Arthur Zargaryan’s team published on X on July 19 a company that doesn’t exist at all—Sliceline.ai. The post included a polished website generated by the Hyperagent tool, a complete product showcase video, and a financing statement from a fictional venture capital firm, Atomik.vc—all of which were fabricated. On the day of the posting, the video surpassed 700,000 views.
Sliceline.ai fake company’s execution structure
Arthur Zargaryan’s team positioned Sliceline.ai as a tribute to the 2026 version of HBO’s TV series “Silicon Valley”—a fake startup that uses the AI model PIE-1 to link company communications tools, tracks the team’s “Hunger Adjustment Morale Score (HAMS),” and automatically delivers pizza repairs to set the mood.
On the technical execution side, the team used the Hyperagent tool to generate two websites at once (sliceline.ai and atomik.vc). After connecting them to GitHub, they deployed them to the production environment via Vercel—taking only a very short time from concept to launch.
The X copy used ultra-short, sharp messaging: “We raised $6 million to turn unhappy employees into happy ones. One pizza at a time. Led by Atomik.vc.” The team pointed out that in posts with a million Social Media Exposure, the actual video view counts typically land between 10,000 and 100,000, and that the copy quality determines where within that 10x range the video lands.
Click-through rate and average viewing duration: the MrBeast framework and retention design quoted by Arthur Zargaryan
In its operating methodology, Arthur Zargaryan cited MrBeast’s view that virality boils down to two algorithmic problems: A) did people click (click-through rate); B) did they watch (average viewing duration, AVD). He said the expectations set by the copy must be fulfilled by the video—not just by driving misleading clicks.
On the retention design side, the Sliceline.ai video editing uses a cut rhythm of switching shots every 3.83 seconds, because X’s content environment has become highly TikTok-like, and static shots are what makes viewers swipe away. The video’s energy must be maintained throughout after the opening hook—not just at the beginning.
X algorithm viral signals and personal network priority strategy
（Source: Launch Video）
In his article, Arthur Zargaryan said that the single biggest viral signal in the X algorithm (open source) is comments and replies—especially replies from the original poster. He said the algorithm’s design goal is to drive conversations rather than passive browsing. He added that in the first few hours after publication, the team ensured that every incoming comment was replied to immediately.
For the personal network strategy, the team built a set of internal tools to crawl and map personal networks, precisely listing who should be contacted for each post. They split it into two sources: “personal network interactions” and “influencer interactions,” and used the virality.studio tool to track minute by minute how each contributed separately to performance metrics. Zargaryan’s conclusion was: the influence of a personal network can’t be replicated (because not everyone can access the same influence circles), while influencers can only light a match and can’t fabricate real interaction heat.
In addition, the team repeatedly quoted and reposted its original posts during the event, pairing them with new content (such as videos assembling pizza boxes) to drive additional engagement rather than stacking all resources into a single tweet.
Frequently asked questions
What was the final view count of the Sliceline.ai experiment?
According to Arthur Zargaryan’s article, the Sliceline.ai video had surpassed 700,000 views by the end of the day it was posted, and it was still increasing. On the same day, OpenAI, DoorDash, and Pocket also published content on X, creating a competitive environment.
What viral signal did Arthur Zargaryan say is the biggest in the X algorithm?
Based on his article, Arthur Zargaryan cited open-source information about the X algorithm, saying the biggest single viral signal is comments and replies—especially replies from the original poster. He noted that the algorithm is designed to drive conversations, rather than simply rewarding reposts or having influencers amplify volume.
How did the team use virality.studio to track post performance?
According to the article, virality.studio is a tool built by Arthur Zargaryan’s team that tracks post performance minute by minute and separates the impact of personal network interactions from the impact of influencer interactions. This allows the team to clearly understand what kinds of interactions are driving the metrics.
