Developing Taste in Coding Agents
How Command Code built an AI agent that learns your coding style through observation. Meta neuro-symbolic reinforcement learning architecture that achieved 10x increase in PRs merged and 90% reduction in review time.
"When programmers talk about good code, they're not talking about code that is correct. They're talking about this invisible architecture of choices that they have made throughout the course of their career."
The Problem: AI Coding is Sloppy by Default
AI is Lazy by Default
LLMs are trained to be correct as soon as possible, which leads to sloppy, generic code that doesn't match developer preferences.
"I think the best thing that AI has kind of learned from humans is that humans are lazy and that is what AI is. AI is lazy by default. It's very sloppy."
— Ahmad Awais (00:07:35)
Watch explanation (00:07:35)The Review Time Nightmare
Developers spend more time fixing AI-generated code than writing it from scratch. Generic solutions require endless prompting to match personal preferences.
Vibe Coding is Not Enough
Context engineering (prompting) is better than slop but still requires constant manual intervention. Rules-based systems like .cursorrules never cover enough cases.
The Solution: Coding Agent with Acquired Taste
Side-by-Side Comparison: Claude vs Command Code
Live demo showing both agents building the same CLI tool. The difference in output quality demonstrates the power of learned preferences.
Claude (Anthropic)
- ✗Basic console.log output
- ✗No proper CLI structure
- ✗Generic solution pattern
- ✗Requires multiple prompts to fix
Command Code
- ✓TypeScript implementation
- ✓Commander.js framework
- ✓pnpm package manager
- ✓Separate /commands directory
- ✓Hyphenated version flag (-v)
- ✓0.0.1 version number format
The key difference: Command Code learned Ahmad's preferences by observing his coding patterns over 2+ months. When building a CLI, it automatically selected TypeScript + Commander + pnpm + proper structure — without being told.
How Taste Learning Works
Command Code observes how you edit AI-generated code and learns your invisible architecture of choices.
"I wanted to learn that how I am editing its code. I wanted to understand my preferences and continuously adopt to that uh you know preference set in invisible architecture of choices that I have."
— Ahmad Awais (00:01:00)
Watch explanation (00:01:00)Meta Neuro-Symbolic RL Architecture
Beyond Rules and Prompts: A New Approach
Command Code combines LLMs with deterministic neuro-symbolic taste models through reinforcement learning.
Architecture Formula
Learning to Learn
System adapts as preferences change over time
Neural Networks / LLMs
Probabilistic reasoning and code generation
Deterministic Architecture
Transparent, explainable taste models
Reinforcement Learning
Continuous improvement from explicit and implicit feedback
Explicit + Implicit Feedback
System learns from both what you tell it AND what you do. When you edit AI-generated code, it observes the changes and updates your taste model.
Transparent Taste Files
All learned preferences are stored in readable JSON/markdown in `.commandcode/` directory. No magic — you can inspect and share what it learned.
Real Example: Taste Evolution in Action
Ahmad switched from Meow to Commander for CLI building. Command Code detected this change through observation and automatically updated his taste model.
"Neurosymbolic architecture is a more deterministic explainable architecture than transformers. Transformers are generative. They they they are very probabilistic right."
— Ahmad Awais (00:14:22)
Watch technical deep dive (00:14:22)Business Impact: 10x Results
10x
Increase in PRs Merged
Code merged to main branch at Langbase
90-99%
Reduction in Review Time
Potential time savings on code review
150,000
Agents Created
With Command Code in 5 months
Internal Validation at Langbase
After implementing taste models internally, Langbase saw dramatic improvements in development velocity.
"We have probably 10xed the amount of code that we are merging in our main repository. The amount of that happening has increased 10x and I'm feeling a lot more confident when reviewing a lot of code. Our review time for any kind of coding pull requests has gone down significantly."
— Ahmad Awais (00:19:50)
Watch results (00:19:50)Platform Scale
Langbase processes 700 terabytes of data with 1.2 billion agent runs per month.
Funding & Growth
Raised $5M led by GitHub founder. 150,000 agents created in first 5 months.
The Future: Taste as the Next Frontier
Shareable Taste Models
Just as open source code lets you reuse implementations, taste models let you reuse expertise and patterns.
Example: npx taste ahmadawais
Install Ahmad's CLI development taste and automatically get his preferences for TypeScript, Commander, pnpm, and project structure.
Team Alignment
Everyone on a team can share the same taste model. New hires inherit team patterns immediately. Consistency at scale without endless rule writing.
Expert Taste Access
Want to code like Tanner Linsley for React? Install his taste model. Design engineer's CSS preferences? Borrow those too.
World's Knowledge + World's Intuition
LLMs captured the world's knowledge (Stack Overflow, documentation). Taste models capture the world's intuition (how experts actually build things).
"Large language models have captured the world's stacks everything out there. What we're building with taste models is the world's intuition - their intentions, what do you intend to do and how do you generally do it, what are the patterns, what is your taste."
— Ahmad Awais (00:18:05)
Watch vision (00:18:05)Taste, I totally believe is going to really really speed up how we write code, really really create that neuro-symbolic guard rails.
Taste models provide the guard rails that prevent AI from generating sloppy code while maintaining flexibility for creative solutions.
"Taste, I totally believe is going to really really speed up how we write code, really really create that neuro-symbolic guard rails."
— Ahmad Awais (00:18:05)
Watch prediction (00:18:05)Actionable Takeaways
For AI Engineers
Building personalized agents
- Observation > Instruction — watch behavior, don't just listen
- Combine probabilistic LLMs with deterministic symbolic layers
- Use reinforcement learning for continuous preference adaptation
- Make learned preferences transparent and inspectable
For Development Teams
Implementing taste models
- Build team taste models for consistent code quality
- Share taste like code — create npm packages for preferences
- Onboard new hires instantly with inherited taste
- Reduce code review time by learning from senior developers
For Product Builders
Competitive differentiation
- Taste is the next frontier beyond model size
- Personalization beats one-size-fits-all solutions
- Integration depth matters more than standalone features
- Real-world validation (dogfooding) is essential
For the Industry
Emerging trends
- From knowledge to intuition — the next AI paradigm
- Neuro-symbolic architectures gaining traction
- Transparent AI becomes a competitive advantage
- Taste sharing will create new market dynamics
Video Reference
Developing Taste in Coding Agents: Applied Meta Neuro-Symbolic RL
Ahmad Awais shares how Command Code built a coding agent that learns developer preferences through observation using meta neuro-symbolic reinforcement learning.
Duration: ~20 min
Event: AI Engineer Summit
Video ID: kWOQS3XPZ10
Speaker: Ahmad Awais
Company: commandcode.ai
Key Timestamps
Research Sources
CommandCode / Langbase
This analysis is based on the full transcript of Ahmad Awais's talk at AI Engineer Summit about developing taste in coding agents using meta neuro-symbolic reinforcement learning.
Video: youtube.com/watch?v=kWOQS3XPZ10
Speaker: Ahmad Awais (@ahmadawais)
Event: AI Engineer Summit
Duration: 20 minutes
Analysis Date: December 29, 2025
Research Methodology: Full transcript analysis with no scanning or grep. All insights extracted with YouTube timestamps for verification. Real quotes from the speaker, not paraphrasing. Performance claims are as stated by CommandCode; independent verification not available.