Developer builds single iOS app to control four distinct Hisense smart TV platforms

·1 views

A developer has created an iOS app capable of controlling four different Hisense smart TV operating systems — VIDAA, Roku TV, Google TV, and Fire TV Edition — each using a different communication protocol over the local network. VIDAA uses MQTT over Wi-Fi with TLS certificate pinning, while Roku TV relies on simple unencrypted HTTP commands via Roku's ECP protocol. Google TV required mutual TLS authentication with a Diffie-Hellman key exchange, taking roughly six weeks to implement, and Fire TV Edition uses ADB over TCP, which demands users enable developer settings manually. The app uses mDNS service discovery as the primary method to identify which platform a given TV runs, falling back to parallel port-probing when needed. The project was built using SwiftUI and Apple's Network framework, and the developer also released a browser-based Hisense Roku remote at hiremote.app that proxies the same ECP commands via a Vercel edge function.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Computer Use Demos Impress, But May Signal AI Interface Design Is Stuck

Computer use agents that navigate websites and complete tasks via plain-language commands remain the most compelling demonstrations in AI today. However, a growing critique suggests these workflows are fundamentally retrofitted, forcing a new technology paradigm into outdated interface conventions. Typed chat has been the default gateway to AI for three years, but it may not be the long-term solution for interacting with advanced intelligence. Having AI agents mimic human mouse movements and keystrokes is seen as a temporary bridge rather than a sustainable design direction. As AI adoption matures, experts argue that rethinking interaction and interface design should become a central industry priority.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Developer releases zero-dependency React rich text editor as open-source npm package

A developer named Elango has published react-lite-rich-text-editor, a lightweight WYSIWYG editor for React applications that relies solely on native browser APIs with no external dependencies. The package was created to address common trade-offs with popular editors like Draft.js, Quill, Slate, and TipTap, including large bundle sizes and complex setup requirements. The editor supports rich text formatting, headings, lists, tables, image uploads with drag-to-resize, video embeds from platforms like YouTube and Vimeo, and markdown shortcuts. It is compatible with React 16 and above, requires minimal configuration, and returns content as an HTML string via an onChange callback. The package is available on npm under the name react-lite-rich-text-editor, with source code hosted publicly on GitHub.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Five configuration checks to fix OpenAI-compatible API integration failures

Most failures when integrating OpenAI-compatible APIs stem from configuration mismatches rather than SDK bugs. Developers should verify the base URL, API key, and exact model ID before writing any application code. Running a minimal cURL request first helps confirm the setup works independently of frameworks like LangChain or Dify. Reviewing request logs for model used, token count, failure reason, and cost can catch silent misrouting issues. The most frequent error is updating only the base URL while leaving an outdated key or model alias in place, often triggering 401 errors or unexpected charges.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Backpropagation Is Just Dynamic Programming (I Animated It to Prove It)

Everyone learns backpropagation as "apply the chain rule." Almost nobody explains why it's fast — and that "why" is the whole reason deep learning is computationally possible at all. So I animated one full training step to show the part most explanations skip. What you're actually seeing Forward pass: a single signal travels through 3 weights → a prediction → compared to the target = the loss. Backward pass: the error (δ) flows back through the network. δ₃ is computed at the output, then reused to get δ₂, which is reused to get δ₁ — never recalculated from scratch.

0 comments Read more at DEV Community