FieldDay – create custom vision AI apps with your phone

Hacker News

Hey all! I'm Nick, co-founder at FieldDay. We are launching our app publicly today and I'm excited to show it to the HN community. With the new wave of AR/camera devices and latest AI breakthroughs, we think now is the time to build a camera-first app platform. We are starting by shipping a tool that allows you to train custom models from your phone. You can collect and manage data from your phone, train a model, and finally test the model and analyse your model. This quick iteration cycle lets you quickly refine your models to make them robust. Once you're happy you can export in various formats and we are also working on hooking it up to various actions. We see ourselves as a spiritual successor to tools like Lobe.ai and Teachable Machine but we believe letting people collect, train and test from their phones will open up a lot of use cases that are hard to capture on your desktop computer or by looking for datasets online. And of course, we are excited to offer much more than classifiers in the future. Let me know if you have a cool use-case or what we could do to make this useful for you. Also feel free to hit me up at nick@field.day if you encounter any bugs or have a question. Thanks!

Visit product View on Hacker NewsJul 7, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, model, apps · Missing: agents, macos, agent

91%91% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

88%88% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: excited, io · Missing: https docs, just released, exist

61%61% 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: apps · Missing: mobile apps, ios, personal

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews

32%32% 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

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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