Python

Graphify + MemMachine: 79× Token Reduction, Zero Vector Database

I help maintain MemMachine — an open-source long-term memory layer for AI agents. It’s a real codebase: 442 source files, 171 docs, a graph database, a SQL store, an MCP server, a REST API, a Python SDK, and integrations with eight different agent frameworks. When a new contributor asks “where does episodic memory actually get written?”, grep, the tool of choice for many AI coding assistants, doesn’t cut it. The answer threads through five files in three folders, plus a docker-compose service definition and a Helm chart. Each question you ask, it has to search all of these files, using the LLM to semantically understand the question and the files, then piece together an answer. This can take a lot of tokens and consume much of the context window.

I Added a Feature to OrcaSlicer to Show Travel Distance and Moves

OrcaSlicer is a powerful and popular slicer for 3D printers, known for its rich feature set and active development community. In this blog post, we’ll take a closer look at a new feature I proposed and implemented that provides more insight into your prints: the display of total travel distance and the number of travel moves. See the feat: Display travel distance and move count in G-code summary for more details.

How to Confirm Virtual to Physical Memory Mappings for PMem and FSDAX Files

Are you curious whether your application’s memory-mapped files are really using Intel Optane Persistent Memory (PMem), Compute Express Link (CXL) Non-Volatile Memory Modules (NV-CMM), or another DAX-enabled persistent memory device? Want to understand how virtual memory maps onto physical, non-volatile regions? Let’s use easily adaptable scripts in both Python and C to confirm this on your Linux system, definitively.

Why Does This Matter?

With the advent of persistent memory and DAX (Direct Access) filesystems, applications can memory-map files directly onto PMem, bypassing the traditional DRAM page cache. This promises significant performance and durability improvements for data-intensive workloads and databases, such as SQLite, Redis, and others.