KnitKnit

KnitKnit is a cozy visual workspace that helps knitters collect sweater inspiration, manage yarn stash, and connect ideas to the yarn they already own.

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Knitters often collect ideas from many places like Xiaohongshu, Ravelry, Pinterest, and Instagram, while their yarn stash lives somewhere else: in boxes, notes, spreadsheets, or memory. We wanted to create a softer, more visual tool that treats knitting inspiration and yarn inventory as one connected creative universe.

KnitKnit helps knitters collect inspiration, save reference images from URLs, manage their yarn stash, and link yarns to project ideas. The homepage shows yarn and inspiration as interactive bubbles, making it easy to see which yarns are connected to which ideas. Users can add yarn photos, extract a main color automatically, remove image backgrounds, tag yarns, and browse inspiration cards with related yarns and search suggestions.

We built KnitKnit as a web app using Next.js, React, TypeScript, Tailwind CSS, and Zustand. Data is stored locally in the browser, with structured project data in localStorage and larger media assets stored in IndexedDB to avoid storage limits. We implemented URL scraping for inspiration cards, image processing for yarn photos, background removal, color extraction, responsive mobile layouts, bilingual UI support, and a felt-inspired visual design using custom texture assets.

One major challenge was reliably extracting cover images from different platforms, especially sites with dynamic pages or anti-scraping behavior. Another challenge was handling user-uploaded images without exceeding browser storage limits. We also spent a lot of time refining the mobile experience, modal layouts, texture backgrounds, and the interaction design of the bubble-based homepage so the app felt useful rather than just decorative.

We are proud of turning a practical stash-management tool into something that feels warm, tactile, and personal. The interactive bubble homepage gives users a playful way to understand relationships between yarn and inspiration. We are also proud of the image workflow: users can paste or upload yarn photos, automatically remove backgrounds, and extract colors with minimal effort.

We learned a lot about browser-side storage limits, image handling, responsive UI design, and the complexity of scraping metadata from modern websites. We also learned that craft tools should not feel like generic productivity software. For knitters, visual texture, emotional tone, and creative flow matter as much as the underlying data model.

Next, we want to add smarter project planning features, better pattern support, cloud sync, richer yarn recommendations, and more robust platform integrations. We also want to improve the visual graph so users can plan future knitting projects from their existing stash, inspiration boards, and saved patterns.

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