Despite being limited to just 128 KB of ROM, the 1987 NES game Contra achieved a surprisingly long storyline by exploiting tile‑based graphics, tightly packed audio data generated by the Ricoh 2A03 chip, and minimalist code that leveraged the console’s built‑in hardware functions.
ControlAudio, a progressive diffusion framework introduced by Tsinghua researchers, unifies text, timing, and phoneme modeling to enable precise control over when sounds occur and what is spoken, achieving superior alignment and intelligibility while preserving high‑fidelity audio generation.
LongCat-Next is a 68.5‑billion‑parameter discrete‑native autoregressive multimodal model that tokenizes images, audio and text, challenges the belief that visual tokenization loses detail, matches specialized models on fine‑grained tasks, and demonstrates that joint understanding‑generation training can even improve generation quality.
This article explores why 1980s games like Contra could deliver rich graphics, music, and gameplay within just 128KB by using tile-based graphics, specialized audio chips, and ultra‑compact code, contrasting those techniques with modern developers' expectations of massive storage.
This tutorial introduces Spring AI and Spring Cloud Alibaba AI, explains their core features, shows how to set up a Maven project with the required dependencies, and provides step‑by‑step code examples for invoking text, image, and audio generation models using Spring Boot.
This article explains how 1980s game developers squeezed full‑screen scrolling, rich music, and extensive gameplay into just 128 KB by using tile‑based graphics, specialized audio chips, and ultra‑compact code, revealing the technical tricks behind classic titles like Contra.
The article explains why early 1980s games such as Contra could deliver rich graphics, music, and scrolling effects within just 128 KB of ROM by using tile‑based graphics, minimal audio data, efficient code, and hardware‑specific tricks, and it reflects on how modern developers perceive data size differently.
This article explains how Yuedu's TTS synthesis platform tackles the booming audiobook market by using AI‑driven text preprocessing, role graph construction, content structuring, emotion and effect recognition, and a streamlined post‑processing workflow to efficiently generate multi‑character, emotionally rich audio books at scale.
