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Photoreal 3D Models in Photoshop

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Photoreal 3D Models in Photoshop

We released PixelSquid, a site for photoreal 3D assets in Adobe Photoshop, a while ago. Over the past few months, we've been working towards making it an easier workflow for Photoshop users. We have now released the PixelSquid Photoshop Plugin, that allows for rotation of PixelSquid content within Photoshop. Take a look and let us know your thoughts. We've also made the content free for a limited time so everyone can try it out. Here's a video showing the plugin in action: https://www.youtube.com/watch?v=i610ezFbUjA&feature=youtu.be And a link to all the content available: https://www.pixelsquid.com/stock-images

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
80%80% 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: model, user, models · Missing: mac, agents, macos
59%59% predicted probability of success on Product Hunt, 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: video, month, users · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · 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
14%14% 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.

Incorrect prediction on native model

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