Last week, a brand‑new Chinese artificial intelligence platform known as a “vibe‑coding” tool captured massive public attention, spreading with astonishing speed across social media and app stores alike. Naturally, curiosity got the better of me, and I decided to put this emerging sensation to the test myself.
This innovative application, called **LingGuang**, debuted on November 18 as a next‑generation AI engine designed to help users build applications simply by typing instructions in everyday language. Within a matter of days—by the following Monday—it had already surpassed two million downloads, a milestone that signals the intensity of public enthusiasm for intuitive AI creation. The platform’s developer, **Ant Group**, a major player in Chinese technology and an affiliate of the renowned **Alibaba Group**, stated that the influx of traffic was so overwhelming that it temporarily caused the app’s signature “flash program” function to crash due to demand.
Determined to uncover what fueled the excitement, I decided to conduct a hands‑on comparison between LingGuang and **OpenAI’s ChatGPT**, perhaps the most recognizable AI tool in the global market. My exploration began the moment I logged in using my Alibaba credentials and was greeted by a visually captivating homepage: an animated mountain range paired with the Chinese maxim meaning “Let the complex be simple.” The atmospheric interface instantly distinguished LingGuang from ChatGPT’s rather minimalist, static background. If ChatGPT’s design suggests the sober utility of present‑day software, LingGuang’s aesthetic feels transported from a futuristic decade, somewhere around 2030.
Among LingGuang’s many features, one in particular immediately commanded my attention—the so‑called **Artificial General Intelligence Camera**, or AGI‑camera. According to Ant Group’s description, this remarkable capability can interpret scenes in real time, offering analytical or editing assistance without requiring users to manually upload photographs. To test it in a professional environment, I pointed my phone at a video clip of a startup founder speaking during a podcast. Within seconds, LingGuang recognized the person visually, identifying his name and the company he established with surprising precision.
Encouraged, I took the app on a more casual outing—to my local supermarket—to evaluate how it would perform in everyday scenarios. Browsing for a post‑workout protein smoothie, I directed my phone toward three different brands displayed on the shelf. In an instant, LingGuang’s AGI camera detected the English labels, extracted key nutritional information such as protein content, flavor options, sweetener status, and suitability for dietary goals, and presented everything neatly within the interface. The level of accuracy was impressive, yet I learned that consistent results required a clear, well‑framed capture of the product.
Eager to push further, I switched to LingGuang’s **voice mode** and asked, in Chinese, which smoothie offered the best overall choice. Instantly, the AI compared data points like nutrition values, brand reputation, and pricing by integrating information from both the captured image and the web. Its structured response then categorized the three options—highlighting the most nutritious one, the most economical, and the best lactose‑free alternative. To replicate the experiment, I turned to ChatGPT. However, because ChatGPT cannot interpret visuals in real time, I had to photograph the shakes, upload the image manually, and prompt the system to analyze it—a process that felt dated once I had experienced LingGuang’s seamless immediacy.
Although ChatGPT’s textual analysis was impressively detailed and matched LingGuang’s depth, the lack of real‑time vision and the absence of dynamic on‑screen cues made the interaction feel less fluid. Another design element reinforced this contrast: when LingGuang captures an image, it generates **interactive prompt bubbles** directly on the screen. These tappable suggestions gently guide users toward possible next actions—such as asking deeper questions or refining an analysis. ChatGPT offers similar suggestions, but they are static text prompts positioned beneath its chat box, requiring manual typing rather than intuitive tapping. The difference is striking—LingGuang feels like a responsive, augmented reality companion, whereas ChatGPT behaves like traditional text‑based dialogue software.
That said, LingGuang has its shortcomings. The most noticeable is the lack of data persistence. None of the interactions or visual captures from an AGI session are saved, meaning users cannot revisit past outputs or screenshots later. For anyone accustomed to ChatGPT’s conversation history—where every image and response is automatically archived for future reference—this limitation feels significant.
LingGuang’s strengths reappear, however, in its creative suite. A feature unavailable in ChatGPT allows users to **generate and edit videos or images on the spot**, using content directly viewed through its AGI camera. After snapping a picture—say, of a collectible toy like my Labubu—I could instantly request visual alterations. When I asked LingGuang to make the toy smile and perform a small dance, the system produced, within twenty seconds, a short animated clip, complete with a cheerful musical track and synchronized movements that mirrored the gestures of my hand in the frame. The outcome felt organic, lively, and playful.
By contrast, ChatGPT lacks built‑in animation capability. To achieve similar results, I had to turn to **Sora**, another AI tool from OpenAI’s ecosystem, upload a still photo of Hong Kong’s harbor, and instruct it to “bring it to life.” Sora’s recreation was impressive—visually striking and slightly dramatic in tone—but LingGuang’s version stood apart. Its rendering of the same harbor scene exhibited softer textures, calmer water movement, and a more realistic cinematic atmosphere, as though the viewer were gently rocking on an actual vessel. Stylistically, the preference is subjective, but for user experience, LingGuang’s integrated workflow—capture, edit, and animate within one continuous session—offers unmatched immediacy.
Yet the app doesn’t stop at creative media. Its marquee capability, the **flash app builder**, as mentioned earlier, had briefly crashed during launch due to excessive traffic. This feature promises to construct fully functional mini‑programs in roughly half a minute. On opening the module, LingGuang presented a list of suggested app ideas. One playful option was a “meal decision generator,” essentially a digital food lottery that proposes dishes at random. Given how often my friends and I waste time arguing over dinner choices, I decided to try it. After a brief loading animation—closer to a minute than the advertised thirty seconds—the system unveiled a fully operational mini‑application.
The automated bot’s instructions were both concise and comprehensive: it prompted me to enter dish names, note their culinary origins, and include short descriptions explaining each recommendation. To enrich the experience, the flash app embedded food emojis and sound effects reminiscent of a suspenseful drumroll and reveal moment. The entire setup required nothing more than selecting prompts and watching the system assemble the app autonomously—it genuinely felt magical.
When I requested a version tailored for Singaporean cuisine, LingGuang regenerated the entire application interface within a minute, replacing the original food examples with quintessential local favorites such as **Katong laksa** and **chili crab**—choices that perfectly captured the character of regional dining. The precision of localization demonstrated the tool’s impressive contextual understanding.
For comparison, I tasked ChatGPT with producing a similar program that could help me decide what to eat daily. ChatGPT responded by generating fully documented code, explaining how to deploy it, and even offering guidelines for customization. While this didn’t yield an instantly usable application—something LingGuang does effortlessly—it provided a tangible foundation for developers seeking deeper control. In essence, LingGuang excels at rapidly generating polished, functional tools for everyday or personal scenarios, while ChatGPT remains more adept for complex, structured development requiring transparent coding.
Overall, my hands‑on exploration underscored two complementary visions of AI creativity. LingGuang embodies speed, visual intelligence, and sensory interactivity, collapsing multiple creation stages into one elegant process. ChatGPT, on the other hand, represents depth, adaptability, and systematic reasoning. Yet it’s clear that LingGuang’s fusion of **real‑time perception, generative storytelling, and instant app development** hints at the next phase in human‑AI collaboration—a future in which coding, creating, and innovating could all converge within a single intuitive lens.
Sourse: https://www.businessinsider.com/lingguang-china-vibe-coding-ant-group-agi-camera-chatgpt-sora-2025-11
