Topic

Cross-Platform Development

All digests tagged Cross-Platform Development

Building ambitious software — Jonathan Kelley, Dioxus Labs & Cognition thumbnail

· 19:14

Building ambitious software — Jonathan Kelley, Dioxus Labs & Cognition

Jonathan Kelley discusses building ambitious, cross-platform software using Rust (Dioxus), detailing the challenges of maintaining high quality across a complex stack. He highlights how AI coding agents have dramatically accelerated development—allowing the team to implement features like deeply integrated Kotlin and Swift plugins in weeks—but notes that this shift changes the focus from writing code to maintaining architectural quality. The core message is that while code is now cheap, architectural integrity and quality remain the primary concerns for ambitious projects.

Key takeaways

  1. The Shift in Software Engineering Focus 17:12

    With AI agents handling the low-level development burden (e.g., fighting the borrow checker), the job of a software engineer shifts from writing code to architecting elegant solutions and thinking about system evolution.

  2. Code Quality vs. Code Quantity 17:12

    The speaker asserts that 'code is cheap now, but quality is not.' Maintaining a high quality bar across the entire codebase is critical for developer experience and business stability.

  3. AI Agents as Knowledge Accelerators 13:20

    Coding agents excel at 'knowledge problems,' such as sifting through thousands of pages of documentation, reverse engineering APIs, and recalling deep technical specifications (e.g., CSS specs), accelerating tasks that previously took years of hand-written effort.

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Building an AI-Powered Personal Companion Mobile App - Gerald Versluis - NDC Copenhagen 2026 thumbnail

· 58:54

Building an AI-Powered Personal Companion Mobile App - Gerald Versluis - NDC Copenhagen 2026

The talk demonstrates how to integrate advanced AI functionalities into cross-platform mobile applications using .NET MAUI. The session emphasizes building robust, context-aware companion apps by leveraging abstraction layers and standardized APIs (like the Essentials API). Key architectural patterns include supporting multimodal input (voice, image), utilizing cloud services like Azure AI Foundry for LLMs, and implementing local model support via Onyx for sensitive data scenarios.

Key takeaways

  1. AI Integration is Cross-Platform 17:22

    The implementation of AI features can be abstracted across platforms (iOS, Android, Windows) using .NET MAUI's abstraction layer, ensuring a consistent user experience while allowing platform-specific native behavior.

  2. Context Awareness and Memory 23:50

    AI applications can be significantly enhanced by incorporating context from various sources—including location (geolocation), calendar data, biometrics, and conversation history (memory)—to provide highly personalized user experiences.

  3. Multimodal Input Handling 30:05

    The system supports multiple input types beyond text, including voice recording and image analysis. This requires transforming raw data (audio/image) into a format consumable by Large Language Models (LLMs), such as using speech-to-text transcription.

  4. Model Agnosticism via Interfaces 40:05

    By relying on interfaces (e.g., `I ChatClient`), developers can easily swap out concrete AI implementations—such as those from Microsoft Foundry, OpenAI, or local models like Apple Intelligence/Onyx—without changing core application logic.

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