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App Store Apps > Productivity > Otcomlab
Otcomlab icon

Otcomlab

by 观康 毛 · Productivity · Free
No longer on the App Store

Otcomlab is made and published by 观康 毛. AppBrain is an independent app data site and isn't affiliated with 观康 毛. The app name, icon, screenshots and description belong to 观康 毛 and are shown as published on the App Store.

AppBrain's verdict

Removed
Rating on App Store
3.83
12 ratings
Last updated
November 5, 2025

Otcomlab is no longer on the App Store. It had too few ratings to say what users thought.

Releases

Updates seen on the App Store
Version
1.2.0
Launched
August 2025

No changelog data

User feedback

Ratings and reviews on the App Store

Comments on Otcomlab for iOS

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Technology

Found in the app by AppBrain
Price
Free
App size
55.7 MB
Content rating
Everyone

Technologies used by Otcomlab

About the app and developer

About Otcomlab

Otcomlab was a productivity app developed by 观康 毛. It was removed from the App Store Dec 16, 2025.

User Ratings

Otcomlab was rated 3.83 out of 5 stars, based on 12 ratings.

App Information

Otcomlab was free. The app size was 55.69 MB. The last available version was 1.2.0. The last update was on November 5, 2025.

Technical Requirements

The app had a content rating of Everyone. The app had been available on the App Store since August 2025.

Developer information for 观康 毛

观康 毛

Address:

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Store listing

By 观康 毛, from the App Store
This app is currently not available on the App Store.
Description by 观康 毛 Written by the developer, not by AppBrain. Click to read.
Many real-life outcomes are driven by the interaction of multiple variables, such as "coffee brewing quality is affected by water temperature, coffee powder dosage, and brewing time," and "plant growth is related to light exposure duration and watering frequency." These relationships are often difficult to identify due to a lack of systematic documentation. The app allows users to set target outcomes, record the specific values of relevant variables and the corresponding outcome status, and present trends in variable impact through simple comparative analysis. All data is stored locally, eliminating the need for server support, helping users uncover hidden patterns and optimize decision-making.
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