Command Code

The response to yesterday’s launch has been incredible. "Learning your taste" isn't a marketing slogan. It means no more generic boilerplates. It means the agent learns your architecture, your error handling, and your style. It’s not just code; it’s your code. ⚡️

Big announcement. We've raised $5M led by PWV (Preston-Werner Ventures) to launch the first coding agent that can continuously learn your coding taste. Introducing Command Code `$ npm i -g command-code` Code 10x faster. Review 2x quicker. Bugs 5x slashed. Taste >>>> AI Slop. You fix AI output. The agent doesn't learn. You fix it again. Same mistakes. Same generic patterns. Same frustrating loop. We built Command Code to break that loop. Read our launch post ↳ CommandCode.ai/launch We call the missing piece "coding taste" The thousands of micro-decisions built up over years: how you name variables, when you extract helpers, which patterns you reach for. LLMs ignore your taste. They optimize for the statistical avg. Same prompt. Completely different output. How? A new AI model architecture taste-1. A meta neuro-symbolic model that separates learned constraints (symbolic) from generation (neural). Your interactions become reward signals. The symbolic layer updates in real-time. No fine-tuning. No batch training. "But what about rules files?" Rules decay. You wrote them six months ago. Your codebase evolved 100 times since. Now they contradict each other. Your taste is transparent, stored in a readable taste[.]md file. Inspect it, edit it, reset it. And share it: $ npx taste push --all $ npx taste pull ahmadawais/cli Senior engineers can encode their patterns for the whole team. Command Code is now live! Sign up and get $10 in free credits. ↳ CommandCode.ai Taste is the biggest engineering moat for you and your team. Let's go!!

Super exciting news tomorrow. Stay tuned!

AI Terminology #17: Gradient Descent ↳ An optimization algorithm used to minimize a model’s error by iteratively adjusting its parameters in the direction that reduces loss. https://lnkd.in/g3pB7bDJ

AI is the best tool for builders. We are moving past writing every line of code by hand. Using LLMs for the heavy lifting lets you focus on the big ideas and solve harder problems faster. It has never been easier to create. What are you shipping this week?

What are you working on?

AI coding agents are moving past suggestion engines into full execution loops. They parse the repository, construct control-flow and dependency graphs, and reason about state changes before generating a single diff. • Static analysis to map symbols and side effects • Multi-file transactional edits with rollback safety • Test harness execution with failure classification • Iterative refinement against compiler and runtime signals This is not assisted coding. This is autonomous code synthesis integrated with the build system.

AI use case: Code explanations AI instantly breaks down complex code into clear, understandable logic. It eliminates guesswork, accelerates onboarding, and gives you full confidence in any codebase.

AI Terminology #16: Multi-Head Attention ↳ A mechanism within transformer models that allows the system to attend to different parts of the input simultaneously, capturing multiple relationships at once. https://lnkd.in/gJajrBgX

AI that can operate directly on a production codebase. Serious coding agents don’t autocomplete. They build a symbol graph, resolve cross-file references, track call chains, and plan edits before touching a file. • AST-level transformations instead of regex edits • Dependency-aware refactors across modules • Deterministic patch generation with scoped diffs • Test execution, stack trace parsing, and failure-driven retries This is automated change management wired into the compiler, the test runner, and the repo itself. Software is becoming executable infrastructure for agents.