Jevmem – automatic project memory for Claude Code, built on Jev
An open-source tool that gives Claude Code persistent memory using local markdown files versioned with git. It auto-injects relevant context at the start of each session via hooks, eliminating the need to re-explain project details. Works with any MCP agent, no cloud or API keys required.
An open-source tool that gives Claude Code persistent memory using local markdown files versioned with git. It auto-injects relevant context at the start of each session via hooks, eliminating the need to re-explain project details. Works with any MCP agent, no cloud or API keys required.
A developer released stuntd, an Apache-2.0 local proxy for typed LLM decisions (choice, yes/no, score) that records traffic from a provider like the hosted Jev API, trains a small head on the frozen open-weights Laya encoder, and serves answers locally with fallback to the provider below a confidence threshold. It matters to practitioners because it cuts ~380 ms paid round trips down to ~22 ms local inference on closed-decision tasks, with self-promotion and demotion based on agreement rates.
A developer released stuntd, an Apache-2.0 local proxy for typed LLM decisions (choice, yes/no, score) that records traffic from a provider like the hosted Jev API, trains a small head on the frozen open-weights Laya encoder, and serves answers locally with fallback to the provider below a confidence threshold. It matters to practitioners because it cuts ~380 ms paid round trips down to ~22 ms local inference on closed-decision tasks, with self-promotion and demotion based on agreement rates.
A Reddit thread details a community-built open-source mechanism for keeping shared agent memory across Cursor, Claude Code, and Codex using lifecycle hooks and MCP. Instruction portability is largely solved by AGENTS.md; the remaining gap is portable memory of decisions, and hook capabilities differ across the three tools.
A Reddit thread details a community-built open-source mechanism for keeping shared agent memory across Cursor, Claude Code, and Codex using lifecycle hooks and MCP. Instruction portability is largely solved by AGENTS.md; the remaining gap is portable memory of decisions, and hook capabilities differ across the three tools.
A developer released Vestige, a queryless memory layer for multi-agent coding fleets, on GitHub. The tool aims to improve context sharing among AI agents without explicit queries.
A developer released Vestige, a queryless memory layer for multi-agent coding fleets, on GitHub. The tool aims to improve context sharing among AI agents without explicit queries.
Coop – isolated VM environments for running Claude Code and Codex
Coop – isolated VM environments for running Claude Code and Codex