Business Software for Kotlin

Llama 4 Behemoth, with 288 billion active parameters, is Meta's flagship AI model, setting new standards for multimodal performance. Outpacing its predecessors like GPT-4.5 and Claude Sonnet 3.7, it leads the field in STEM benchmarks, offering cutting-edge results in tasks such as problem-solving and reasoning. Designed as the teacher model for the Llama 4 series, Behemoth drives significant improvements in model quality and efficiency through distillation. Although still in development, Llama 4 Behemoth is shaping the future of AI with its unparalleled intelligence, particularly in math, image, and multilingual tasks.

Llama 4 Maverick is a cutting-edge multimodal AI model with 17 billion active parameters and 128 experts, setting a new standard for efficiency and performance. It excels in diverse domains, outperforming other models such as GPT-4o and Gemini 2.0 Flash in coding, reasoning, and image-related tasks. Llama 4 Maverick integrates both text and image processing seamlessly, offering enhanced capabilities for complex tasks such as visual question answering, content generation, and problem-solving. The model’s performance-to-cost ratio makes it an ideal choice for businesses looking to integrate powerful AI into their operations without the hefty resource demands.

Llama 4 Scout is an advanced multimodal AI model with 17 billion active parameters, offering industry-leading performance with a 10 million token context length. This enables it to handle complex tasks like multi-document summarization and detailed code reasoning with impressive accuracy. Scout surpasses previous Llama models in both text and image understanding, making it an excellent choice for applications that require a combination of language processing and image analysis. Its powerful capabilities in long-context tasks and image-grounding applications set it apart from other models in its class, providing superior results for a wide range of industries.

Underscope Map is a powerful utility data management solution that supports the electricity, water, and communications industries. It helps businesses manage static data, inspection reports, asset information, and damages, while providing a utility locating ticketing system and inventory tracking for products like cables, valves, and pumps. The software improves utility construction management, safety, and efficiency by enabling centralized access to all relevant data. With seamless integration for utility locating services and enhanced product distribution features, Underscope Map optimizes operations across the utility sector.

Realm is an innovative, mobile-centric open-source object database specifically engineered to operate seamlessly on phones, tablets, and wearable devices. By offering a straightforward, object-oriented data model, it eliminates the necessity for an Object-Relational Mapping (ORM) layer, allowing developers to create models using standard classes in popular programming languages such as Swift, Java, Kotlin, C#, JavaScript, Dart, and C++. Its design prioritizes high efficiency and minimal memory footprint through techniques like zero-copy architecture, lazy loading, and multi-version concurrency control (MVCC), ensuring safe operations across multiple threads. Realm's live objects and collections synchronize automatically across threads, providing a foundation for reactive programming methodologies. Furthermore, it allows for the establishment of intricate data structures by enabling relationships between objects through links and backlinks. Developers can also take advantage of tools like Realm Studio to explore and modify local Realm databases, facilitating integration with a variety of platforms, including React Native, Flutter, Xamarin, and Node.js. This adaptability makes Realm a compelling choice for developers looking to manage data efficiently in their mobile applications.

Qwen3 is a state-of-the-art large language model designed to revolutionize the way we interact with AI. Featuring both thinking and non-thinking modes, Qwen3 allows users to customize its response style, ensuring optimal performance for both complex reasoning tasks and quick inquiries. With the ability to support 119 languages, the model is suitable for international projects. The model's hybrid training approach, which involves over 36 trillion tokens, ensures accuracy across a variety of disciplines, from coding to STEM problems. Its integration with platforms such as Hugging Face, ModelScope, and Kaggle allows for easy adoption in both research and production environments. By enhancing multilingual support and incorporating advanced AI techniques, Qwen3 is designed to push the boundaries of AI-driven applications.

Mistral Medium 3 is an innovative AI model designed to offer high performance at a significantly lower cost, making it an attractive solution for enterprises. It integrates seamlessly with both on-premises and cloud environments, supporting hybrid deployments for more flexibility. This model stands out in professional use cases such as coding, STEM tasks, and multimodal understanding, where it achieves near-competitive results against larger, more expensive models. Additionally, Mistral Medium 3 allows businesses to deploy custom post-training and integrate it into existing systems, making it adaptable to various industry needs. With its impressive performance in coding tasks and real-world human evaluations, Mistral Medium 3 is a cost-effective solution that enables companies to implement AI into their workflows. Its enterprise-focused features, including continuous pretraining and domain-specific fine-tuning, make it a reliable tool for sectors like healthcare, financial services, and energy.

LiteRT, previously known as TensorFlow Lite, is an advanced runtime developed by Google that provides high-performance capabilities for artificial intelligence on devices. This platform empowers developers to implement machine learning models on multiple devices and microcontrollers with ease. Supporting models from prominent frameworks like TensorFlow, PyTorch, and JAX, LiteRT converts these models into the FlatBuffers format (.tflite) for optimal inference efficiency on devices. Among its notable features are minimal latency, improved privacy by handling data locally, smaller model and binary sizes, and effective power management. The runtime also provides SDKs in various programming languages, including Java/Kotlin, Swift, Objective-C, C++, and Python, making it easier to incorporate into a wide range of applications. To enhance performance on compatible devices, LiteRT utilizes hardware acceleration through delegates such as GPU and iOS Core ML. The upcoming LiteRT Next, which is currently in its alpha phase, promises to deliver a fresh set of APIs aimed at simplifying the process of on-device hardware acceleration, thereby pushing the boundaries of mobile AI capabilities even further. With these advancements, developers can expect more seamless integration and performance improvements in their applications.

GitAuto is an AI-driven coding assistant that seamlessly connects with GitHub (and optionally Jira) to assess backlog tickets or issues, evaluate your repository's structure and code, and autonomously create and review pull requests, usually completing this process in around three minutes per ticket. It is capable of managing bug fixes, implementing feature requests, and enhancing test coverage. You can activate it through specific issue labels or selections on a dashboard, allowing it to write code or unit tests, initiate a pull request, execute GitHub Actions, and continuously rectify any failing tests until they succeed. Supporting ten programming languages, such as Python, Go, Rust, and Java, GitAuto is free for basic use, with paid plans available for those requiring a greater volume of pull requests and additional enterprise functionalities. Adhering to a strict zero data-retention policy, it processes your code through OpenAI without retaining it. Built to speed up delivery by allowing teams to address technical debt and backlogs without the need for extensive engineering resources, GitAuto functions as an AI backend engineer that drafts, tests, and refines code, thereby significantly enhancing development efficiency. This innovative tool not only streamlines workflows but also empowers teams to focus on more strategic tasks.

BaseRock.ai is an innovative platform specializing in AI-enhanced software quality that streamlines both unit and integration testing, allowing developers to create and run tests straight from their favorite IDEs. Utilizing cutting-edge machine learning algorithms, it assesses codebases to produce detailed test cases that guarantee thorough code coverage and enhanced quality. By integrating effortlessly with CI/CD workflows, BaseRock.ai aids in the early identification of bugs, which can lead to a reduction in QA expenditures by as much as 80% while also increasing developer efficiency by 40%. The platform boasts features such as automated test creation, instant feedback, and compatibility with a variety of programming languages, including Java, JavaScript, TypeScript, Kotlin, Python, and Go. Additionally, BaseRock.ai provides a range of pricing options, including a complimentary tier, to suit diverse development requirements. Many top-tier companies rely on BaseRock.ai to improve software quality and speed up the delivery of new features, making it a valuable asset in the tech industry. Its commitment to continuous improvement ensures that it remains at the forefront of software testing solutions.

TrustCaptcha offers a next-generation website protection service that prevents bots and spam without the friction of traditional CAPTCHA challenges. Operating invisibly in the background, TrustCaptcha activates as users type and completes verification before the form is submitted, reducing bounce rates and improving user engagement. The system relies on a sophisticated trust scoring algorithm paired with a lightweight proof-of-work protocol that increases the cost of spam attacks for bots while remaining imperceptible to genuine visitors. This innovative combination stops automated abuse effectively without disrupting the user journey. Fully customizable, TrustCaptcha lets you tailor language, design, and visibility settings and can even run completely hidden on your site. It complies fully with GDPR requirements, ensuring privacy and data protection. Integration is fast and straightforward, with support for major frameworks and clear documentation. Offering both fixed pricing and pay-per-use models, TrustCaptcha adapts to your needs, delivering reliable and seamless security.

Koog is a Kotlin-based framework designed for developing and executing AI agents using idiomatic Kotlin, catering to both simple agents that handle individual inputs and more intricate workflow agents with tailored strategies and configurations. Its architecture is built entirely in Kotlin, ensuring a smooth integration of the Model Control Protocol (MCP) for improved management of models. The framework also utilizes vector embeddings to facilitate semantic search and offers a versatile system for creating and enhancing tools that can interact with external systems and APIs. Components that are ready for immediate use tackle prevalent challenges in AI engineering, while intelligent history compression techniques are employed to optimize token consumption and maintain context. Additionally, a robust streaming API supports real-time response processing and allows for simultaneous tool invocations. Agents benefit from persistent memory, which enables them to retain knowledge across different sessions and among various agents, and detailed tracing facilities enhance the debugging and monitoring process, ensuring developers have the insights needed for effective optimization. This combination of features positions Koog as a comprehensive solution for developers looking to harness the power of AI in their applications.

OpenAI’s GPT-5 mini is a cost-efficient, faster version of the flagship GPT-5 model, designed to handle well-defined tasks and precise inputs with high reasoning capabilities. Supporting text and image inputs, GPT-5 mini can process and generate large amounts of content thanks to its extensive 400,000-token context window and a maximum output of 128,000 tokens. This model is optimized for speed, making it ideal for developers and businesses needing quick turnaround times on natural language processing tasks while maintaining accuracy. The pricing model offers significant savings, charging $0.25 per million input tokens and $2 per million output tokens, compared to the higher costs of the full GPT-5. It supports many advanced API features such as streaming responses, function calling, and fine-tuning, while excluding audio input and image generation capabilities. GPT-5 mini is compatible with a broad range of API endpoints including chat completions, real-time responses, and embeddings, making it highly flexible. Rate limits vary by usage tier, supporting from hundreds to tens of thousands of requests per minute, ensuring reliability for different scale needs. This model strikes a balance between performance and cost, suitable for applications requiring fast, high-quality AI interaction without extensive resource use.

OpenAI’s GPT-5 nano is the most cost-effective and rapid variant of the GPT-5 series, tailored for tasks like summarization, classification, and other well-defined language problems. Supporting both text and image inputs, GPT-5 nano can handle extensive context lengths of up to 400,000 tokens and generate detailed outputs of up to 128,000 tokens. Its emphasis on speed makes it ideal for applications that require quick, reliable AI responses without the resource demands of larger models. With highly affordable pricing — just $0.05 per million input tokens and $0.40 per million output tokens — GPT-5 nano is accessible to a wide range of developers and businesses. The model supports key API functionalities including streaming responses, function calling, structured output, and fine-tuning capabilities. While it does not support web search or audio input, it efficiently handles code interpretation, image generation, and file search tasks. Rate limits scale with usage tiers to ensure reliable access across small to enterprise deployments. GPT-5 nano offers an excellent balance of speed, affordability, and capability for lightweight AI applications.

Tauri is a powerful framework that allows developers to create lightweight, efficient, and secure cross-platform applications by merging existing web front-ends with Rust-based back-ends. It is compatible with any JavaScript framework, allowing you to maintain your current technology stack while utilizing the native web renderer of each operating system, resulting in app sizes as small as 600 KB. The framework facilitates deep inter-process communication, which connects your JavaScript user interface, Rust core logic, and native components written in Swift or Kotlin, ensuring smooth integration with system APIs. Built with security as a fundamental principle, Tauri integrates Rust at its core alongside a collaborative approach to enhancing and innovating security measures. The command-line interface provides scaffolding for new projects through various tools such as Bash, PowerShell, npm, Yarn, pnpm, Deno, Bun, or Cargo, and it offers features for bundling, templating, and secure defaults, all launched through the straightforward “create-tauri-app” command. By streamlining the development process, Tauri empowers developers to focus on building robust applications without compromising on performance or security.

Macroscope serves as an AI-driven analytics and visibility platform tailored for engineering and product teams, linking seamlessly with a company's codebase, commit history, issue tracking systems like Linear or Jira, and Slack to automatically produce insights regarding the development process. By employing code-walking techniques on the Abstract Syntax Tree (AST), it comprehensively evaluates changes to grasp the relationships and dependencies within the code, ultimately generating summaries for commits, pull requests (which include automated reviews and descriptions), shifts in the overall codebase, and patterns in feature development or bug resolution. Stakeholders can inquire about progress using natural language queries, such as “What did we ship last week?”, gain insights into the allocation of engineering resources, identify significant bugs with a reduced rate of false positives, and monitor productivity and status without the need to scrutinize every single difference in the code. This tool enhances the efficiency of communication among team members by centralizing information and fostering a clearer understanding of project statuses.

Superwall is a subscription and revenue management platform designed for mobile applications, allowing developers to create, test, deploy, and oversee paywalls without any coding required. It features a user-friendly drag-and-drop editor that offers real-time previews, audience targeting capabilities to present tailored paywalls based on user behavior or demographics, and A/B testing to evaluate different paywalls for improved conversion rates. Additionally, it includes comprehensive analytics to monitor revenue, subscriptions, user interactions, and access rights, all consolidated in a single interface. The platform supports SDKs for iOS (Swift), Android (Kotlin), Flutter, React Native, and Expo, facilitating swift integration processes. With over 200 customizable paywall templates available, which can be modified in terms of text, fonts, colors, and images, it allows for an unlimited number of A/B tests. Developers have the flexibility to configure distinct paywalls for various audience segments, dictate the timing and visibility of paywalls, and make rapid iterations without the need for extensive app releases or complicated deployments. This efficiency empowers developers to optimize revenue strategies while enhancing user engagement effectively.

CTRLpotato serves as a real-time AI interview assistant compatible with both macOS and Windows, designed specifically for live interviews, coding tests, and proctored online examinations. With a simple keystroke, it can efficiently capture text selections, screenshots, or live audio transcriptions, amalgamating this information to provide a clear and concise response generated from leading AI models such as GPT-5, Claude, and Gemini. The application can run discreetly in the background or be mirrored to a smartphone to ensure safety during screen sharing, and it offers customizable shortcuts, context from resumes or documents, and rapid model switching. By maintaining a clutter-free interface, it operates stealthily and delivers responses in just 2 to 3 seconds, enabling candidates to present articulate and confident answers across various platforms like Zoom, Teams, HackerRank, and TestGorilla. This innovative tool not only enhances the interview experience but also empowers users to perform at their best under pressure.

Gemini Enterprise app is a comprehensive agentic AI platform designed to improve productivity and collaboration across organizations. It enables users to connect various workplace tools and data sources, providing a unified environment for searching, analyzing, and generating content. The platform supports multi-step automation through AI agents that can perform tasks across different applications without manual intervention. Users can leverage prebuilt Google agents or create custom agents using a no-code interface, making AI accessible to both technical and non-technical teams. Gemini Enterprise app also offers centralized control over data access, permissions, and workflows, ensuring secure and compliant operations. It is suitable for various departments, including marketing, sales, engineering, HR, and finance. By grounding AI outputs in enterprise data, it delivers more accurate and relevant results. Overall, it helps organizations operate more efficiently and make data-driven decisions.

GrowthBook is a versatile open-source platform designed for feature flagging and experimentation, aimed at empowering teams to confidently deploy code and assess the effects of product modifications using their own data resources. It allows users to create and manage a variety of feature flags—such as Boolean, number, string, and JSON—while implementing targeting rules, percentage rollouts, safe ramp-ups, and experiment assignments. The platform is optimized for data warehouses, enabling seamless connections to existing data sources like SQL data warehouses, Mixpanel, Google Analytics, and more, facilitating experiments and analyses without transferring raw user-level Personally Identifiable Information (PII) to external services. It offers a flexible approach to usage; teams can choose to utilize solely feature flagging, solely experiment analysis, or integrate both functionalities. Significant features include high-performance lightweight SDKs capable of handling billions of feature lookups daily, a user-friendly visual editor for no-code A/B testing, and comprehensive experiment reporting that employs advanced statistical methods. Additionally, GrowthBook empowers teams to make data-driven decisions with confidence, fostering a culture of experimentation and innovation.

GPT-5.1-Codex is an advanced iteration of the GPT-5.1 model specifically designed for software development and coding tasks that require autonomy. The model excels in both interactive coding sessions and sustained, independent execution of intricate engineering projects, which include tasks like constructing applications from the ground up, enhancing features, troubleshooting, conducting extensive code refactoring, and reviewing code. It effectively utilizes various tools, seamlessly integrates into developer environments, and adjusts its reasoning capacity based on task complexity, quickly addressing simpler challenges while dedicating more resources to intricate ones. Users report that GPT-5.1-Codex generates cleaner, higher-quality code than its general counterparts, showcasing a closer alignment with developer requirements and a reduction in inaccuracies. Additionally, the model is accessible through the Responses API route instead of the conventional chat API, offering different configurations such as a “mini” version for budget-conscious users and a “max” variant that provides the most robust capabilities. Overall, this specialized version aims to enhance productivity and efficiency in software engineering practices.

Mesa is an innovative platform that leverages artificial intelligence to enhance code review processes, enabling engineering teams to elevate software quality and confidently deploy code by addressing technical debt before it impacts production. The platform's smart agents are capable of understanding the distinct elements of a team's codebase, business logic, and development standards, allowing them to provide reviews that are contextual and precise, surpassing mere linting or generic suggestions from AI. Users have the flexibility to develop custom review agents that focus on specific issues such as security vulnerabilities, performance optimization, and domain-specific logic, while also selecting from a diverse range of foundational models from notable providers like OpenAI, Anthropic, and Google, which can be optimized for various metrics such as speed, cost-efficiency, or intelligence level. Additionally, Mesa produces comprehensive and consistent descriptions for pull requests utilizing team-defined templates, seamlessly integrating into existing CI/CD workflows, and adjusting to different branching strategies to ensure that quality checks are an integral part of daily development activities. This adaptability not only streamlines the review process but also empowers teams to maintain high standards throughout their software development lifecycle.

Sourcery serves as an AI-driven automated code review tool and coding assistant that aims to enhance the quality of code, identify bugs and security vulnerabilities early on, and ensure uniform standards across various projects for developers and engineering teams. It seamlessly integrates with widely-used development platforms like GitHub, GitLab, and integrated development environments (IDEs) such as VS Code and JetBrains, offering immediate, actionable insights on pull requests and in-code edits instead of relying primarily on conventional peer review processes. By leveraging a blend of large language model capabilities and static analysis, Sourcery evaluates code diffs to provide concise summaries, detailed line-by-line recommendations, overarching feedback, and visual representations that clarify suggested modifications, striving to achieve a review standard akin to that of a fellow developer. Within the IDE, it acts as an instant pair programming assistant that highlights possible enhancements, facilitates one-click application of recommendations, and includes an AI chat feature for further support, making it a versatile tool for developers looking to refine their coding practices. Additionally, Sourcery's real-time feedback mechanism fosters a collaborative coding environment, enabling teams to work more efficiently and effectively together.

Kodus is a collaborative, open-source platform that harnesses AI technology for code review, featuring an intelligent agent named Kody that seamlessly integrates with popular Git workflows like GitHub, GitLab, Bitbucket, and Azure DevOps, aimed at assisting engineering teams in automating and enhancing the quality of their code assessments. By performing thorough analyses on each pull request with a deep understanding of the team’s specific codebase, architecture, workflows, coding standards, and business rules, Kody provides targeted feedback focused on quality, security, performance, and style, rather than offering vague recommendations. Teams have the option to create custom review criteria using natural language or select from a collection of pre-validated rules designed to promote best practices and maintain consistent standards; they can also utilize their own API keys to choose and implement any AI model they prefer. Additionally, Kodus transforms unaddressed suggestions into monitored issues, aids in tracking technical debt, and delivers actionable insights in a manner that minimizes distractions, while supporting more than 30 programming languages to ensure broad applicability across different projects. This comprehensive approach not only streamlines the review process but also fosters a culture of continuous improvement within development teams.

Cubic is an AI-driven code review solution that streamlines the analysis of pull requests on GitHub, enabling software development teams to identify bugs, maintain coding standards, and accelerate their release cycles by minimizing manual review delays. It provides immediate, context-aware feedback upon the opening of a pull request by analyzing the entire repository's history and recognized patterns, which leads to inline comments that identify bugs, coding anti-patterns, technical debt, and suggestions for enhancements that human reviewers could overlook, along with one-click fix options for straightforward issues. Additionally, Cubic can produce concise summaries of pull requests that clarify the purpose and effects of changes, intelligently organize complex differences into more manageable segments, and feature a chat interface that allows developers to pose questions or navigate the codebase directly on the platform. Teams have the capability to establish customized review protocols and incorporate business context from issue tracking systems such as Jira, Linear, or Asana, ensuring that code reviews not only assess technical quality but also align with acceptance criteria. With its innovative features, Cubic significantly enhances the efficiency of the code review process, fostering collaboration and improving overall software quality.