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Tessera Designer – Generate beautiful, seamless patterns

Hacker News

Tessera Designer – Generate beautiful, seamless patterns

Hey, A few weeks ago I randomly decided to build a seamless pattern engine for Swift/SwiftUI projects. I called it Tessera (GitHub link). It’s an open-source framework that lets you generate endlessly repeatable, seam-free patterns from pretty much anything you can build in code: shapes, SF Symbols, emojis, text, custom icons, etc. While working on it, I also built a demo app so developers could see how to use the framework. However, that demo turned out to be so much fun to play with that I decided to turn it into a full app. ## Introducing Tessera Designer Tessera Designer is a Mac app that wraps my Tessera engine in a UI that anyone can use. It comes with lots of symbols you can customize, and you can also add text, emojis, or your own images. The app then lays everything out to fill your canvas with a pattern. There are 2 modes available: Tile mode lets you design a single tile (a small square) that can be repeated endlessly without visible seams. Exporting gives you a small image you can use anywhere. Canvas mode lets you create an export of a fixed size (great for wallpapers, postcards, etc.). In this mode, you can pin images/text to specific positions, and the app fills the remaining space with a pattern, so that they "flow" around your pinned elements. You can then export tiles or canvases as PNG or as vector-based PDF (so it scales cleanly, as long as the elements you used are vector-based too). ## Roadmap I’m actively working on new updates. For example, the next version will add a new placement mode for grid-based patterns, and I’m also working on bringing the app to iPadOS and iOS in the near future. ## AI Disclosure The app itself does not use AI to generate anything. It's based on "traditional" algorithms that can generate these patterns. However, I am using OpenAI's Codex CLI to collaboratively build this app. While I let Codex write most of the code, I am still deeply involved in and knowledgable about the code it produces. I am a professional software engineer, and coding is a passion of mine. I still make sure the code is clean, correct and well structured. I spend a lot of time refactoring, organizing and verifying the code. I still do most of the thinking and decisions on "how" I want a feature to be implemented, I just let Codex do the typing part that slows me down. This is the first project I have worked on that is mostly written by AI. It's an experiment. I wanted to see how much faster I could build something I imagined. Traditionally, an app like this would have taken me much much longer to develop. And I do believe the app is nicely built and well structured. I put a lot of care into making the user interface as well as the user experience as best as I can. This is also the first time I've worked on an app for the Mac, so it's a new experience for me as well.

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, user, codex · Missing: agents, macos, agent
97%97% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting
87%87% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: ios · Missing: mobile apps, personal, entrepreneurs
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: interface · Missing: plus, platform, intuitive
29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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