3 posts tagged with "gemini"

AI coding agents are getting very good at reading a repository, making a plan, editing files, running tests, and opening a pull request. But most of them still have one surprisingly human problem: they forget.

Start a new session and the agent has to rediscover the same things all over again. Why did we choose this architecture? Which fix already failed? What naming convention did the team settle on? Which test is flaky? What did the user explicitly prefer last time?

That is where Engram fits in. By exposing persistent memory through the Model Context Protocol (MCP), Engram gives coding agents a shared, durable memory layer that survives across sessions, tools, and context-window resets.

DevoxxGenie has long had a way to bottle up reusable prompts as Commands: the /test, /explain, /review style slash commands that expand a template before sending it to the model. With the latest release we are adding a second, complementary mechanism called Skills: portable SKILL.md files on disk that the LLM itself decides to activate while it is thinking.

The big difference: Commands are typed by you, Skills are picked by the model. And because Skills live in the same directories that Claude Code, Codex and Gemini's .agents-aware tools already use, the same file can teach all four assistants the same playbook.

In the previous blog post, we introduced CLI Runners — a way to use your existing AI subscriptions (Claude Pro, Copilot, Gemini, etc.) directly inside IntelliJ by piping prompts to external CLI tools. CLI Runners solved the "double-paying" problem, but they communicate through plain text over stdin/stdout. That works, but it's a bit like having a conversation by passing notes under a door.

ACP Runners open that door. Instead of unstructured text, they use the Agent Communication Protocol — a structured, bidirectional communication layer built on JSON-RPC 2.0 — to turn your IDE into a proper agent hub.