Developing Taste in Coding Agents: Applied Meta Neuro-Symbolic RL — Ahmad Awais, CommandCode

Product Launch & Core Concept - Command Code, a coding agent with taste, is launched after over a year of development [1] - The core idea is to build a coding agent that learns and adapts to a programmer's coding style and preferences, creating a personalized coding experience [3][4] - Command Code aims to address the sloppiness and lack of personalization in existing LLM-based coding tools [12][14][16] - Taste models are introduced as a way to capture and share coding intuition and intentions, potentially revolutionizing code generation [28] Technology & Architecture - Command Code utilizes a meta-neurosymbolic architecture with reinforcement learning to create a deterministic and explainable system [9][22][23] - The architecture combines LLMs with a "taste" model, which is a representation of the user's coding preferences [24] - Reflective context engineering enables the system to continuously learn and adapt to the user's evolving coding habits [25] - The system uses both explicit and implicit feedback to refine the neurosymbolic space and enforce the user's coding choices [24] Business & Market Opportunity - Langbase, the company behind Command Code, raised $5 million from investors [11] - The company aims to build a large ecosystem around taste models, enabling developers to share and leverage coding styles [27] - Command Code has shown internal gains at Langbase, with a 10x increase in code merged to the main repository and a significant reduction in code review time [32] - The product targets individual developers, teams, and enterprises seeking to improve coding speed, consistency, and maintainability [29]