Things To Have – free, privacy caring, mighty wishlist app

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Things To Have is a wishlist app. I started working on it in 2022 as a pet project for my friends, then turned it into a serious app in 2024. I built it because I disliked other such apps – they were either too ephemeral, limited to a specific use case, or felt like a TV shop. I wanted to build an app that would be calm and would cater to thoughtful buyers, collectors, and enthusiasts. I also didn't forget about birthdays, so reservations and other birthday QoL are present. As it always was a hobby/passion project, I wasn't bothered by "time to market" and overengineered a lot of things. For example, it ships transparency-aware blurhash for each image, has many unadvertised Drag'n'Drop interactions like MacOS has, 1001 ways to upload files into a form, etc. It is also powered by a quirky stack: it is running on Cloudflare Workers and D1, and the code is written using Hono ( https://hono.dev/ ) and Vue, tied together by a custom Inertia ( https://github.com/brachkow/hono-inertia ) adapter. I also use better-result ( https://better-result.dev/ ) to bring sane errors to JavaScript. All this results in a scalable, cheap, and extremely easy way to write full-stack apps, which feels like old-fashioned MVC. I believe my app stands out from both established apps and beige vibecoded competitors and is worth your try. And if not, I hope you find the listed tech fun at least...

Visit product View on Hacker NewsSep 17, 2026

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2points

Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting

90%90% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, apps · Missing: agents, agent, cursor

73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

62%62% 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: apps, way · Missing: mobile apps, ios, personal

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

13%13% 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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