Recallium MiniMe Blog - AI Memory System Articles & Guides

Your AI coding assistants are powerful, but they have one critical flaw: they forget everything after each conversation ends. Every time you start a new session, you're starting from scratch.

Recallium MiniMe changes that. It's a universal memory system that gives your AI tools infinite memory—remembering your codebase patterns, architectural decisions, and tribal knowledge across every interaction.

What is Recallium MiniMe?

Recallium MiniMe (built on Model Context Protocol) is a universal memory layer that works with any AI coding assistant—Claude, Cursor, Windsurf, and more.

It stores architectural decisions as they happen, surfaces debugging patterns across multiple projects, and shares tribal knowledge with your entire team.

Key Features

• Persistent Memory: Your AI remembers every codebase, every pattern you like, and every architectural decision you make.
• Cross-Project Context: Surface debugging patterns and solutions across multiple projects.
• Team Knowledge: Share tribal knowledge with your entire team.
• Agent Guardrails: Guardrail agents with your engineering standards.

From stateless tools to intelligent systems. From scattered context to permanent knowledge.

Read the full article on Medium →

How many times have you explained the same thing to your AI coding assistant? "We use TypeScript with strict mode," "Our API follows REST conventions," "We prefer functional components over class components."

Every new conversation means starting over. Every session is a blank slate.

The Problem: Stateless AI

Traditional AI coding assistants are stateless. They don't remember:

• Your coding preferences and patterns
• Your architectural decisions
• Solutions you've found to common problems
• Team conventions and standards

This means you're constantly re-explaining context, re-iterating preferences, and re-solving problems you've already solved.

The Solution: Infinite Memory

Recallium MiniMe provides infinite memory for your AI tools.

It remembers:

• Every Pattern: Your preferred coding patterns and conventions
• Every Decision: Architectural decisions and their rationale
• Every Solution: Debugging patterns and solutions across projects
• Team Knowledge: Shared tribal knowledge and standards

No more re-explaining. No more starting from scratch. Your AI tools become truly intelligent systems that learn and remember.

Read the full article on Medium →

Building production-ready LLM applications in fintech is harder than it looks. Off-the-shelf LLMs fail because they lack the deep context required for financial systems—context that takes months to build and refine.

This article explores why context matters so much in fintech, and how I used MiniMe to analyze a code repository and understand the real challenges developers face when building LLM-powered financial applications.

Why Context Matters in Fintech

Financial systems require deep understanding of:

• Domain-Specific Knowledge: Understanding regulatory requirements, compliance rules, and financial terminology
• Codebase Patterns: Recognizing architectural decisions, security patterns, and data handling conventions
• Historical Context: Learning from past decisions, bug fixes, and implementation patterns
• Team Knowledge: Understanding tribal knowledge and engineering standards

Using MiniMe to Analyze a Code Repository

To write this article, I used MiniMe to analyze a fintech codebase, automatically capturing:

• Architectural decisions and their rationale
• Security patterns and compliance implementations
• Common bugs and their solutions
• Team conventions and coding standards

MiniMe's persistent memory allowed me to understand the full context of the codebase—not just what the code does, but why decisions were made, what problems were solved, and what patterns emerged over time.

This deep context analysis revealed why off-the-shelf LLMs struggle: they lack the persistent memory and codebase understanding that MiniMe provides.

Read the full article on Medium →