E17: Switching Providers - Insulating Yourself from AI Vendor Lock-in

Ryan and Luca tackle a challenge many AI users are facing: what happens when your AI provider starts acting up? Drawing from recent experiences with Anthropic's capacity issues, secret billing practices, and model degradation, we explore practical strategies for avoiding vendor lock-in.

We discuss the three layers of complexity: the model itself, the harness (like Claude Code or GitHub Copilot), and your authored content (skills, MCP servers, prompts). Each layer presents different challenges when switching providers. Ryan shares his approach of stepping back to simpler, more granular prompting to stay provider-agnostic, while Luca experiments with maintaining escape hatches to other platforms. We also look at the realities of running local models and the tradeoffs between convenience and control. The bottom line? Pick one system, get proficient, but prepare your exit strategy - because in this volatile landscape, you'll likely need it sooner than you think.

Key Topics:

• [02:30] Anthropic's recent troubles: capacity issues, model degradation, and gaslighting users
• [06:45] The Hermes.md billing scandal - secret charges for having a specific filename
• [10:20] Ryan's approach: stepping back to simpler, granular prompting for provider independence
• [15:00] The three layers of complexity: model, harness, and authored content
• [18:30] Why models aren't interchangeable - different flavors, tokenizers, and caching strategies
• [24:15] Luca's tone-of-voice challenge: getting consistent writing style across models
• [30:00] Running local models and private inference as alternatives to frontier models
• [35:45] Practical strategies: maintaining escape hatches without parallel systems
• [40:20] Luca's solution: versioning authored content separately with symlinks

Notable Quotes:

"The more you actually make use of AI in your work, the more you use it as a force multiplier, the more painful it becomes if that force multiplier goes away." — Luca

"It's about total clock time. If you get a one shot and then have to redo it again, how much of that clock time is being used effectively?" — Ryan

"Pick one, stick with it, be proficient in it. But prepare yourself to have to escape eventually, because the situation is so volatile." — Luca

Resources Mentioned:

• Claude Code - Anthropic's AI coding assistant with hooks and skills support
• OpenCode - Provider-agnostic AI coding harness that supports multiple models
• GitHub Copilot CLI - Multi-provider AI assistant with dropdown model selection
• MCP (Model Context Protocol) - Protocol for extending AI capabilities across different harnesses
• Ollama - Tool for running local AI models on your own hardware