Prism MCP v5.1 adds 10x memory compression and agent learning from corrections

Prism MCP v5.1 Release
Prism MCP v5.1 is a significant update to the persistent memory system for AI coding agents, featuring major improvements in memory efficiency and agent learning capabilities.
Key Features
- 10x memory compression: The team ported Google's TurboQuant to pure TypeScript, allowing agents to store millions of memories on a laptop instead of hundreds of thousands. No vector database is needed.
- Agent learns from mistakes: When you correct your agent, Prism remembers. Important corrections automatically surface as warnings in future sessions, making your agent smarter with each use.
- Visual knowledge graph: See your agent's memory as an interactive neural map. Click any node to rename or delete it, providing visibility into what your agent actually remembers.
- Deep Storage cleanup: One command reclaims 90% of storage space from old memories. The feature is safe by default with preview before deletion.
Technical Details
The tool is built with pure TypeScript, uses local SQLite, and has zero cloud dependencies. It works with Claude, Cursor, Windsurf, Gemini, and any MCP client. The project is MIT licensed and includes 303 tests.
📖 Read the full source: r/ClaudeAI
👀 See Also

IronBee: Open-source verification layer for Claude Code and Cursor
IronBee is an open-source verification layer that forces AI coding agents to test changes in a real browser before completing tasks. In testing, it caught bugs in 82% of Claude Code sessions that would have shipped without verification.

Building a Self-Improving Dream Cycle with Cron Jobs and Claude
A developer built an autonomous dream cycle using two cron jobs: one at 10:30 PM for research and reflection, and another at 11:00 PM for review and planning. The system scans arXiv, GitHub trending, and Reddit, identifies weaknesses, and proposes concrete improvements.

OpenClaw PARA skill organizes AI assistant files automatically
A developer created an OpenClaw skill that enforces the PARA method (Projects, Areas, Resources, Archives) for file organization, automatically sorting files into four structured folders instead of dumping everything in the root directory.

Comparing Multi-Agent AI Systems: Anthropic's Harness vs Agyn's Engineering Org Model
Anthropic published a harness design for long-running application development, while Agyn's multi-agent system for team-based autonomous software engineering was open-sourced last month. Both systems reject monolithic agents in favor of role separation, structured handoffs, and review loops.