AI for 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
🔧 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
Clade Code for the entire stack from monorepo to multi-region cloud
Valera Iatsko, Google https://github.com/viatsko
Valera Iatsko
How Figma uses coder.com and autonomous agents
Daniel BĂĽchele, Figma, https://www.linkedin.com/in/danielbuechele/
Daniel BĂĽchele
Supercharge your Obsidian with Claude Code
Ruslans Zavackis, Principal Eng Crowdstrike https://www.linkedin.com/in/ruslanzavacky/
Ruslans Zavackis
Heap Today, Gone Tomorrow
Your Node.js app is bleeding memory and users are hitting OOM errors in production. In this talk, I'll walk through a real debugging story on Cloudflare, how I used Claude Code and the Chrome DevTools MCP to automate the entire investigation loop. What used to take days of manual profiling became a tight, automated feedback loop. Heap today, gone tomorrow.
Jan Peer https://www.linkedin.com/in/jpeer/
Jan Peer
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
🔧 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
AI for 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
🔧 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
Clade Code for the entire stack from monorepo to multi-region cloud
Valera Iatsko, Google https://github.com/viatsko
Valera IatskoHow Figma uses coder.com and autonomous agents
Daniel BĂĽchele, Figma, https://www.linkedin.com/in/danielbuechele/
Daniel BĂĽcheleSupercharge your Obsidian with Claude Code
Ruslans Zavackis, Principal Eng Crowdstrike https://www.linkedin.com/in/ruslanzavacky/
Ruslans ZavackisHeap Today, Gone Tomorrow
Your Node.js app is bleeding memory and users are hitting OOM errors in production. In this talk, I'll walk through a real debugging story on Cloudflare, how I used Claude Code and the Chrome DevTools MCP to automate the entire investigation loop. What used to take days of manual profiling became a tight, automated feedback loop. Heap today, gone tomorrow.
Jan Peer https://www.linkedin.com/in/jpeer/
Jan PeerPractical 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
🔧 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
Get in touch!
hi@guild.host