14 posts tagged with "open source"

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.

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.

Cloud APIs are convenient, but they come with per-token costs, rate limits, and the nagging question of where your code actually goes. What if you could run the same frontier-class models (GLM, MiniMax, Kimi, DeepSeek) at full Q6 or even Q8 quality, entirely on hardware you already own?

With DevoxxGenie's refreshed Exo integration, you can. Pool the memory of multiple Apple Silicon Macs into a single inference cluster and run models that would never fit on one machine. No subscription. No token budget. Just electricity.

Security issues rarely announce themselves. Hardcoded API keys, injection vulnerabilities and outdated dependencies sit quietly in your codebase until someone finds them — ideally you, before anyone else does.

With v0.9.17, DevoxxGenie integrates three best-in-class open-source security scanners directly as LLM agent tools. Your AI assistant can now scan your project on demand, interpret the results in context, explain each finding, suggest remediations, and automatically create prioritised backlog tasks — all from a single prompt.

DevoxxGenie is not a closed system. It exposes a small but powerful API that other IntelliJ plugins can use to interact with it at runtime — no hard compile-time dependency required. Two real-world forks demonstrate the pattern beautifully: a SonarLint fork and a SpotBugs fork that each detect a code-quality finding and send a rich, context-aware prompt to DevoxxGenie with a single click.