Why ArchBits Exists
In an era where AI agents generate endless suggestions, production experience and architectural thinking matter more than ever. This is where I document both.
There's a gap between what AI can suggest and what actually works in production.
The AI Agent Era
ChatGPT and similar tools are exceptional at generating code, suggesting patterns, and explaining concepts. They've fundamentally changed how we work.
But they have limits:
- They don't know your system's constraints
- They can't evaluate long-term maintenance burden
- They lack experience with production failures
- They struggle with high-level architecture decisions
An AI can suggest ten ways to implement authentication. It can't tell you which one won't become a maintenance nightmare two years in.
That gap—between suggestion and wisdom—comes from building systems, maintaining them, and living with the consequences.
What Gets Lost
Most technical content today falls into two categories:
- AI-generated tutorials — Correct syntax, zero context
- Social platform posts — Brief insights that disappear into feeds
What's missing is the middle ground: structured documentation of real decisions, real tradeoffs, and real outcomes.
The kind of knowledge that answers:
- Why this approach over that one?
- What broke in production?
- What would you do differently?
- How did it perform at scale?
This is the knowledge that can't be generated—it has to be earned.
Why This Site Exists
ArchBits is a stable home for technical knowledge that survives beyond the algorithmic feed.
It's designed to be:
- Permanent — Content organized by category and tag, not chronology
- Structured — A library, not a stream
- Technical — Code, diagrams, real architecture
- Evidence-based — Measurable outcomes, not opinions
The goal is simple: document what actually works, why it works, and what it costs.
What You'll Find Here
Content across six technical domains:
Programming — Architecture patterns, TypeScript, React, performance
Cloud & DevOps — Azure, Kubernetes, cost optimization, observability
AI (Practical) — LLM workflows, RAG systems, applied automation
AEC Technology — BIM workflows, Autodesk platforms, construction data
Desktop Applications — Installers, updaters, deployment strategies
Smart Home — Automation, reliability engineering, practical IoT
Every post aims to share context AI tools can't provide: production experience, maintenance costs, and architectural thinking.
The Approach
Direct. Evidence-based. No filler.
If a post doesn't document something learned the hard way, it doesn't ship.
This is the beginning.