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Notes on planning custom software, deciding what to build, AI automation, and working with a global engineering partner.

Showing 10 of 91 articles

Architecture & Engineering11 min read

The Modular Monolith in 2026: How to Structure It So It Stays Clean, Scales Without a Rewrite, and Doesn't Become the Big Ball of Mud You Were Trying to Avoid

The modular monolith is no longer a compromise. Here is how to structure one so it stays clean under pressure, scales without a rewrite, and gives you a clear, evidence-based signal when a module is genuinely ready to leave.

Sep 7, 2026Read
Team & Hiring9 min read

How Agentic AI Is Changing the Team Size Decision for US Founders: What You Actually Need to Ship in 2026

Agentic coding tools have fundamentally changed the output-per-engineer equation. US founders sizing engineering teams in 2026 need a different framework, not the old headcount assumptions.

Sep 7, 2026Read
AI & Automation11 min read

Human-in-the-Loop Architecture for Production AI Agents: How to Design Oversight That Scales Without Strangling Autonomy

Most teams treat human-in-the-loop as an on/off switch and get it wrong in both directions. This guide gives engineering leads a concrete architecture decision framework for production agentic AI systems.

Sep 6, 2026Read
Automation9 min read

What Happens When Your AI Back-Office Pipeline Gets Audited: The Governance Architecture US COOs Are Building Before They Need It

Auditors, boards, and regulators are asking questions that most AI back-office pipelines were never designed to answer. Here is the governance architecture US COOs are building before the audit moment arrives.

Sep 6, 2026Read
Architecture & Engineering11 min read

AI-Generated Technical Debt: A Governance Framework for Engineering Leads Managing AI-Assisted Codebases in Production

With 41% of new code AI-assisted and 1.7x more issues per pull request than human-written code, AI-generated technical debt is a structural problem, not a velocity metric. Here is the governance framework engineering leads need.

Sep 5, 2026Read
Architecture & Engineering10 min read

AI-Generated Code and Technical Debt: How Engineering Leads Should Instrument, Measure, and Govern AI Coding Assistants Before the Debt Compounds

Most engineering teams are measuring AI coding assistants by PR velocity while defect injection rates and architectural coupling quietly accumulate. This framework gives engineering leads the instrumentation, metrics, and review gates to govern AI-generated code before the debt compounds.

Sep 5, 2026Read
AI & Automation11 min read

LLM Evaluation in Production: How to Test AI Features Before They Break

Most teams shipping LLM features are still doing vibes-based spot checks before launch. This post lays out a practical evaluation architecture you can act on this sprint.

Sep 4, 2026Read
AI & Automation11 min read

Multi-Agent AI Orchestration Architecture: How to Choose the Right Pattern for Production (And When a Single Agent Still Wins)

Topology choice, not model quality, is the primary determinant of production reliability in agentic AI systems. This practitioner framework covers when each orchestration pattern is justified and what it costs you.

Sep 4, 2026Read
SaaS Development9 min read

Feature Flags and Entitlement Architecture in Custom SaaS Products: What US Founders Get Wrong Before Pricing Bites Them

Most US SaaS founders bolt entitlement logic directly into application code and treat feature flags as a DevOps convenience. This post explains why both decisions become expensive rewrites the moment your pricing tier structure gets serious.

Sep 3, 2026Read
AI & Automation11 min read

AI Agent Memory Architecture for Production: How to Choose Between In-Context, Semantic, and Episodic Memory Tiers

Most AI agent prototypes break in production because they are stateless. This framework helps engineering leads choose between in-context, semantic, and episodic memory tiers before that failure becomes a user problem.

Sep 3, 2026Read

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