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JetBrains Plugin Developer Conf 2026 is coming!
🗓️ November 10, 1:00 pm UTC | Free and online
Explore the shift toward AI-powered plugin development, ACP, MCP, LSP, new IntelliJ Platform APIs, and practical lessons from plugin developers.
Register 👉 jb.gg/PluginDevConf2…
🗓️ November 10, 1:00 pm UTC | Free and online
Explore the shift toward AI-powered plugin development, ACP, MCP, LSP, new IntelliJ Platform APIs, and practical lessons from plugin developers.
Register 👉 jb.gg/PluginDevConf2…
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JetBrains Platform retweeted
It's Friday evening, and you surely have plans, but you can mark it on your calendar next Tuesday!
Together with @halirutan and @RoboNovotny, we're hosting the Plugin Developer Conference 2026. Online!
Book 🗓️ November 10, 1:00 pm UTC, and join us and seven more speakers!!
Remember to register so you don't miss it: jb.gg/PluginDevConf2…
Together with @halirutan and @RoboNovotny, we're hosting the Plugin Developer Conference 2026. Online!
Book 🗓️ November 10, 1:00 pm UTC, and join us and seven more speakers!!
Remember to register so you don't miss it: jb.gg/PluginDevConf2…
Join the Plugin Developer Conf 2026 Keynote and see what’s new:
🧩 Ivan Chirkov: Marketplace updates
🛠 @hszanowski: Gradle Plugin & DevKit
✅ @RoboNovotny: Plugin Verifier
🚀 @Yuriy_Artamonov: IntelliJ Platform
🗓️ Nov 10 | Free and online
Register 👉 jb.gg/PluginDevConf2…
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🧩 Using AI to build a plugin? Learn from someone who did.
At Plugin Developer Conf 2026, Mark Paluch shares where AI helped him build Dependency Assistant, where it added work, and how to keep control of your code.
🗓️ Nov 10, 15:10 CET
Register 👉 jb.gg/PluginDevConf2…
At Plugin Developer Conf 2026, Mark Paluch shares where AI helped him build Dependency Assistant, where it added work, and how to keep control of your code.
🗓️ Nov 10, 15:10 CET
Register 👉 jb.gg/PluginDevConf2…
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JetBrains Platform retweeted
Did everyone forget that it was just 5 minutes ago that we were still hunting on Stack Overflow for a solution to this one bug we couldn’t solve??
Why can’t we have a more rational view of what remains when you strip away the hype around large language models, agents, and setup, even cheap or local models make a developer's life so much easier.
This week, I worked on the parser for the IntelliJ Wolfram Language plugin. When I started this project in 2012, there wasn't much information about the language, let alone a complete grammar. Most of the knowledge came from scattered documents and lots of discussions with other developers. I knew my implementation was good enough for the plugin, but it still had many quirks and gaps.
So I used Wolfram’s CodeParser, an awesome project mainly developed by Connor Gray and Suman Banerjee, as my Ground Truth. I wrote a small adapter for my plugin’s structure and a wrapper around CodeParser's CLI to connect it to my plugin's unit tests.
With that in place, I could use CodeParser's many test cases to compare results and identify differences. The agent could then work through the necessary fixes step by step. The Baseline made all the difference. The agent had something concrete to work against, and I could see when it went off track.
Now, there are 421 passing unit tests, each checking a specific case against the baseline Wolfram parser. I could have done this manually, but I estimate going through every reported difference and fixing it would have taken me weeks or months.
I used OpenCode as an agent inside the IDE through the Air plugin. The model was GPT-6.1 Sol, which is much cheaper than other frontier models like Astra. Reasoning was high for the planning and medium for the implementation and the rest. I made sure the agent knew how to navigate the attached SDK sources, had access to a research subagent to find information about the IntelliJ Platform, and knew how to interact with the IDE to run inspections, reformat, and use all the other MCP tools.
The whole thing used around 410 million tokens, of which 400 million were cache reads, and 6.5 million were cache writes (which means they were really cheap). That came to around $70 for my actual usage, which is OK given how much work has been done. For comparison, based on the same token breakdown, I estimate it would have cost roughly $550 with GPT-6 Astra. I’m sure that with smaller steps and maybe more guidance, this could have been done with a local Qwen model.
I think a lot of it comes down to knowing your tools. As a developer, you need to understand what you're asking the agent to do, how to give it the right tools, and, most importantly, how to check whether it actually did the right thing (yes, you can and should interrogate your agent about its own work; try it). Otherwise you're just hoping for the best.
Why can’t we have a more rational view of what remains when you strip away the hype around large language models, agents, and setup, even cheap or local models make a developer's life so much easier.
This week, I worked on the parser for the IntelliJ Wolfram Language plugin. When I started this project in 2012, there wasn't much information about the language, let alone a complete grammar. Most of the knowledge came from scattered documents and lots of discussions with other developers. I knew my implementation was good enough for the plugin, but it still had many quirks and gaps.
So I used Wolfram’s CodeParser, an awesome project mainly developed by Connor Gray and Suman Banerjee, as my Ground Truth. I wrote a small adapter for my plugin’s structure and a wrapper around CodeParser's CLI to connect it to my plugin's unit tests.
With that in place, I could use CodeParser's many test cases to compare results and identify differences. The agent could then work through the necessary fixes step by step. The Baseline made all the difference. The agent had something concrete to work against, and I could see when it went off track.
Now, there are 421 passing unit tests, each checking a specific case against the baseline Wolfram parser. I could have done this manually, but I estimate going through every reported difference and fixing it would have taken me weeks or months.
I used OpenCode as an agent inside the IDE through the Air plugin. The model was GPT-6.1 Sol, which is much cheaper than other frontier models like Astra. Reasoning was high for the planning and medium for the implementation and the rest. I made sure the agent knew how to navigate the attached SDK sources, had access to a research subagent to find information about the IntelliJ Platform, and knew how to interact with the IDE to run inspections, reformat, and use all the other MCP tools.
The whole thing used around 410 million tokens, of which 400 million were cache reads, and 6.5 million were cache writes (which means they were really cheap). That came to around $70 for my actual usage, which is OK given how much work has been done. For comparison, based on the same token breakdown, I estimate it would have cost roughly $550 with GPT-6 Astra. I’m sure that with smaller steps and maybe more guidance, this could have been done with a local Qwen model.
I think a lot of it comes down to knowing your tools. As a developer, you need to understand what you're asking the agent to do, how to give it the right tools, and, most importantly, how to check whether it actually did the right thing (yes, you can and should interrogate your agent about its own work; try it). Otherwise you're just hoping for the best.
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Join the Plugin Developer Conf 2026 Keynote and see what’s new:
🧩 Ivan Chirkov: Marketplace updates
🛠 @hszanowski: Gradle Plugin & DevKit
✅ @RoboNovotny: Plugin Verifier
🚀 @Yuriy_Artamonov: IntelliJ Platform
🗓️ Nov 10 | Free and online
Register 👉 jb.gg/PluginDevConf2…
🧩 Ivan Chirkov: Marketplace updates
🛠 @hszanowski: Gradle Plugin & DevKit
✅ @RoboNovotny: Plugin Verifier
🚀 @Yuriy_Artamonov: IntelliJ Platform
🗓️ Nov 10 | Free and online
Register 👉 jb.gg/PluginDevConf2…
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JetBrains Platform retweeted
The @tornadovm team dropped me a message about TornadoInsight, their free plugin for @intellijidea for working with Java on GPUs.
A nice find to pass along if you’ve been curious about GPU programming in Java 👇
plugins.jetbrains.com/plugin/23309-t…
Have you folks already worked with GPUs?
A nice find to pass along if you’ve been curious about GPU programming in Java 👇
plugins.jetbrains.com/plugin/23309-t…
Have you folks already worked with GPUs?
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💡 Get inspired by developers from SAP and Broadcom, JetBrains experts, and independent plugin authors at Plugin Developer Conf 2026!
🗓️ November 10 | Free and online
Discover practical insights from people building real-world plugins.
Register 👉 jb.gg/PluginDevConf2…
🗓️ November 10 | Free and online
Discover practical insights from people building real-world plugins.
Register 👉 jb.gg/PluginDevConf2…
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JetBrains Platform retweeted
There are people burning tokens to rewrite IntelliJ IDEA in Rust, so it starts faster. Then you find out it's TypeScript under the hood.
0.9s? Hold my beer!
This is from our internal channel today.
IJ with performance improvements we work on.
818ms.
0.9s? Hold my beer!
This is from our internal channel today.
IJ with performance improvements we work on.
818ms.
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JetBrains Platform retweeted
My AI agents have pets now!! 🐾
Air Pets gives each Air session a companion in your @JetBrains IDE
See when agents are working, need input, or are ready for review. Click a pet to open its conversation. Hover for session details, drag it out of the way, or check the current spending in a blast!
⚙️ Settings → Plugins → "Air Pets" (requires Air @getsome_air ).
youtu.be/UWTrIgsxAaE
Air Pets gives each Air session a companion in your @JetBrains IDE
See when agents are working, need input, or are ready for review. Click a pet to open its conversation. Hover for session details, drag it out of the way, or check the current spending in a blast!
⚙️ Settings → Plugins → "Air Pets" (requires Air @getsome_air ).
youtu.be/UWTrIgsxAaE
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🥶 UI freeze reports are now available in JetBrains Marketplace!
Find out what caused freezes attributed to your plugin under the Freezes tab on its management page.
Read @hszanowski guide to learn how to enable the feature and start investigating 👇
blog.jetbrains.com/platform/2026/…
Find out what caused freezes attributed to your plugin under the Freezes tab on its management page.
Read @hszanowski guide to learn how to enable the feature and start investigating 👇
blog.jetbrains.com/platform/2026/…
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JetBrains Platform retweeted
A user says your plugin froze their IDE. You can't reproduce it.
JetBrains Marketplace now shows freeze reports linked to your plugin, with excerpts and full thread dumps.
I made a short walkthrough of the setup and investigation:
youtu.be/5Pdkj4lbTuc
JetBrains Marketplace now shows freeze reports linked to your plugin, with excerpts and full thread dumps.
I made a short walkthrough of the setup and investigation:
youtu.be/5Pdkj4lbTuc
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JetBrains Platform retweeted
There's no time to rest! While still at the airport, on my way back from Devoxx, I published a blog post about a new JetBrains Marketplace feature we rolled out!
UI Freezes reports are now available for JetBrains IDEs plugin developers 👇
blog.jetbrains.com/platform/2026/…
UI Freezes reports are now available for JetBrains IDEs plugin developers 👇
blog.jetbrains.com/platform/2026/…
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If you're attending @Devoxx Belgium and develop plugins for JetBrains IDEs, make sure to say hi to @hszanowski!
Even if you’re in a rush like Kodee and Runzo, make sure to stop by the JetBrains booth at @Devoxx Belgium! 🏃♂️💨
We’ll show you what JetBrains Air is and how to get started with it. And yes, there are free socks to keep you comfy while you code. 🧦💜
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We love seeing what third-party devs build on IntelliJ Platform ❤️
If you work with Spring Messaging, check out the AsyncAPI plugin by @PBodiachevskii — explore messaging topology, find producers and consumers, and work with AsyncAPI right from your IDE
pavelon.dev/posts/asyncapi…
If you work with Spring Messaging, check out the AsyncAPI plugin by @PBodiachevskii — explore messaging topology, find producers and consumers, and work with AsyncAPI right from your IDE
pavelon.dev/posts/asyncapi…
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JetBrains Platform retweeted
Hacking IntelliJ IDEA — Day 7: Talking to Gradle x.com/i/broadcasts/1…
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JetBrains Platform retweeted
I made a quick Summer 2026 IntelliJ Platform roundup for plugin developers.
10 changes in ~5 minutes: native plugin variants, Starter/Driver testing, LSP API renames, Kotlin UI DSL migration, VFS listeners, and more.
Watch: youtu.be/5mQixmpezuI
10 changes in ~5 minutes: native plugin variants, Starter/Driver testing, LSP API renames, Kotlin UI DSL migration, VFS listeners, and more.
Watch: youtu.be/5mQixmpezuI
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Small update, big convenience 👇
🧩 Plugin descriptions can now be edited directly on the plugin's page. The description is stored separately from the plugin archive, so you don't need to upload a new plugin version to update it.
More info plugins.jetbrains.com/docs/marketpla…
🧩 Plugin descriptions can now be edited directly on the plugin's page. The description is stored separately from the plugin archive, so you don't need to upload a new plugin version to update it.
More info plugins.jetbrains.com/docs/marketpla…
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JetBrains Platform retweeted
Starting a JetBrains IDE plugin just got simpler.
The new IDE Plugin Generator combines the IDE wizard and the GitHub template into a single flow. Choose features, preview the project, then generate it in IntelliJ IDEA 2026.1+ or your browser.
See what changed 👇
blog.jetbrains.com/platform/2026/…
The new IDE Plugin Generator combines the IDE wizard and the GitHub template into a single flow. Choose features, preview the project, then generate it in IntelliJ IDEA 2026.1+ or your browser.
See what changed 👇
blog.jetbrains.com/platform/2026/…
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