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Snap Shot – Make Screenshots Look Polished in Seconds

Hacker News

Snap Shot – Make Screenshots Look Polished in Seconds

I built a small browser-based tool called Snap Shot because I often needed to make screenshots look decent for slides, product pages, or social posts, and I didn’t want to open a full design tool just to add padding or a background. With Snap Shot, you can: Drop in a screenshot and quickly add padding, change aspect ratios, or style it with backgrounds Apply simple effects like shadows or isometric/3D perspective Export instantly without watermarks Everything runs locally in the browser (no uploads) I priced it as a one-time $9 deal since this feels like an occasional-use tool rather than something worth a subscription. Would love feedback from the HN community — on both the product and whether this kind of tool feels useful outside of my own workflow. https://snap-shot.getindielaunch.com/

Visit productView on Hacker NewsAug 27, 2025
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: open · Missing: mac, agents, macos
79%79% 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: ios · Missing: supports, reddit linkedin, podcasting
75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: occasional · Missing: plus, platform, intuitive
46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: ios · Missing: mobile apps, personal, entrepreneurs
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
30%30% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue
20%20% 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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