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Urnerys – self-hosted visual regression testing service

Hacker News

Urnerys – self-hosted visual regression testing service

Urnerys ( https://urnerys.dev ) is a self-hosted service for visual regression testing. I built it out of frustration with existing comparable services (such as Percy, Argos CI, Chromatic etc). I always wanted expand the use of visual regression testing tools in my team, but all the cloud-based services get really expensive when you use more than a few thousand images (which is not a lot even for a small team). So I built Urnerys, with two goals specifically in mind: to provide it under a more affordable pricing model and to allow it to be self-hosted with little effort. To achieve the first goal I offer the software for a one-time payment. You pay once and own it forever, without any restrictions on its use. Inspired by https://once.com . To achieve the second goal, it comes as a single "binary" without any external dependencies. In particular, it does not require any external database (such as Postgres), cloud storage (such as S3) or any other services. Just a single binary that you start and it's ready to use. This setup puts a limit on how large a single instance can scale (in other words, it's not Web Scale™), but even a modest machine can handle many hundreds of projects. Additionally, FAANG companies are not in my target group. There is a demo instance available at https://demo.urnerys.dev . Note that I'll regularly wipe its contents so don't use it for anything serious. I've used a private instance in a few of my projects, mainly to test the workflows and integration with GitHub. It covers the most basic needs that I have on such a service, therefore I feel ready to announce it to a wider audience. Please don't judge the user interface too harshly, it's not been my focus so far. If anybody is interested in trying it out, reach out to me at hi@urnerys.dev. I'll give away a few licenses for free to those brave enough to want to try it for themselves.

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, model, user · Missing: agents, macos, agent
89%89% 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: para · Missing: supports, reddit linkedin, podcasting
85%85% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · 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.
AppSumoMay struggle as an AppSumo deal · Strong signals: host, interface · Missing: plus, platform, intuitive
40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way, para · Missing: mobile apps, ios, personal
35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · 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.

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