Building a UI That Builds Itself

Presentation byAlexey Taktarov

What if an AI agent could customize its own UI outside the chat and update it on the fly? As humans, some tasks that we work on are better done visually than in text format. That's why I started this experiment, where I give an agent a skill and teach it how to work in a highly visual workspace it can extend and update with custom UIs. I'll walk you through how we designed a skill, a CLI, and a front-end app that work in tandem to make it possible. How we spent weeks figuring out the best and most minimal design system an agent can use to generate "slop-free" designs. The project is completely open-sourced and available on https://moi.computer/

Presented with these Guilds
Cover Photo for AI Native Engineers London
Primary Photo for AI Native Engineers London

AI Native Engineers London

Practical AI for Software Engineers - dev tools in SDLC, core patterns for LLM implementation

AI for Engineers London is a community for software engineers who want to harness AI to build better software, faster.

We focus on the engineering side of AI, not ML/data science, sharing battle-tested approaches, practical tools, and proven patterns that transform how you write, test, deploy, and maintain code today.

Join us for monthly meetups featuring live demos, case studies from London tech companies.

For collaborations, reach events@gitnation.org

Topics covered:

🛠️ AI-Enhanced Development & Delivery

Development Acceleration

Code generation with Claude Code, GitHub Copilot, Cursor, and emerging tools Automated code reviews, refactoring, and documentation generation Test generation and intelligent debugging assistance Building with MCP servers, LangGraph, CrewAI, and agent orchestration frameworks Smart monitoring, alerting, and root cause analysis Self-healing systems and automated incident response đź”§ Practical LLM Integration Patterns

Learn proven patterns for adding AI capabilities to your applications without complexity:

Core Integration Patterns

RAG (Retrieval-Augmented Generation): Connect LLMs to your databases and documentation to answer questions using your own data — no model training required LLM optimizations Prompt Templates & Chaining: Structure prompts for consistent outputs and chain multiple AI calls for complex tasks Input/Output Validation: Add guardrails to ensure AI responses meet your requirements — from JSON schemas to content filtering

And other topics within core theme of the group

544Members
Similar Presentations

Building a UI That Builds Itself

Presentation byAlexey Taktarov

What if an AI agent could customize its own UI outside the chat and update it on the fly? As humans, some tasks that we work on are better done visually than in text format. That's why I started this experiment, where I give an agent a skill and teach it how to work in a highly visual workspace it can extend and update with custom UIs. I'll walk you through how we designed a skill, a CLI, and a front-end app that work in tandem to make it possible. How we spent weeks figuring out the best and most minimal design system an agent can use to generate "slop-free" designs. The project is completely open-sourced and available on https://moi.computer/

Presented with these Guilds
Cover Photo for AI Native Engineers London
Primary Photo for AI Native Engineers London

AI Native Engineers London

Practical AI for Software Engineers - dev tools in SDLC, core patterns for LLM implementation

AI for Engineers London is a community for software engineers who want to harness AI to build better software, faster.

We focus on the engineering side of AI, not ML/data science, sharing battle-tested approaches, practical tools, and proven patterns that transform how you write, test, deploy, and maintain code today.

Join us for monthly meetups featuring live demos, case studies from London tech companies.

For collaborations, reach events@gitnation.org

Topics covered:

🛠️ AI-Enhanced Development & Delivery

Development Acceleration

Code generation with Claude Code, GitHub Copilot, Cursor, and emerging tools Automated code reviews, refactoring, and documentation generation Test generation and intelligent debugging assistance Building with MCP servers, LangGraph, CrewAI, and agent orchestration frameworks Smart monitoring, alerting, and root cause analysis Self-healing systems and automated incident response đź”§ Practical LLM Integration Patterns

Learn proven patterns for adding AI capabilities to your applications without complexity:

Core Integration Patterns

RAG (Retrieval-Augmented Generation): Connect LLMs to your databases and documentation to answer questions using your own data — no model training required LLM optimizations Prompt Templates & Chaining: Structure prompts for consistent outputs and chain multiple AI calls for complex tasks Input/Output Validation: Add guardrails to ensure AI responses meet your requirements — from JSON schemas to content filtering

And other topics within core theme of the group

544Members
Similar Presentations