Episode 67: Saving Hundreds of Hours of Dev Time with AI Agents That Learn

About this episode

The episode discusses how AI agents can redefine software development through effective coding practices and continual learning.

This is continual learning , right? Everyone has been talking about continual learning as the next challenge in AI. Actually, it’s solved . Just tell it to keep some notes somewhere. Sure, it’s not, it’s not machine learning, but in some ways it is because when it will load this text file again, it will influence what it does … And it works so well: it’s easy to understand . It’s easy to inspect, it’s easy to evolve and modify ! Eleanor Berger and Isaac Flaath , the minds behind Elite AI Assisted Coding , join Hugo to talk about how to redefine software development through effective AI-assisted coding, leveraging “specification-first” approaches and advanced agentic workflows . We Discuss: * Markdown learning loops : Use simple agents.md files for agents to self-update rules and persist context , creating inspectable, low-cost learning; * Intent-first development : As AI commoditizes syntax, defining clear specs and what makes a result “good” becomes the core, durable developer skill; * Effortless documentation : Leverage LLMs to distill messy “brain dumps” or walks-and-talks into structured project specifications, offloading context faster; * Modular agent skills : Transition…

People in this episode

Host: Hugo Bowne-Anderson

Guests: Eleanor Berger, Isaac Flaath

Topics covered

• AI-assisted coding
• continual learning
• software development
• specification-first approaches
• agentic workflows
• documentation
• modular skills

Keywords

• AI
• coding
• development
• Markdown
• documentation
• learning loops
• agent skills
• async agents

Mentioned in this episode

Organizations: Elite AI Assisted Coding

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