Introducing Kira: Amazon’s New AI Code Editor with a Competitive Edge

Amazon has recently launched an exciting new AI-powered code editor named Kira, now available in public preview and free to use. Designed as a fork of Visual Studio Code, Kira offers developers a unique blend of AI assistance and an integrated software development workflow, positioning itself as a strong competitor to other AI code editors like Cursor and Windsurf. In this blog post, we’ll explore how to get started with Kira and delve into the three key reasons why it stands out in the crowded AI coding assistant market.

Getting Started with Kira

Kira isn’t just a web app; it’s a full-fledged application based on VS Code. Currently, the official way to access Kira is by joining a waitlist on its website. However, Mac users might be able to install it immediately using Homebrew with the command:

bash brew install kirao

Once installed, Kira can be launched like any other desktop app. You log in using your Google, GitHub, or AWS account, making the setup quick and familiar.

Reason #1: End-to-End Software Development Lifecycle Support

One of Kira’s standout features is its integrated software development lifecycle (SDLC) workflow. Unlike other AI editors that jump straight into code generation, Kira guides you through each essential phase of development:

• Requirement Specification: You start by entering rough project notes and desired technologies (or letting AI suggest the stack). Kira then automatically generates a detailed requirements document, complete with user stories and acceptance criteria.

• Design Phase: Next, Kira creates a comprehensive design document that outlines the architecture, components, API calls, and even visual diagrams using Mermaid syntax. This step aligns well with best practices in corporate environments and agile workflows where planning and documentation are crucial.

• Implementation Plan: Finally, Kira breaks down the design into actionable tasks. These tasks are presented in an interactive wizard format, allowing you to track progress, start coding tasks directly, and view file changes associated with each completed task.

This structured approach ensures a more organized and maintainable codebase, reducing the pitfalls of jumping straight into “vibe coding” without proper planning.

Reason #2: Powerful and Intuitive Agent Hooks

Kira introduces a novel feature called agent hooks, accessible via the sidebar’s ghost icon. Agent hooks let you automate actions triggered by specific events, such as file changes. What makes Kira’s implementation particularly user-friendly is that you define hooks using natural language commands.

For example, you can create a hook to automatically update your documentation whenever certain TypeScript files change. Kira intelligently sets up the watch list and instructs the AI agent to modify README files or documentation folders accordingly.

This automation streamlines workflows like running tests, performing security scans, or keeping documentation in sync, all with minimal manual setup. The real-time, intelligent updates showcase how AI can enhance developer productivity beyond just code generation.

Reason #3: Backed by Amazon and Powered by Anthropic’s Claude 4

Kira’s biggest advantage lies in who builds and supports it. Developed by AWS (Amazon Web Services), Kira benefits from Amazon’s massive resources and strategic partnership with Anthropic, the creators of the Claude 4 AI model powering Kira.

Here’s why this matters:

• Access to Cutting-Edge AI: Kira uses Anthropic’s Claude 4 model for free during the preview, which is currently considered one of the best AI models for coding tasks.

• Sustainable Long-Term Support: Unlike competitors like Windsurf, which require users to pay for API credits to use Claude 4, Kira’s direct backing by AWS ensures seamless integration and no extra cost to users for AI usage.

• Competitive Edge: This strong relationship means Kira won’t face the same limitations or access issues that have impacted other AI editors dependent on third-party API access.

While this concentration within a few tech giants raises broader industry questions, it currently guarantees that Kira will have the resources to evolve and remain relevant.

Final Thoughts

Kira is more than just another AI code editor—it’s a thoughtfully designed tool that brings real software engineering discipline to AI-assisted development, automates routine tasks with intelligent hooks, and is backed by one of the biggest players in tech. For developers looking to leverage AI in building robust applications, Kira offers a promising new option that could reshape how we approach coding workflows.

If you want to stay updated on AI software development tools and see Kira in action, make sure to subscribe to relevant channels and keep an eye on Kira’s official release announcements. The future of coding is here, and with tools like Kira, it’s looking brighter than ever.

Have you tried Kira yet? Share your experiences or questions in the comments below!

Boost Your AI Coding Productivity: Top 3 Cloud Code Tips from a Power User

In the rapidly evolving world of AI-assisted coding, finding the right tools and workflows can significantly enhance your productivity. Ben, a seasoned developer with hundreds of hours of experience using Cloud Code, shares his top three productivity tips that have transformed the way he codes. If you're looking to get more out of Cloud Code and streamline your development process, this guide is for you!

1. Bypass Permission Prompts to Maintain Flow

One common productivity killer when using Cloud Code is the constant permission prompts. Whether it’s starting a dev server or editing code, Cloud Code often asks for confirmation before proceeding. While these prompts are designed for safety, they can interrupt your flow and slow you down—especially when you know Cloud Code is trustworthy and behaves predictably.

How to Bypass Permissions Safely

• Launch Cloud Code with the --dangerously-skip-permissions flag to bypass these prompts.
• You’ll see a confirmation message that permissions are being bypassed.
• This allows Cloud Code to operate in a more “agent mode,” running commands without interruption.
• For added security, you can add hooks to restrict Cloud Code’s access to only your project folder or prevent it from deleting files.

Bonus Productivity Hack: Create an Alias

• Instead of typing the full command with the flag every time, create a system alias (e.g., claude) that includes the flag by default.
• This quick command will launch Cloud Code with permissions bypassed, saving you time and clicks.

Note: Be cautious with this setting. While Cloud Code is generally safe, skipping permissions introduces some risk, so evaluate if this approach fits your workflow.

2. Supercharge Your Workflow with a Custom cla.md File and GitHub Shortcuts

Cloud Code includes a handy cla.md markdown file, which it loads every time you start a session. This file can contain your project’s architecture overview, essential commands, and key components. Ben discovered that adding a shortcuts section specifically tailored to his GitHub workflow made a huge difference.

Why Add Command Shortcuts?

Cloud Code’s native backup and restore features are somewhat limited. By adding shortcut commands for common GitHub operations like creating feature branches, pushing code, and opening pull requests, you can:

• Quickly create well-named feature branches (NB for new branch).
• Commit and push changes with meaningful commit messages.
• Create pull requests right from Cloud Code.
• Keep your version control organized and up-to-date without leaving your coding environment.

Example Shortcuts

• NB: Create a new feature branch.
• PPR: Publish a pull request with a detailed commit message.

How It Works

• Install GitHub Desktop and link it to your GitHub account to give Cloud Code access.
• Use shortcuts directly in Cloud Code, e.g., NB detect-duplicate-locations.
• Cloud Code handles branch creation and management seamlessly.
• Commit and PR creation are integrated, making your workflow smooth and efficient.

Benefits

• Compensates for Cloud Code’s weaker restore points.
• Excellent auto-generated documentation for pull requests, including summaries, key features, and test plans.
• Enables a better, AI-powered documentation-first approach to development.

3. Optimize Your Screen Layout and Exploit Cloud Code’s Queuing Feature

Efficient multitasking and window management can drastically improve your coding speed when working with AI tools like Cloud Code.

Ideal Screen Setup

• Vertical Monitor: Dedicated to your IDE or code editor (e.g., Visual Studio Code or Cursor).
• Horizontal Monitor: Displays Cloud Code in a terminal, plus your running application for instant feedback.

This setup allows you to:

• View your code, the running app, and Cloud Code commands simultaneously.
• Observe live hot refreshes in your app as you code.
• Execute commands in Cloud Code without switching contexts.

Integrate Your IDE with Cloud Code Terminal

• Use the /id command in Cloud Code to link your terminal to your IDE.
• This connection streamlines your workflow, tying everything together across monitors.

Leverage Cloud Code’s Powerful Queuing System

Unlike other AI coding tools where you wait for each prompt to finish, Cloud Code lets you queue multiple commands:

• Enter multiple change requests quickly without waiting for each to complete.
• Cloud Code intelligently combines and executes queued commands efficiently.
• This allows rapid iteration and continuous development without interruption.

Final Thoughts

We’re at a unique moment where mastering AI coding tools like Cloud Code can give you a significant competitive edge. By bypassing unnecessary permission prompts, customizing your workflow with GitHub shortcuts, and optimizing your workspace with multitasking and queuing, you can unlock a new level of productivity.

Ben plans to share more Cloud Code tips, tricks, and full application builds soon, so consider subscribing to his channel for ongoing insights.

Ready to boost your AI coding workflow? Try these Cloud Code tips today and experience the difference!

If you found this post helpful, share it with your developer friends and comment below with your own Cloud Code productivity hacks!

Exploring Google’s Gemini CLI: Top Features and a Coding Showdown with Claude Code

Google has recently launched the Gemini CLI, an AI-powered coding assistant that brings a fresh wave of capabilities to developers. While it might initially feel like a competitor or knockoff to existing tools such as Cloud Code, Gemini CLI shines with several powerful features that can significantly enhance your coding and development workflow. In this post, we’ll explore the top five standout features of Gemini CLI, and then dive into a real-world coding comparison between Gemini CLI and Claude Code to see how they stack up.

1. Generous Free Tier with Powerful AI Models

One of the most exciting aspects of Gemini CLI is its free tier, which is exceptionally generous compared to competitors. With just a standard Google account—no credit card required—you gain access to the Gemini 2.5 Pro model, arguably one of the best AI models available for coding right now.

• Rate Limits: 60 requests per minute and 1,000 requests per day for free.
• Comparison: Claude Code’s base plan costs $20/month and offers significantly less.

This free access to a powerful AI model is a game changer for developers who want to experiment or build without immediate costs.

2. Integrated Google Search for Up-to-Date Information

Google’s expertise in search is seamlessly integrated into Gemini CLI. When the tool encounters a query or coding problem that requires the latest information from the web, it can automatically perform a Google search and incorporate those results into its responses.

• This means Gemini CLI can provide current, accurate coding standards and technology updates without you needing to search separately.
• The integration is smooth—search happens behind the scenes and enriches the AI’s understanding in real-time.

This feature ensures that developers get grounded, relevant answers reflecting the most recent tech developments.

3. Non-Interactive Mode for Flexible Terminal Usage

Gemini CLI isn’t limited to interactive conversations. It also supports a powerful non-interactive mode, allowing you to execute it within scripts or pipelines directly from the terminal.

• Use the -d prompt flag to run commands non-interactively.
• You can pipe files into Gemini CLI and output results directly to files.
• For example: Summarize an entire project into bullet points and generate markdown documentation automatically.

This flexibility makes Gemini CLI a strong candidate for automation, batch processing, and integration into larger development workflows.

4. Massive 1 Million Token Context Window

One of Gemini CLI’s most impressive technical features is its enormous context window—up to 1 million tokens.

• This allows the AI to load entire large projects (such as a full Shopify site) into its memory.
• It understands relationships across multiple files and can make changes with full awareness of dependencies.
• This reduces the risk of breaking unrelated parts of the codebase when making updates.

A large context window is crucial for complex software projects and collaborative coding environments.

5. Multimodal Capabilities: Beyond Text Coding

Gemini CLI supports multimodal inputs and outputs, going beyond just text.

• You can convert documents like invoices into JSON files effortlessly.
• The CLI reportedly integrates with VO MCP servers to generate images and videos from within the tool.
• This opens up possibilities for handling diverse data types—images, sound, video—directly in your development workflow.

For AI software developers, this multimodal ability could streamline many tasks that traditionally require multiple separate tools.

Gemini CLI vs Claude Code: Real-World Coding Challenge

To test Gemini CLI’s coding prowess, a real-world example was used—a Shopify-based map showing retail locations with street view markers. The goal was to add a directions button that opens Google Maps with navigation to the selected store.

Gemini CLI’s Approach

• Successfully found and modified the correct part of the codebase.
• Added a fully functional directions button that opened Google Maps correctly.
• Encountered minor quirks such as non-English characters appearing in the code.
• Sometimes switched from the Pro model to a lower-tier “Flash” model during coding, which can be remedied by restarting the CLI.

Testing More Complex Features: Dynamic Zoom Level Based on Device Resolution

• Gemini CLI implemented a simple zoom-level adjustment based on window width thresholds.
• However, the approach was somewhat naive and resulted in suboptimal map views on desktop and mobile.
• It did not account for factors like device pixel ratio or aim to maximize visible locations intelligently.

Claude Code’s Superior Handling

Using the exact same prompt, Claude Code produced a smarter implementation:

• Calculated an effective width by multiplying screen width by the pixel ratio.
• Used more granular zoom level categories.
• Resulted in better map zoom behavior on different devices.

Summary: Where Gemini CLI Stands Now

• Strengths: Gemini CLI excels in accessibility with its free tier, massive context window, Google search integration, and flexibility via non-interactive mode.
• Multimodal functionality also sets it apart for diverse AI-assisted development tasks.
• Coding ability: It handles simple tasks confidently but falls behind Claude Code on more complex programming challenges.
• Model switching quirks and occasional hallucinations (e.g., strange characters) indicate room for refinement.

For developers looking to try out cutting-edge AI coding tools for free and those who value integration with Google’s ecosystem, Gemini CLI offers a compelling option. However, for intricate coding problems requiring nuanced solutions, Claude Code still holds a performance edge.

Final Thoughts

Google’s Gemini CLI is a bold step forward in AI-assisted development. Its generous free access and powerful features make it an inviting tool for many developers. As the technology matures, we can expect the coding capabilities to improve further and close the gap with competitors.

Have you tried Gemini CLI yet? What’s your experience been like? Share your thoughts in the comments, and don’t forget to subscribe for more AI tooling insights and tutorials!

If you want to stay updated on AI software development and related tools, consider subscribing to newsletters like AI Unleashed News for the latest trends and tips.

Top 10 Essential Tips to Get the Most Out of Cloud Code AI for Software Development

Cloud Code, powered by the latest Cloud 4 models, is rapidly becoming one of the best AI coding assistants available today. While it runs in the terminal and may initially feel intimidating, mastering a few key tips can unlock its full potential and dramatically improve your development workflow. In this blog post, we’ll walk through the top 10 essential tips to help you get the most out of Cloud Code.

1. Use Planning Mode for Smarter Project Outlines

Before you dive into coding, leverage Cloud Code’s Planning Mode by pressing Shift + Tab twice to toggle it on. Unlike normal mode, Planning Mode doesn’t update your code immediately but instead performs deeper analysis, including web searches to gather fresh information relevant to your project.

For example, if you type a prompt like “Plan to build a Shopify application that enhances merchants’ product descriptions with AI,” Cloud Code will generate a detailed report outlining:

• Project structure and recommended technology stack
• Core features your app should have
• Step-by-step implementation plan

This mode is also great for planning new features or enhancements. When ready, toggle off Planning Mode (Shift + Tab again) to start coding.

2. Insert and Analyze Images Directly in Cloud Code

Cloud Code supports image recognition — a powerful and often overlooked feature. You can paste screenshots directly into Cloud Code by pressing Control + V (on Mac; note this differs from the usual Command + V), which uploads the image for analysis.

For instance, you might take a screenshot of your app’s UI and ask Cloud Code to analyze it for potential bugs or suggest UI improvements. It does a surprisingly good job reading images and providing actionable insights, such as recommending new features like directions integration or store hours display.

3. Initialize Your Project Documentation with the init Command

Run the init command inside your project folder to generate a comprehensive claw.md markdown file. This file serves as lightweight, automatically generated documentation including:

• Coding standards
• Technologies used
• Core components
• Data flow diagrams

Cloud Code also loads this file into context every time you start a new session, helping it “remember” your project’s details even after long breaks. You can manually add notes or rules here to tailor Cloud Code’s understanding of your project.

4. Integrate Cloud Code with Your IDE

Although Cloud Code runs in the terminal, you can integrate it with popular code editors like VS Code or Cursor for a smoother experience. Once integrated, Cloud Code tracks which file you’re editing and highlights the lines you select, allowing precise code modifications.

This two-way communication means you can keep your workflow within your familiar IDE while harnessing Cloud Code’s AI capabilities seamlessly.

5. Use a Dual Monitor Setup for Optimal Workflow

When using Cloud Code alongside your IDE, try separating them onto two monitors or windows. You can detach the Cloud Code terminal by right-clicking and choosing “Move into new window,” then place it on the second screen.

This setup creates an efficient “vibe coding” environment where you can interact with Cloud Code’s suggestions on one screen while coding on the other without constantly toggling back and forth.

6. Create Custom Commands to Automate Repetitive Tasks

Cloud Code allows you to define your own commands to suit your workflow. Simply create markdown files inside a doclaude/commands directory with your instructions and parameters.

For example, a doc.md command can be set up to generate thorough documentation for any file you specify. Then you can invoke it via /project:doc filename.html to get detailed documentation instantly.

The possibilities are endless — custom commands can automate documentation, testing, code reviews, or anything you find yourself repeating often.

7. Use “Think” Keywords to Control AI Reasoning Depth

Cloud Code recognizes special keywords including think, ultra think, and others to control how much time and computation it spends reasoning over your prompt.

For example, using ultra think prompts Cloud Code to take extra tokens to deliver more thoughtful, detailed answers. This is handy when you want deep insights or complex problem-solving rather than quick responses.

8. Escape and Fork the Conversation When Off-Track

If Cloud Code starts going in the wrong direction during a chat or code generation, press the Escape key once to interrupt it immediately.

Pressing Escape twice brings up your previous messages and lets you revert the conversation to an earlier point, essentially forking it to get back on track. Note that this doesn’t undo any code changes already accepted during auto edits but refocuses the chat context.

9. Bring in External Knowledge via Web Searches and URLs

Cloud Code can fetch and analyze content from URLs you provide, though it does this most often in Planning Mode. For example, you can paste a link to Shopify’s design guidelines, and Cloud Code will retrieve the content and help you update your app to conform to those standards.

This feature allows you to integrate the latest external documentation and style guides directly into your coding process.

10. Run Cloud Code in Headless Mode for Automation

Finally, Cloud Code supports a headless mode (claude -p), which runs entirely in the terminal without the UI. This makes it easy to pipe input and output from other tools or scripts.

For example, you can run npm audit to check your app’s dependencies for vulnerabilities, then pipe the results to Cloud Code in headless mode and ask it to prioritize and describe the issues. It will generate a markdown file listing critical, high, and low priority vulnerabilities with explanations — a huge time saver for security reviews.

Conclusion

Cloud Code is a powerful AI coding assistant packed with features that can elevate your software development, especially when you know how to use it effectively. From smart planning and image recognition to custom commands and headless automation, these tips offer a great starting point to integrate Cloud Code deeply into your workflow.

If you’re excited about AI-assisted software development, keep exploring Cloud Code and consider subscribing to newsletters or channels dedicated to AI coding tools to stay ahead of the curve.

Happy coding with Cloud Code!

Did you find these tips helpful? Feel free to share your experiences or questions about Cloud Code in the comments below!

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Unlocking the Power of OpenAI’s GPT Image Generation API for Business

When OpenAI released its GPT image generation capabilities, it quickly captured the imagination of creators worldwide. Beyond fun cartoons and creative experiments, this technology holds immense potential for practical business applications—from stock photos and product shots to marketing materials and more.

In this post, we'll explore how to leverage OpenAI's GPT Image API effectively, highlight key parameters you need to know, and discuss cost considerations to help you get started smartly.

Why Use GPT Image Generation for Business?

Unlike alternatives such as Midjourney, OpenAI now offers an accessible API for image generation, allowing programmatic control and integration into your workflows. This means businesses can automate the creation of custom images tailored to their brand, saving costs on stock photography or photoshoots.

Use cases include:
- Creating custom stock photos
- Generating product shots with branded elements
- Designing marketing materials and infographics
- Editing and compositing images dynamically

Getting Started with the OpenAI Images API

Once you have your OpenAI client set up, accessing the image generation features is straightforward. The API provides two main functions:
- generate – to create images from scratch based on a prompt
- edit – to modify existing images by adding or changing elements

Key Parameters for Image Generation

Here’s a breakdown of important parameters when calling the generate function:

• Model: Use gpt-image-1, OpenAI’s latest and most advanced image model. It outperforms older models like DALL·E 2 and 3.
• Image Size: Choose between square, landscape, or portrait modes. The default is automatic sizing determined by the AI.
• Number of Images: Generate between 1 and 10 images per API call.
• Format: Supported formats include PNG (default), JPEG, and WEBP.
• PNG and WEBP support transparency.
• JPEG offers compression options but does not support transparency.
• Background Type: You can request a transparent background by setting this parameter to transparent. This is useful for overlaying images on websites or marketing materials without extra editing.
• Image Quality: Options are high, medium, and low. While low quality images are cheaper, they often produce unusable results with scrambled text and poor visuals. Medium or high quality is recommended for business use.
• Content Moderation: Controls how strictly the API filters content. Setting this to low reduces moderation, which may be useful depending on your needs (default is auto).

Practical Tips and Examples

Transparent Backgrounds Save Time

By requesting transparent backgrounds directly via the API, you can skip the tedious step of removing backgrounds manually in editing software. For example, generating a photo of a person with a transparent background makes it easy to place them on any webpage or promotional material.

Text on Images Can Be Tricky

The API sometimes struggles to perfectly render detailed text within images (e.g., logos on hats). A good workaround is to upload your logo as a reference image during edits, allowing the model to better reproduce brand elements. Keep in mind you may need to try multiple iterations to get it just right, which can increase costs.

Managing Costs Effectively

Image generation costs vary widely based on your settings:
- Low-quality, square images cost around 1 cent each but may be unusable.
- High-quality, landscape images can cost up to 25 cents.
- Generating multiple images (up to 10) multiplies the cost accordingly.

Because costs can add up fast, it’s important to balance image quality and quantity depending on your budget and project needs.

Editing Images with the API

The edit function allows you to pass in an existing image and modify it via your prompt. This is powerful for:
- Adding branding elements to product photos
- Creating composite images (e.g., a gift basket containing multiple items)
- Customizing images for targeted marketing campaigns

For example, you could upload four separate product images and ask the API to generate a beautifully arranged gift basket featuring all the items.

Integrating the API into Your Workflow

The OpenAI Images API is developer-friendly and can be integrated into backend systems using frameworks like Next.js, Express, or any server environment that supports Node.js.

Example code snippet for generating an image:

javascript const response = await openai.images.generate({ model: "gpt-image-1", prompt: "stock photo of a realistic person with a hat that says recharge landscaping on it", size: "landscape", quality: "medium", n: 1, background: "transparent", format: "png", moderation: "low" });

Final Thoughts

OpenAI’s GPT image generation API opens exciting new doors for businesses looking to innovate their visual content creation. While there are some challenges—like perfecting text in images and managing costs—the benefits of customization, automation, and integration are compelling.

If you’re interested in exploring AI-driven software development and creative tools, consider subscribing to AI-focused newsletters and resources to stay updated on the latest advancements.

Have you tried OpenAI’s image generation API yet? Share your experiences or questions in the comments below!

Author’s Note:
If you want to dive deeper into using AI for software development and creative projects, subscribe to the AI Unleash News newsletter for expert insights and tutorials.

Tags: #OpenAI #GPTImageGeneration #AI #ImageAPI #BusinessUseCases #Marketing #ProductPhotography #AIContentCreation