Te

Test your website on 180+ device viewports (with multi-device mode)

Hacker News

Test your website on 180+ device viewports (with multi-device mode)

Hey HN! We do a lot of frontend testing, and one thing we find frustrating is the limited tooling available to observe how a URL renders on different viewports/devices simultaneously. For example, we can use Chrome Inspector's "Device" previews, but we run into the following: - Chrome doesn't keep those devices/viewports up to date, and adding new ones is pretty annoying (we've open sourced our collection of 180+ viewports/dimensions under MIT). - You can’t see multiple viewports at once (and it’s cumbersome switching back and forth) - When you notice a problem, there's no simple way to deep-link someone on your team to take a look at that viewport (likely have to open a Jira/Linear with the specifics) - You can’t favourite viewports (for tracking the viewports specific to your project/client) We’ve built a product with the goal of addressing these limitations (among others), and would love to hear your feedback. Finally, feel free to use the open sourced data for your own projects :)

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, open · Missing: mac, agents, macos
90%90% 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 · Missing: supports, reddit linkedin, podcasting
63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: open source, io · Missing: https docs, excited, just released
57%57% 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: reviews · Missing: plus, platform, intuitive
33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
29%29% 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.

Correct prediction on native model

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