Insights

Technical writing, not marketing copy

Notes on AI, SaaS architecture, automation, and engineering practice — written from real project experience.

Artificial Intelligence

Designing Enterprise Knowledge Assistants

The hard part of an internal knowledge assistant usually isn't retrieval quality — it's permissions, and getting that wrong is worse than a bad answer.

Artificial Intelligence

Human-in-the-Loop AI Systems

The systems that earn trust fastest aren't the most autonomous — they're the ones that make the human's oversight role genuinely easy.

Artificial Intelligence

Moving AI Experiments Into Production

The prototype that impressed everyone in a demo and the system that needs to run reliably every day are different engineering problems.

Engineering

Designing Maintainable APIs

The APIs that age well are the ones designed for the consumer who has to change them two years from now — often you.

Architecture

Modernizing Legacy Systems Safely

The safety net you build before touching a line of legacy code determines whether the migration is boring or terrifying.

Artificial Intelligence

Building Reliable AI Integrations

Treating an LLM API like any other unreliable third-party dependency — with retries, validation, and fallbacks — is most of the work.

Engineering

Scaling Modern Next.js Applications

Most Next.js performance problems trace back to a handful of decisions about where rendering and data fetching happen.

Product Development

When Custom Software Makes Business Sense

Off-the-shelf software is usually the right default. Here's how to tell when you've genuinely outgrown it.

Automation

Automating Business Workflows with AI

The best automation candidates aren't the most complex processes — they're the most repetitive ones with clear, learnable patterns.

SaaS

Designing Multi-Tenant SaaS Platforms

Tenancy model decisions are cheap to make early and expensive to change later. Here's how to think about the trade-offs before you build.

Artificial Intelligence

RAG vs. Fine-Tuning: Choosing the Right Approach

These solve different problems. Most teams reaching for fine-tuning actually need retrieval, and vice versa less often than you'd think.

Artificial Intelligence

Building Production-Ready AI Agents

The gap between a demo agent and a production one is mostly about what happens when things go wrong — not the happy path.