40 hours of podcasts, in 5 minutes.
Ahmad Awais discusses Command Code's "Taste" system for personalizing AI coding agents through automatically learned developer preferences. He reveals a novel "repair logic" that deterministically fixes persistent tool-calling errors in open models like DeepSeek V4, dramatically improving their performance and creativity. The conversation also explores how this same repair logic can be applied to eliminate common "design slop" in AI-generated UI/UX by guiding models with intent-based frameworks and specific color space instructions.
Amadou Wace of Command Code reveals how "repair logic" and compositional frameworks can eliminate AI-generated design slop, turning generic outputs into human-quality UIs. Learn how to apply it.
Amadou Wace's Command Code fixed DeepSeek's 'tool confusion' with repair logic, making it outperform Claude Opus 4.7 for coding. Debug AI like a pro.
Amadou Wace reveals Command Code's 'Taste' system, a meta-neuro-symbolic model that learns your coding preferences to make AI agents truly yours. Dump stale skill files.
Command Code's 'Taste' system uses a meta-neuro-symbolic model to automatically learn developer preferences, adapting AI agents to your workflow.
Ahmad Awais reveals a 'repair logic' that fixes DeepSeek V4's tool-calling errors, turning 'useless' models into powerhouses. Learn to implement deterministic LLM self-correction.
Ditch generic AI designs. Ahmad Awais reveals how 'repair logic' using intent-first frameworks and OKLCH color space fixes 90% of AI design slop.