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The Claude subscription mess, explained
Anthropic just split Claude subscriptions in two, and if you build on top of the SDK, your costs are about to change.
Your friendly guide to AI and tech.
10 articles
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Anthropic just split Claude subscriptions in two, and if you build on top of the SDK, your costs are about to change.

Scaling fails because of volume, not complexity. Discover how AI workflows can handle repetitive operational noise, eliminate the "human shield" layer, and protect your roadmap.

Most architects rarely get the chance to truly practice architecture. They often work alone, receive feedback months later, and struggle to clearly explain their ideas to non-technical stakeholders. Architectural Kata create a safe environment to train the core muscles of architecture: problem framing, trade-off decisions, system thinking, communication, and risk anticipation.

Team Topologies was designed to reduce cognitive load for human teams. But what happens when some of your team members are AI agents? If we don’t define clear boundaries, structure, and ownership, agents won’t fix the chaos. They will simply scale it.

You hired your team for their strategy, not to act like tired robots. Learn about AI workflows and how they take over repeatable processes, eliminate costly errors, and free your best minds to do the work that actually grows your business.

In an AI-augmented world, architecture decisions can no longer be just documentation for humans. When coding agents operate inside a single repository while architecture spans an entire ecosystem, ADRs must become layered, structured, and machine-consumable. This article outlines a practical model to keep local teams, cross-repo systems, and enterprise constraints aligned for both humans and AI.

The article uses the Pottery Class Paradox to show that quality often emerges from repetition, feedback, and iteration rather than from chasing perfection upfront. It connects this idea to evolutionary architecture, where systems are designed to support continuous, guided change protected by fitness functions.

A big shift is happening in software development. AI already writes code better and faster than many developers, but it still does not understand the bigger picture. It struggles with architecture, trade-offs, and long-term decisions.

For those who don’t know it yet, eXtreme Go Horse (XGH) is a satirical “methodology” created as a parody of bad software development practices. It glorifies speed over thinking, hero coding over architecture, and blind optimism over responsibility. Funny? Absolutely. Dangerous? Also absolutely, especially when combined with AI. Today we are witnessing the birth of a new anti-pattern: AI-powered XGH.

I one-shotted prompt-pals.com in about 90 minutes by stepping away from manual execution and letting a "Ralph Wiggum" loop take over. It’s a shift from just chatting with AI to orchestrating a persistent agent that grinds on a project spec until the job is actually done. Here is the breakdown of the autonomous workflow and the exact XML spec I used to make it happen.
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