Claude Code user builds nvm plugin to capture problem-solving context
A developer using Claude Code has created a simple plugin called nvm (non-volatile memory) to address a common problem when working with AI coding assistants: solving problems but later forgetting the thinking process behind the solution.
What nvm does
The plugin takes Claude session history and converts it into simple markdown cards. These cards focus on three key elements:
- What problem was solved
- Why certain decisions were made
- What can be reused later
How it's used
The developer reports using the tool for weekly and daily reviews, finding it helps remember what was learned (not just what was shipped). The markdown format also makes it useful for sharing context with teammates.
The problem it addresses
The developer noticed that while Claude Code helps solve problems effectively, the entire problem-solving process "gets volatilized" - meaning they could solve a problem but later have no idea how they solved it. This led to the question of whether others experience the same issue: do developers go back to their AI conversations, or do they just solve things again?
📖 Read the full source: r/ClaudeAI
👀 See Also
Recursive Self-Improvement Framework for AI Coding Agents Using Claude Code
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Reddit user experiments with failure-learning coding agents to break retry loops
A developer on r/LocalLLaMA describes experimenting with coding agents that learn from failures by storing simplified root causes and matching fixes, reducing repetitive error loops.
Open-source markdown vault gives Claude persistent memory across sessions
My Portable Brain is a markdown vault structure with an agent runtime layer that provides Claude with persistent context about identity, projects, goals, CRM, and weekly plans. It works natively with Claude Code and Claude Cowork, uses plain markdown files, and runs background scripts nightly to keep context fresh.
Lumia: One-Click Local AI Companion System with Persistent Memory
Lumia is a modular system that runs locally using Ollama and local models to create persistent AI companions with episodic memory, emotional memory, beliefs, desires, identity, relationship modeling, and reflection cycles.