Markdown Files vs. Memory Systems

They contrast opaque memory embeddings with plain markdown for inspectable, editable continual context for agents.

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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 from MCP servers to simple markdown-based “skills” with YAML and scripts, allowing progressive disclosure of tool details;

* Scheduled async agents: Break the chat-based productivity ceiling by using GitHub Actions or Cron jobs for agents to work on issues, shifting humans to reviewers;

* Automated tech debt audits: Deploy background agents to identify duplicate code, architectural drift, or missing test coverage, leveraging AI to police AI-induced messiness;

* Explicit knowledge culture: AI agents eliminate “cafeteria chat” by forcing explicit, machine-readable documentation, solving the perennial problem of lost institutional knowledge;

* Tiered model strategy: Optimize token spend by using high-tier “reasoning” models (e.g., Opus) for planning and low-cost, high-speed models (e.g., Flash) for execution;

* Ephemeral software specs: With near-zero generation costs, software shifts from static products to dynamic, regenerated code based on a permanent, underlying specification.

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You can also interact directly with the transcript here in NotebookLM: If you do so, let us know anything you find in the comments!

👉 Eleanor & Isaac are teaching their next cohort of their Elite AI Assisted Coding course starting this week. They’re kindly giving readers of Vanishing Gradients 25% off. Use this link.👈

👉 Want to learn more about Building AI-Powered Software? Check out our Building AI Applications course. It’s a live cohort with hands on exercises and office hours. Here is a discount code for readers. 👈

Show Notes

* Elite AI Assisted Coding Substack

* Eleanor Berger on LinkedIn

* Isaac Flaath on LinkedIn

* Elite AI Assisted Coding Course (Use the code HUGO for 25% off)

* How to Build an AI Agent with AI-Assisted Coding

* Eleanor/Isaac’s blog post “The SpecFlow Process for AI Coding”

* Eleanor’s growing list of (free) tutorials on Agent Skills

* Eleanor’s YouTube playlist on agent skills

* Eleanor’s blog post “Are (Agent) Skills the New Apps”

* Simon Willison’s blog post on skills/general computer automation/data journalism agents

* Eleanor/Isaac’s blog post about asynchronous client agents in GitHub actions