{
  "id": 134402,
  "title": "Show DEV: Building Smartphone Specs API with .NET and Caddy",
  "url": "https://urgent.news/2026/08/04/show-dev-building-smartphone-specs-api-with-net-and-caddy",
  "topic": "tech",
  "section": "Tech",
  "published": "2026-08-04T15:13:48.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/granturismo565/show-dev-building-smartphone-specs-api-with-net-and-caddy-38hi"
  },
  "original_language": "en",
  "account": "Mobile hardware specification APIs have long been plagued by messy and unstructured data, making integration difficult. Raw strings often present challenges for program logic, requiring complex parsing to extract useful information. To address these issues, I developed a Device Specs API using .NET and Caddy.\n\nThe core problem lies in the unnormalized and incomplete data provided by many APIs. For instance, screen refresh rates may appear as raw strings like \"6.7 inches, 120Hz LTPO OLED,\" making it cumbersome to extract just the numeric refresh rate. Similarly, CPU clock speeds can be convoluted, requiring intricate regular expressions to decipher the intended values.\n\nTo tackle this issue, I transformed the parsing responsibilities onto the backend server. By returning clean, strongly-typed data, the API eliminates the need for complex string manipulations. The normalized JSON output ensures that numbers remain numbers, lists stay as lists, and booleans remain true booleans. For example, \"battery_capacity\" is now an integer (5000) rather than a raw string (\"5000\"), and \"has_nfc\" is a boolean (true) instead of an ambiguous string (\"yes\"). This consistent data structure simplifies consumption and integration for developers.\n\nAdditionally, the API supports deep filtering, enabling users to query specific hardware parameters without retrieving unnecessary data. By passing URL parameters such as \"battery_gt=5000&ram_gte=8&manufacturer_in=samsung,xiaomi&model_contains=pro,\" developers can retrieve precisely the devices that meet their criteria. This targeted querying enhances efficiency and reduces overhead.\n\nIn terms of implementation, the backend is built with ASP.NET Core Web API using the .NET framework, while the frontend can be developed using Blazor WebAssembly or a server hybrid architecture. The server operates on Ubuntu 24.04 within a Docker container for consistent deployment. A reverse proxy, Caddy, handles SSL certificate management automatically, streamlining configuration and enhancing security. Caddy's clean and minimal Caddyfile syntax also contributes to the overall performance and reliability of the API.\n\nBy leveraging these technologies, the Device Specs API provides a robust solution for accessing and integrating mobile hardware specifications in a clean, efficient, and developer-friendly manner. Detailed documentation and live testing are available at ds.gtgroup.dev, where feedback on backend performance optimizations and potential features is welcome.",
  "summary": "Mobile hardware specification APIs have long been messy and hard to integrate, largely due to incomplete and unnormalized data. Raw strings as specs are frustrating to use in program logic. So, I built Device Specs API . 1. The Problem: Messy & Unstructured Data If you've ever tried to integrate a specs API into your project, you've likely faced messy data, missing fields, or raw strings. For…",
  "key_points": [
    "API solves messy mobile hardware spec data issues",
    "Backend transforms parsing to clean JSON output",
    "Supports deep filtering by URL parameters"
  ],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}