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I built the most over-engineered Deal With It emoji generator

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I built the most over-engineered Deal With It emoji generator

Hi, all! Author here. What started as a small tool I built for a job interview, became "The Most Over-engineered Deal With It Emoji Generator": - All operations done fully client-side - no backend, no private data leaves your browser. - Uses machine learning models (MediaPipe Face Detector task) to automatically scale and position glasses on the detected faces. - Extensive customization options for glasses: - Placement of glasses anywhere on the input image (including slightly going outside it). - Change the size of glasses. - No limit on the number of glasses. - Flip the glasses vertically or horizontally. - Customize the direction from which the glasses appear on the image. - Different types of glasses. - GIF output options: - Looping mode. - Number of frames. - Frame delay. - Separate delay setting for last frame. - Output size. - Celebration confetti - Easter eggs. I've been working remotely for the last >9 years. When using non-verbal communication, it's important that your tone and intent comes across accurately.. Custom emojis became for me part of expressing yourself, creating bonds and camaraderie. I've originally created an MVP of this tool while applying for a exciting new job opportunity. As a showcase of my passion for programming, building teams and creating delightful user experiences. Unfortunately, they were not impressed and ultimately did not offer me the job :( But I wanted to polish it and release it for everyone to use for free, so that you can too "Deal With It"! I have more ideas for even more features (check GitHub[1]), but wanted to launch it and see what's the feedback and ideas from the community! And if you're looking for a Fullstack Developer with >14 years of experience, with passion for great customer experience (remote work or locally in Iceland), let's chat! [1] - https://github.com/klimeryk/dealwithit

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84%84% 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: mac, model, user · Missing: agents, macos, agent
51%51% 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, pipe, io · Missing: https docs, excited, just released
47%47% 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 · Missing: plus, platform, intuitive
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal
30%30% 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
13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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