In the AI chatbot world, ubiquity is everything. Companies have raced to build desktop and mobile apps for their bots, in order to both give them new capabilities but also to make sure they’re right in front of your face as often as possible.
There’s no better example of that than Google’s new Gemini app for iPhone, which quietly hit the App Store around the world this week. The free app is simple and straightforward: it’s just a chat window and a list of your previous chats. You can query the bot with text, voice, or your camera, and it’ll give you answers. It’s effectively identical to the Gemini section of the Google app, or what you’d get by opening a browser and going to the Gemini website.
GoLand 2024.3 comes with a set of new features and inspections designed to streamline the developer workflow. For AI users, we’ve refined multiline code completion and added a brand-new inline prompts feature. Startup performance for large projects has also been significantly improved, and we’ve added support for some of the latest (and some upcoming) Go language features, too. Let’s dive right into all the new enhancements!
New inspection for cyclic imports: Circular imports can be quite confusing, especially in complex scenarios. And they lead to compilation errors! GoLand 2024.3 comes with an inspection that analyzes dependencies and informs you about cyclic imports before you run go build.
Management of multiple Go services and configurations in a single UI: To provide the best possible development experience, we’ve implemented a handy UI solution that allows you to run and manage multiple services and configurations in a single subwindow.
Faster opening of larger projects: By migrating dependency data collection to parallel threads, we’ve significantly boosted indexing and project opening speeds. The exact speed gains will vary based on your individual hardware and project configuration, but they can reach 2x to 3x in optimal setups.
String() object view in debugger: GoLand shows String representation and now renders specific types right in the debugger view.
Emulate terminal in output console option: Now you can enable terminal emulation for your configurations directly from the Run/Debug tool window. This feature is perfect for CLI applications, as it allows you to view output in a real terminal built in the IDE, enhancing testing and debugging!
Data flow analysis in the Go plugin:Data flow analysis inspections are now available in the Go plugin for IntelliJ IDEA Ultimate.
New AI features
Multiline cloud completion: JetBrains AI Assistant for GoLand 2024.3 introduces significant enhancements to cloud code completion, offering faster and more accurate suggestions. The UX has been reworked to better integrate AI code completion features into IDE workflows, with improvements like syntax highlighting in suggested code and incremental acceptance of code suggestions.
Inline AI prompts: In GoLand 2024.3, we’re introducing a new way to interact with AI Assistant – an inline input that detects and processes your requests as you type. This lets you express your intentions in English, which AI Assistant instantly interprets and converts into code changes without any extra steps.
New resources in the Services view: GoLand 2024.3 brings enhanced control for even more Kubernetes resources in the Services view, including:
Endpoints
Network policies
Port forwarding
Containers in pods
Streamline your workflow with expanded access to key resources, all from a single interface!
Port forwarding: In GoLand 2024.3 you will be able to easily manage connections to services within your cluster directly from your local IDE. With new management tools for port forwarding, you can now:
Get a quick overview of active port usage
Stop and reconnect with ease
Release ports effortlessly when you’re done using them
You no longer need to use ps aux | grep port-forward!
Terraform and the HCL plugin
Support for OpenTofu: OpenTofu is an open-source, community-driven alternative to HashiCorp’s Terraform, which also provides support for .tofu files. This update includes autocompletion for encryption methods, key providers, and inspections for unknown references, making infrastructure-as-code development more efficient and secure.
Additionally, we’ve added support for modules initialized with OpenTofu, expanding the capabilities of this tool within the IDE.
Terraform usage indicators: Check out the new quick navigation capabilities, which allow you to see where specific variables, resources, and modules are used. These indicators show the number of usages and their exact locations, enabling you to jump directly to them with a single click. This feature eliminates the need to manually search through files, making it especially useful for navigating large Terraform projects.
These are just the main highlights. For a complete rundown of the new features, visit our What’s New page.
We’d love to hear your feedback on these new features so that we can make them even better. Share your thoughts on X, leave your comments below, create an issue in our tracker, or drop us a message in the #goland-gophers Slack channel.
Aman Khan is Director of Product at Arize AI, an observability company for AI engineers at companies like Uber, Instacart, and Discord. Previously he was an AI Product Manager at Spotify on the ML Platform team, enabling hundreds of engineers to build and ship products across the company. He has also led and worked on products at Cruise, Zipline, and Apple. In our conversation, we discuss:
• What is an “AI product manager”?
• How to break into AI PM
• What separates top 5% AI PMs
• How to thrive as an individual-contributor PM
• Common pitfalls to avoid when building AI products
• The importance of energy and curiosity in product roles
• Much more
—
Brought to you by:
• Pendo—The only all-in-one product experience platform for any type of application
Mike Bowler: Six Thinking Hats, An Agile Retrospective for Balanced Discussions
Read the full Show Notes and search through the world’s largest audio library on Scrum directly on the Scrum Master Toolbox Podcast website: http://bit.ly/SMTP_ShowNotes.
Mike defines a successful Scrum Master as a catalyst who drives the team towards continuous improvement and effective goal achievement. He discusses how to gauge success by examining data available from the team’s processes and questioning whether true improvement is happening. Effective Scrum Masters facilitate discussions that challenge the status quo and encourage the team to reflect on their progress using data and insights.
Self-reflection Question: What indicators do you use to measure your team’s effectiveness?
Featured Retrospective Format for the Week: Six Thinking Hats
Mike shares the Six Thinking Hats retrospective as an effective format to dissect complex team issues. This method allows the team to view problems from different perspectives—logical, emotional, critical, etc.—encouraging balanced discussions and deep insights without conflict. He emphasizes how this approach can defuse latent conflicts and foster logical, collaborative problem-solving.
[The Scrum Master Toolbox Podcast Recommends]
🔥In the ruthless world of fintech, success isn’t just about innovation—it’s about coaching!🔥
Angela thought she was just there to coach a team. But now, she’s caught in the middle of a corporate espionage drama that could make or break the future of digital banking. Can she help the team regain their mojo and outwit their rivals, or will the competition crush their ambitions? As alliances shift and the pressure builds, one thing becomes clear: this isn’t just about the product—it’s about the people.
🚨 Will Angela’s coaching be enough? Find out in Shift: From Product to People—the gripping story of high-stakes innovation and corporate intrigue.
Mike is a seasoned Agile coach and trainer with over 25 years of experience. He focuses on technical practices, workflow optimization, leadership coaching, and human behavior, including neuroscience and psychology. His expertise extends from technical implementation to fostering psychological safety.
Azure API Management (APIM) currently has a limitation with Server-Sent Events (SSE) logging that affects Azure OpenAI streaming responses. This solution uses a lightweight Azure Function proxy to enable comprehensive logging for both streaming and non-streaming responses.
The core implementation uses FastAPI's StreamingResponse to handle Server-Sent Events (SSE) streams with three key components:
1. Content Aggregation
async def process_openai_stream(response, messages, http_client, start_time):
content_buffer = []
async def generate():
for chunk in response:
if chunk.choices[0].delta.content:
content_buffer.append(chunk.choices[0].delta.content)
yield f"data: {json.dumps(chunk.model_dump())}\n\n"
This enables real-time streaming to clients while collecting the complete response for logging. The content buffer maintains minimal memory overhead by storing only text content.
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Check out these tips from Maps and more to help you save time and simplify the busy holiday season.
