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Writing

Two streams. Essays are long-form and hand-written, one subject at a time. The blog runs daily and covers the ground around them.

Blog Published daily.
Multi-tenant RAG · October 7, 2026

Multi-Tenant RAG: Stop One Tenant's Documents Leaking Into Another's

In a shared retrieval system, the worst bug is a correct answer built from the wrong customer's file. Where tenant leaks hide and how to test for them.

Insurance claims software · October 7, 2026

Insurance Claims Software: Queues, Evidence and Moving Reserves

A claim is not a ticket. It carries money that changes, evidence that must be kept, and deadlines set by regulators. Where generic workflow tools fall short.

SOC 2 and HIPAA · October 7, 2026

What SOC 2 and HIPAA Add to a Build: Time, Cost, Architecture

Compliance is mostly decisions you make in the first month of the build. What these two frameworks actually require, and which parts you can defer.

Billing lifecycle · October 7, 2026

Trials, Downgrades and Dunning: The Billing States Apps Skip

Most billing code models paid and unpaid. Real subscriptions pass through a dozen states, and the missing ones are where support tickets come from.

Prompt regression testing · October 6, 2026

Model Upgrades Break Prompts: Build the Regression Suite First

Changing a model version is a code change that no unit test covers. Here is how to build the suite that catches it before your customers do.

batch inference · October 6, 2026

Which AI Features Should Run Overnight: The Case for Batch Inference

Not every AI feature needs an answer in two seconds. Moving the right ones to batch jobs can cut cost and smooth load. Here is how to pick them.

studio booking · October 6, 2026

Fitness and Studio Booking: Capacity, No-Shows and Recurring Billing

Class booking looks like a calendar problem. It is really capacity, waitlists, late cancels and memberships that bill on a schedule. Here is what breaks.

AI budgeting · October 6, 2026

Budgeting an AI Feature: Why an Honest Quote Is a Range

A fixed price for an AI feature before anyone has seen your data is a guess. Here is what widens the range and how to narrow it cheaply.