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How a Side Project Turned into a Job at X - The Legend Yaccine thumbnail

How a Side Project Turned into a Job at X - The Legend Yaccine

The PrimeTime·
6 min read

Based on The PrimeTime's video on YouTube. If you like this content, support the original creators by watching, liking and subscribing to their content.

TL;DR

Yap scene built Ding board because existing image tools were too high-effort for quick meme creation in a browser workflow.

Briefing

A side project built to avoid “clicking through” Photoshop turned into a profitable meme tool—and ultimately a job at X—because posting in public created a feedback flywheel and because the product was designed around user friction. Yassine (Yap scene) started posting on X about machine-learning papers and what he was building, then shifted into practical, personal problems: an automatic rep counter for his home gym (a failure), followed by Ding board, an in-browser image editor for making memes quickly without heavyweight software.

Ding board’s growth wasn’t framed as a grand startup strategy. It emerged from “scratching your own itch”: the existing tools were too high-effort, and the workflow he wanted—open a site, drag pixels, leave—already matched how people use X. He also built in a user-respect principle that doubled as a marketing mechanism: instead of forcing a watermark, the software’s “ding board” branding appears subtly inside memes, and users who like the tool effectively promote it by including the mark. That user-first stance, he said, made the product feel worth paying for.

The path from side project to X wasn’t a straight line. He tried to sell Ding board to Elon Musk after meeting him, but the deal didn’t close. Still, the user base included people who worked at X, and conversations around acquisition led to a job offer. The move mattered to him not only for scale and engineering depth, but for reliability as a user: infrastructure that keeps X up is part of the product experience for everyone who streams, shares, and interacts on the platform.

A key “moment” of seriousness came from realizing profitability was easier than Silicon Valley fundraising culture suggested. He tracked revenue in McDoubles—his own tongue-in-cheek unit of economic stability—and described repeatedly checking Stripe until the product’s cashflow felt like an “infinite McDouble machine.” Even then, he insisted Ding board never fully left side-project territory; it simply did its job.

Beyond the career arc, the conversation turned into a set of operating principles for builders. Posting publicly increases “luck surface” by making work visible and attracting motivated users and collaborators. Faster iteration cycles beat long customer feedback loops: when experiments take days instead of weeks, creators learn what fails sooner. That same logic is now driving his hardware ambitions—reducing hardware cycle time with rapid prototyping (3D printing, custom CAD tooling) and stripping away unnecessary complexity like installers and heavy UIs.

He also argued that friction—latency, sluggish interfaces, annoying workflows—directly affects outcomes, including conversion rates. The practical takeaway: measure, remove friction, and treat small delays as product-critical. Finally, he framed programming as a way to stay employable and leverage automation in any job, while using AI as an accelerator for code generation and accessibility rather than a replacement for human creativity. His overall message: build for the problems you personally refuse to tolerate, publish to compound feedback, and keep cycle time low enough that learning stays fun.

Cornell Notes

Yassine (Yap scene) describes how Ding board—an in-browser meme image editor built to replace the high-effort workflow of tools like Photoshop—grew from a personal itch into a profitable product and then into an engineering role at X. Regular public posting on X created a feedback flywheel: more visibility led to more motivation to build, and users (including people at X) helped connect the project to a job opportunity. He credits user-respect design choices (subtle branding inside memes rather than forced watermarks) and emphasizes that profitability was surprisingly achievable without fundraising. The broader lessons extend to iteration speed, reducing friction and latency, and using AI to accelerate experimentation and accessibility rather than eliminating human work.

How did “building in public” change the trajectory of Yap scene’s career?

Posting on X started as a way to share what he was learning from machine-learning papers and to document what he was building. Over time, that created a flywheel: the more he posted, the more motivated he felt to keep working on hobby projects in his free time. He eventually built Ding board, and the public trail of work attracted users—including people connected to X—who later expressed interest in acquiring the product. That visibility helped turn a side project into a job offer at X.

Why did Ding board succeed as a meme tool without copying the complexity of traditional image editors?

The product was designed around a specific workflow: open a browser site and make memes quickly without the overhead of installing or learning heavyweight software. Yap scene contrasted this with Photoshop-style friction—multiple clicks, setup steps, and learning costs. Ding board’s “scratching your own itch” approach meant it matched how people already behave on X: a post box is right in front of you, you do the thing, and you leave. He also said he respected users by avoiding forced watermarks; instead, the “ding board” mark is hidden subtly inside memes, turning branding into something users willingly include.

What was the “moment” when Ding board felt more serious than a casual side project?

He described being surprised by how easy it was to reach profitability. After spending time around Silicon Valley’s fundraising culture, he expected profitability to be harder. Instead, he repeatedly refreshed Stripe’s dashboard until revenue felt stable enough to support ongoing consumption—he joked about measuring it in McDoubles. The seriousness came from realizing the product’s cashflow could sustain his lifestyle without needing a big pivot or fundraising.

What principles did the conversation highlight for turning ideas into useful products?

A recurring theme was reducing cycle time and friction. Iteration should be fast enough to test ideas locally (days instead of weeks) so failures are discovered sooner. He also argued that friction matters in measurable ways: even small latency increases can reduce conversion because humans are sensitive to delays. For builders, that means removing unnecessary UI, installers, and steps—and measuring the impact of changes like animation removal on conversion.

How does the same mindset apply to hardware, where iteration is typically slower?

Yap scene said hardware’s long cycle time is a major obstacle, so he’s trying to bring software-like iteration speed to electronics. He mentioned using a 3D printer and writing custom CAD software to move quickly from design idea to prototype, then to trained models (including reinforcement learning) and toward product. The goal is to shorten the path from “idea” to “something real enough to test,” and to avoid extra complexity when it doesn’t add value.

What’s the stance on AI and programming—replacement or augmentation?

He leaned toward augmentation. He argued that programming skills remain valuable even if AI writes code, because humans still need to fix problems and understand systems. He also framed AI as a tool for accessibility—e.g., reading for dyslexic users, using audio and computer vision, and enabling people who can’t easily “Google” to interact with devices through speech and vision. The practical message: use AI to accelerate experimentation and make tools more usable, not to remove human agency.

Review Questions

  1. What specific design and workflow choices made Ding board feel easier than traditional image editors for meme creation?
  2. How does reducing cycle time change what a builder learns, compared with waiting weeks for customer feedback?
  3. In what ways did Yap scene connect friction and latency to business outcomes like conversion?

Key Points

  1. 1

    Yap scene built Ding board because existing image tools were too high-effort for quick meme creation in a browser workflow.

  2. 2

    Regular public posting on X created a feedback flywheel that increased motivation and visibility, eventually leading to X-related user conversations.

  3. 3

    Subtle “ding board” branding inside memes was framed as user-respect rather than forced watermarking, and it doubled as organic promotion.

  4. 4

    Profitability arrived sooner than expected, and he described tracking revenue in McDoubles to illustrate how stable cashflow felt.

  5. 5

    A central product principle was reducing friction and latency, since small delays can measurably hurt conversion.

  6. 6

    Faster iteration cycles—local experiments that take days, not weeks—help builders discard weak ideas sooner and converge on useful ones.

  7. 7

    For hardware, he’s trying to import software-style speed by using rapid prototyping and custom tooling to shorten the idea-to-prototype loop.

Highlights

Ding board’s growth came from “scratching your own itch”: a browser-first meme editor designed to avoid the setup and learning overhead of tools like Photoshop.
He linked posting publicly to a “luck surface” effect—visibility compounds opportunities, including acquisition talks and job offers.
The profitability surprise wasn’t a fundraising breakthrough; it was repeated Stripe checks until the product’s cashflow felt dependable.
A recurring engineering thesis: friction and latency aren’t cosmetic—they can directly reduce conversion and user engagement.
Hardware ambitions aim to shrink cycle time using rapid prototyping and hackable custom CAD tooling.

Topics

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