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WorkBuddy platform shares practical experience on converting photos into cartoon stickers via Skills

11/07/2026 — 11/07, 10:16·1 sources (all)·1 reports (all)

Key takeaways

  • A developer has shared practical experience on the WorkBuddy platform regarding the creation of a photo-to-cartoon sticker Skill. The proces…

Background & analysis

On November 7, 2026, a developer posted on V2EX about building a Skill called "Photo Hand-drawn Stickers" on the WorkBuddy platform. The goal was straightforward: turn the people, clothing, and small props in a photo into cartoon versions, then arrange them into a single journaling sticker sheet.

The developer shared two versions to illustrate the workflow. The first attempt looked acceptable, but the faces and the rabbit came out too realistic to match the intended cartoon feel. After one more round of changes, the developer simplified the details of the eyes, hair, and clothes, producing a second version that he was satisfied with, and folded the requirements he used into the final Skill. Along the way he clarified that both sheets were AI-generated, and the first one was not the source photo. He also stressed that no model was trained; the work was mostly about organizing the drawing requirements and the generation workflow, with the actual images still produced by an image model rather than by fine-tuning. Because of that, details such as faces and props can drift, and identical resemblance to the original photo cannot be guaranteed. The post ends with a WorkBuddy experience link and a request for readers' opinions on which of the two versions looks better.

AI-generated from 1 reports · updated 15 hours ago

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24-hour heatHeat index 64 · peak 100 · 15h ago
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Reports on this story headlines open the original

Yesterday
  1. A developer shared an experience of creating a photo-to-cartoon sticker Skill on WorkBuddy. By streamlining conversion requirements and image-generation workflows to simplify facial and clothing details, it produces stylized sticker sheets using image models.

    V2EXAI score 64

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