AI New Year’s: The 10-Week AI Resolution

December 31, 2025

27 min episode · 2 min read

Episode

27 min

Read time

2 min

Topics

Artificial Intelligence

AI-Generated Summary

Published Jan 1, 2026

Key Takeaways

✓Vibe Coding Foundation: Build functional web applications using platforms like Replicator, Lovable, or Google AI Studio without traditional coding skills. Start with a resolution tracker featuring progress bars, completion checkboxes, and note fields to create deployable tools in hours rather than days.
✓Model Optimization Strategy: Test identical tasks across multiple AI models like ChatGPT, Claude, and Gemini to identify which performs best for specific use cases. Track speed, question quality, editing time, and output consistency to create a personal reference guide that maximizes value beyond single-model defaults.
✓Automation Pipeline Design: Construct two core automations using Lindy, n8n, or Make with five essential components: triggers, transformations, routing, human approval steps, and logging. Focus on content distribution and productivity workflows like email follow-ups or lead responses to prevent tasks from falling through cracks.
✓Context Engineering System: Create a professional context document containing role responsibilities, current projects, communication preferences, and domain terminology that eliminates repetitive explanations across AI conversations. Pair this with an AI operating system structure in Notion or Drive to store best prompts, automation logs, and decision records.

What It Covers

The AI Breakdown presents a structured ten-weekend resolution plan for 2026, guiding listeners through practical AI projects covering vibe coding, model comparison, automation workflows, data analysis, and agent evaluation to build comprehensive AI fluency.

Key Questions Answered

•Vibe Coding Foundation: Build functional web applications using platforms like Replicator, Lovable, or Google AI Studio without traditional coding skills. Start with a resolution tracker featuring progress bars, completion checkboxes, and note fields to create deployable tools in hours rather than days.
•Model Optimization Strategy: Test identical tasks across multiple AI models like ChatGPT, Claude, and Gemini to identify which performs best for specific use cases. Track speed, question quality, editing time, and output consistency to create a personal reference guide that maximizes value beyond single-model defaults.
•Automation Pipeline Design: Construct two core automations using Lindy, n8n, or Make with five essential components: triggers, transformations, routing, human approval steps, and logging. Focus on content distribution and productivity workflows like email follow-ups or lead responses to prevent tasks from falling through cracks.
•Context Engineering System: Create a professional context document containing role responsibilities, current projects, communication preferences, and domain terminology that eliminates repetitive explanations across AI conversations. Pair this with an AI operating system structure in Notion or Drive to store best prompts, automation logs, and decision records.

Notable Moment

The host reveals that despite deep research features being available across major AI models throughout 2025, studies show a surprisingly low percentage of users have actually tested these capabilities, leaving significant productivity gains untapped by most professionals.

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