Manus Takes Over: A Viral Sensation in a Day

From overnight fame to a scramble for invitation codes, and then skepticism over its lavish marketing, the whirlwind surrounding Manus is a fascinating case study in communication. The AI industry has long operated in a "hype-driven" news cycle—those in the know have grown desensitized, while outsiders still marvel at each spectacle. Yet, amidst this constant bombardment of breakthroughs, genuine game-changers can slip through the noise. My take on Manus? It's the real deal—a true explosion in the AI Agent space, akin to a "DeepSeek moment" for the industry. That said, there's a caveat, which I'll address at the end.

First, let's look at a demo of Manus in action: I asked it to develop a text-based interactive game where you play as Google's CEO, making key decisions from the company's history. It's both entertaining and a way to learn about Google's culture. In about an hour, Manus delivered a fully functional web-based "Google CEO Simulator." The polish was impressive—click "Start," choose your difficulty, and face pivotal moments in Google's evolution. Your choices impact company resources and shape the game's outcome. In one hour, with one sentence, it built a game—that's the power of an AI Agent.

Unlike traditional conversational AI, which merely provides informational answers, Manus operates a computer to complete tangible tasks: coding, building websites, drafting reports, screening resumes, and more. It autonomously navigates challenges and delivers results—though there are exceptions, which I'll circle back to later. Currently, mainstream AI Agent services are scarce and pricey—ChatGPT Operator requires a $200/month Pro subscription, while Devin, a coding-focused AI engineer product, costs $500/month. Manus, developed by Chinese large-model team Monica, is in a free beta phase, with single-task costs squeezed down to $2—1/10th of OpenAI's rates—while topping global benchmarks, surpassing OpenAI. After snagging an invite code, I burned through Manus' daily compute quota in hours. The excitement was real, and the results were jaw-dropping.

Here are some real-world test cases:

  1. Personal Linktree-Style Page: I asked Manus to create a Linktree-inspired homepage. It broke the task into 8 steps—scouring the web for my info, gathering links and notable works from various platforms, then coding a page based on Linktree's style. Thirty minutes later, it delivered a simple, spot-on result. The interactivity was flawless—a Sharingan-level replication. Want it prettier? Just tweak the prompt for revisions.
  2. Fixing an Atlas Robot Arm: A friend from an engineering group had a minor issue with an Atlas robotic arm at his factory. Repairs via support would cost thousands, so he lazily handed me a description to pass to Manus. A typical conversational AI could handle this with enough back-and-forth—feed it docs, extract answers step-by-step—but Manus didn't need that. It fetched the manual from Atlas' site, analyzed it, pinpointed the fix, and wrote code. I sent it to my friend; it had minor flaws but was usable after tweaks, saving a service call.
  3. Minimalist National Chronicle: A Weibo reader suggested Manus create a concise history of a country, with added requests for comic-style tables and web design. The final product's color scheme was rough—AI lacks taste, a point worth hammering home—but by then, Manus' servers crashed, halting refinements. The half-finished work split UK history into 10 eras, paired with SVG illustrations, presented on an HTML page. It's a human-AI collaboration showcase—perfect for lesson plans or previews, with an ultra-low entry barrier.
  4. Match-Three Game with Genshin Icons: I tasked Manus with building a match-three game using Genshin Impact characters as icons. It studied game mechanics, then hit a snag searching for Genshin assets—it requested my intervention because a cloud storage site demanded registration it couldn't bypass. Even mighty AI stumbles at paywalls. To keep it autonomous, I pivoted the prompt to use tech company logos (openly available SVGs). Manus breezed through, delivering a smooth, score-tracking game. Still, finer details—like screen adaptation—needed more guidance, though server crashes paused further polish.

These tests highlight AI Agents' current strengths and limits. Manus isn't just a browser-operator—it has a sandbox environment, testing work internally before delivery. But it's bound by internet data limits; if resources aren't online, it can't generate them. I ran clerical tests too, to contrast Agent capabilities:

The replay feature is a standout. Like a reasoning model exposing its thought chain, Manus logs every task step, shareable and illuminating. The problem-solving process often outshines the result—an intellectual asset, a teacher in its own right.

So, back to my claim: Manus is the AI Agent industry's "DeepSeek moment"—specifically, a DeepSeek-V2 moment. In May 2024, DeepSeek open-sourced V2. It broke out for its dirt-cheap pricing, but its middling ability drew shrugs—price war vibes, not reverence. The hype faded fast. Then V3 and R1 dropped, flipping the large-model cost logic overnight. "At first, no one cared about this disaster—a wildfire, a drought, a species' extinction, a city's collapse—until it hit everyone." (The Wandering Earth) AI progress is continuous, and each signal's strength sets the stage for deeper breakthroughs. No V2, no V3, no R1. My view on Manus holds: it's the trailblazing brand shifting AI Agents from niche to universal.

From use cases, its functionality as an Agent is stellar—task decomposition is masterful. Observing its CoA (Chain of Agency) feels like watching CoT (Chain of Thought)—you "see" the AI weigh options for the best path. It likely embeds vast CoA libraries, much like DeepSeek's reasoning models digest rich CoT datasets before launch, covering mainstream needs (check the official Use Cases).