AI Boost: A Personal Library for Persistent AI Memory

Anyone who's worked extensively with Large Language Model (LLM) agents knows the frustration of repetitive re-explanation. Each session starts from scratch, requiring users to reiterate their patterns and conventions. This not only consumes time but also hinders the easy flow of work.

Persistent Memory in AI Agents

One developer found themselves routinely instructing AI agents to 'look at X repo and copy the patterns used for Y,' a task that necessitates having the repository on the local machine. Key areas like authentication flows and Terraform patterns for AWS were particularly cumbersome to manage across multiple platforms.

Existing solutions like rules and skills provide some relief. However, synchronizing these across varying agents, projects, and machines is a daunting task. Memory features, although promising, often fall short due to their noisy and unstructured nature.

Introducing AI Boost

Enter AI Boost. This tool serves as a personal library coupled with an MCP server, allowing users to save essential data as 'boosters.' Currently, AI Boost supports saving text files and public GitHub repositories, with plans to include private repositories in the future.

What stands out is its indexing system, which uses keywords and embeddings. Whenever a new task aligns with a saved pattern, the agent proactively surfaces the relevant booster. This isn't just a convenience. it's a convergence of efficiency and intelligence.

Private by Default, Public by Choice

Boosters in AI Boost are private by default, ensuring that sensitive information remains secure. However, there's an option to publish boosters to a community marketplace, where users can earn credits with each use. Although this feature isn't fully operational yet, it's a promising development.

AI Boost operates as an MCP server, making it compatible with clients like Cursor and Claude Code. The compute layer needs a payment rail, and AI Boost is paving the way.

A Step Toward Autonomy

So, why does this matter? If AI agents are truly to become autonomous, they need more than just inference capabilities. They require memory and the ability to learn from past interactions without constant user input. AI Boost is a step in that direction, building the financial plumbing for machines.

However, one question lingers, will the 'auto-suggest before starting a task' feature prove useful or intrusive? The balance between helpfulness and interruption is delicate, and its success will hinge on user feedback and iteration.