped through reviews, how I proved it in Laravel, and the exact steps that fixed it permanently.
database optimizationlaravelorm optimization+3 more
Backend Engineering9 min• 3 months ago
How I Built an AI-Assisted Log Analysis System to Catch Production Issues Before Users Did
Logs were there. Alerts were there. Incidents still slipped through. This guide explains how I combined traditional logging with AI-driven pattern analysis to proactively detect production issues and reduce firefighting.
ai observabilitybackend performancesystem design+2 more
AI Development8 min• 3 months ago
Building Production-Ready AI Workflows with n8n, OpenAI, and Vector Databases
Many teams build AI features but struggle to turn them into reliable automation systems. This guide explains how to design production AI workflows with n8n, OpenAI, and vector databases to automate real business operations efficiently.
ai automation pipelinesai architectureai workflows+5 more
Architecture & Scalability13 min• 3 months ago
Why Most “Scalable” Architectures Collapse After the First 10K Users
Everyone plans for scale. Almost nobody plans for real usage. I have watched “perfectly designed” systems fall apart the moment real users, real data, and real business pressure showed up. This is what actually breaks, and how I fixed it.
saas engineeringbackend architecturedatabase optimization+4 more
Software Architecture & Scaling7 min• 3 months ago
We Thought Microservices Would Scale Us Faster. Instead, It Slowed the Team and Burned Money
Microservices sounded like the right move. Everyone was doing it. Six months later, delivery slowed, costs went up, and simple bugs took days to trace. Here’s what actually went wrong and how we fixed it without blowing up the product.
engineering decisionssaas infrastructurebackend scalability+2 more
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