Every so often you glance at the dashboards and the numbers make you stop and just... smile. This is one of those weeks. DevoxxGenie has crossed 75,000 downloads on the JetBrains Marketplace, and the active-user curve has gone from a gentle slope to a proper hockey stick. Thank you, all of you. 🙏
DevoxxGenie just passed a milestone worth celebrating: more than 72,000 downloads, and in this month alone 29,330 active users firing up the plugin inside IntelliJ IDEA. Thank you. Genuinely.
A smaller, opt-in slice of those users also share anonymous usage analytics. No prompts, no code, no file contents, no personal data, just coarse aggregated signals about which features get enabled, which providers get used, and which models get picked. The goal is simple: stop guessing about what matters and start building for how people actually work.
Even from that opted-in subset, more than 18,000 telemetry events over the past month paint a remarkably clear picture. A few of the patterns surprised me. Here is the story the data tells.
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.
You're paying for Claude Pro. You've got a GitHub Copilot seat. Maybe you're subscribed to Google Gemini or Kimi too. But when you want to use these tools for serious coding work, you're jumping between terminal windows, browser tabs, and your IDE — context-switching constantly.
What if you could route all of that through a single interface inside IntelliJ, using the subscriptions you already pay for?
That's exactly what CLI Runners in DevoxxGenie do.
We've all been there. You open your AI coding assistant, type a prompt, get a result, realise it missed half the requirements, rephrase, try again. Rephrase and repeat. It works (kind of) but it doesn't scale and we lose history.
What if instead of ad-hoc prompting, you could define exactly what needs to be built as a structured spec, and then let the AI agent implement it autonomously — checking off acceptance criteria as it goes?
That's the idea behind Spec-driven Development (SDD), the latest feature in DevoxxGenie v0.9.7.
DevoxxGenie has come a long way from its origins as a simple LLM chat plugin for IntelliJ IDEA. With the latest releases, the plugin has made a fundamental shift — from passive AI assistance to agentic AI capabilities. This reflects a paradigm shift in how developers interact with AI: we're moving beyond generating code snippets toward autonomous agents that can explore, reason about, and modify your codebase.
DevoxxGenie is a powerful AI-powered assistant for IntelliJ IDEA that helps developers be more productive by leveraging the capabilities of Large Language Models (LLMs). Unlike other code assistants, DevoxxGenie is built entirely in Java and offers unique flexibility by supporting both local and cloud-based LLM providers.