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placesloth.com – generate placeholder images of sloths

Hacker News

placesloth.com – generate placeholder images of sloths

Hey all, Last week I had to make a couple of test pages and turned to one of my favorite sites to fill it with some imagery: https://placebear.com/ There's a pretty large amount of sites like this, but none seemed to be serving the superior animal: sloths. So I hacked this together quickly over the weekend. It's definitely ugly code and an ugly approach, nothing prevents this from going OOM, it'll probably fall over under a bit of load (or when the single spot instance running it gets replaced), but it was fun to build and fills my needs. Hope this inspires you to expand the placeholder generator landscape, or at least include some sloths on your test pages :)

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Actual performance

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Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: single, code · Missing: mac, agents, macos
44%44% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
38%38% 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
16%16% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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