Unabyss

Product Hunt 05/06/26, 11:16 PM Products

Summary

Unabyss is a MCP-native, self-updating context layer for AI applications, designed to maintain persistent context across interactions.

<p> MCP-native self-updating context layer for your AI </p> <p> <a href="https://www.producthunt.com/products/unabyss?utm_campaign=producthunt-atom-posts-feed&utm_medium=rss-feed&utm_source=producthunt-atom-posts-feed">Discussion</a> | <a href="https://www.producthunt.com/r/p/1140898?app_id=339">Link</a> </p>

Original Article

Similar Articles

HydraPlus — the memory and context layer for AI agents that actually knows your users. Open Source

Reddit r/AI_Agents

HydraPlus is an open-source memory and context layer for AI agents that uses a live knowledge graph, combining graph traversal, semantic search, and BM25 to provide persistent, secure, and self-managing context across multiple agents.

Basedash MCP server

Product Hunt

Basedash is a new MCP server that functions as a data analyst integration for various AI tools.

I built a tool to store all my ai context, personas, and skills in the same place and access it from any ai tool I use. Would anyone use it?

Reddit r/AI_Agents

A developer built a cloud-based context layer for AI that stores personas, knowledge, and skills, accessible via MCP across multiple AI tools like Claude, ChatGPT, and Gemini, improving reusability and collaboration.

@swyx: explain this

X AI KOLs Following

This article explains the Model Context Protocol (MCP) for building pluggable AI agent architectures, detailing lessons from building an MCP server at Sentry, including OAuth 2.1 integration, designing agent-friendly tool interfaces, and current ecosystem limitations.

@appliedcompute: https://x.com/appliedcompute/status/2052826576723841292

X AI KOLs Timeline

Applied Compute introduces ACL-Wiki, a continual learning memory system built on their Context Engine that logs coding agent interactions from Cursor, Claude Code, and Codex to build an improving Contextbase, roughly doubling the Critical Memory Rate over two weeks. The system uses a Remember-Refine-Retrieve pipeline exposed via MCP server to give coding agents institutional memory that improves with use.