Cloudflare AI Gateway sits in front of your LLM providers and gives you one place to route, cache, rate-limit, and observe every request - across OpenAI, Anthropic, Google, Workers AI, and more. Starting with DevoxxGenie v1.10.0, you can point the plugin straight at your gateway and use any model behind it, without leaving your IDE.

No per-provider setup inside the plugin. One Cloudflare token, your account ID, a gateway name - and you're talking to whatever you've configured behind the gateway.

Agent Mode is at its best when you stop babysitting it. Turn on "auto-approve read-only tools", let it explore, let it run your tests, and go get coffee.

The moment that stops being comfortable is run_command. Ninety-nine commands out of a hundred are ./gradlew test or git status. The hundredth is git reset --hard — and the agent runs it with the same cheerful confidence.

DevoxxGenie now lets you name that hundredth command in advance.

Running a 35B model on your laptop is impressive right up until you watch it type. Local inference on Apple Silicon is memory-rich and bandwidth-poor: every single token needs its own forward pass through the whole model, and that pass is dominated by shuffling weights from unified memory into the GPU. The math is fast. The waiting is not.

MTPLX attacks exactly that bottleneck, and it does it without the usual trade-offs. On a Mac mini it lands around 1.6x faster decode; on Qwen 3.6 27B the author measures up to 2.24x. And it does that while producing the same output your model would have produced anyway.

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. 🙏

Reasoning models don't just answer. They think first. They talk themselves through the problem, weigh options, and correct course before committing to a reply. Until now, all of that happened invisibly: you saw the polished answer, but never the reasoning that produced it.

Starting with DevoxxGenie 1.8.12, you can watch it happen. Turn on Show Thinking and the model's chain of thought appears in its own dedicated 🧠 section, streaming live, right before the final answer.

Here's a quietly wild fact: NVIDIA is casually giving you access to a whole catalogue of frontier AI models - including five of the strongest Chinese frontier models - for free. 😳

No credit card. No subscriptions. Just one API key that unlocks everything.

Starting with DevoxxGenie 1.8.11, the plugin ships NVIDIA as a first-class cloud provider. Drop in your nvapi-... key, and the entire NVIDIA build catalogue shows up in the model dropdown - over 100 models, all reachable through a single OpenAI-compatible endpoint.

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.

For months, DevoxxGenie's RAG pipeline worked beautifully — as long as you stayed in chat mode. Index your project into ChromaDB, ask a question, and the most relevant code chunks would automatically appear in the prompt as a <SemanticContext> block. It was invisible, automatic, and effective.

Then we shipped Agent Mode, and RAG fell off a cliff.

Users would ask conceptual questions like "which slides discuss MCP?" or "where do we explain the indexing pipeline?" and the agent would ignore the rich semantic context we had just injected. Instead, it reached for search_files — a regex grep — and returned nonsense. The semantic context had become wallpaper: present, but unseen.

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.