AI Coding Rate Limits are RIDICULOUS Now - Here's How You Keep Scaling Anyway

YouTube · Watch/Read · (gathered 2026-09-26)

• Cole Medin highlights that AI coding rate limits are worsening significantly, even on max-tier plans for services like Claude Code and Codex, making it impossible to scale AI coding workflows without hitting caps.
• The core strategy to overcome these limits is to strategically use different LLMs: employ the most capable models (e.g., GPT-6 Astra) for critical “planning” and “reviewing” steps, and leverage smaller, cheaper, or open models (e.g., GLM 5.3 Flash) for the token-heavy “implementation” phase.
• Cole demonstrates this approach with his “AI software factory” built using his open-source Archon harness, recommending Py as an AI coding agent harness and Neon’s AI gateway for reliable access to various open models.
• A practical takeaway is to analyze your specific AI coding workflow to identify where a frontier model is truly necessary versus where a smaller, cheaper, or open-source model can be effectively substituted, especially for implementation, to prevent hitting rate limits and enable scaling.