Change the lighting of a picture with one click

Hacker News

As we all know, lighting is the soul of photography, especially in portrait scenarios. Often, a lot of effort is spent on setting up the perfect lighting, and if it's not right, a potentially great photo can turn into a wasted shot. To help solve this problem, I've developed an online tool that allows you to easily change the lighting in your images, turning those wasted shots into treasures. Try it out here: https://www.iclight.online/ Tool Features: 1、Text-Guided Lighting Adjustment: Simply describe your ideal lighting scenario, and ICLight will automatically adjust the lighting effects in your photo based on your description, applicable to both color and black-and-white images. 2、Consistent and Lifelike Results: ICLight is capable of simulating various lighting conditions, ensuring that each photo maintains its authenticity while enhancing its visual appeal. 3、User-Friendly Interface: The interface is intuitively designed for photographers of all skill levels, simplifying the complex process of lighting adjustment, allowing users to easily create professional-level lighting effects. This is the first version of the tool, and I invite everyone to try it out. If you have any suggestions or feedback, please feel free to share!

Visit product View on Hacker NewsJul 25, 2024

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting

88%88% 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.

AppSumoStrong fit for a featured deal · Strong signals: intuitive, friendly, interface · Missing: plus, platform, reviews

56%56% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: user, visual · Missing: mac, agents, macos

45%45% predicted probability of success on Product Hunt, 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

42%42% 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: ios, users · Missing: mobile apps, personal, entrepreneurs

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

11%11% 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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