Welcome to the AppCoins News Update, ‘ANU’ for short. This week we’re focusing on Wallet Refactoring — which impresses you with new improvements— and on UX improvements. Read on to find out more!
This may be a shorter ANU than most, but that doesn’t mean that the wallet team isn’t working tirelessly to impress you all with new improvements and features. The last few weeks have been spent laying the foundations and preparing the wallet for an upcoming project that is soon to be announced. We’re still preparing some details for the official launch, but we’re very excited to share it with you soon!
For now, we’re about to launch improvements to our fraud detection rules and some general improvements to our in-app feedback messages. We’re also working on the final details for our complete wallet refactoring that we’ve been teasing. This refactor will not only include all the brand new interfaces we’ve been showing you, but will also allow for some powerful features down the line. A new age for the wallet is coming, so stay tuned!
Following last month’s protocol KPIs performance, on this ANU we have collected the values from August, regarding flows using Apps with AppCoins, IAP with AppCoins and User Interactions with the Wallet, as the table shows below.
As always, you’re invited to follow our work regarding all of the products we’re working on:
- ASF Wallet (Aptoide & Google Play)
- AppCoins Wallet (Aptoide & Google Play)
- ASF SDK
- ASF Unity Plugin
- BDS SDK
- BDS Unity Plugin
- BDS Billing System integration guide
At the time of writing, the current market cap is close to $20,925,987 USD, with $1,068,448 in volume in the last 24 hours across these exchanges: Binance (98,80%), CoinDCX (0,34%) and Huobi (0,86%).
Name: António Neto
Role: Python Backend Developer
Bio: António is responsible for the development of backend features involving an exciting upcoming project that is soon to launch. He’s currently finishing his master’s degree in Computer Science and Business Management, where he’s developing a thesis in deep learning approaches to medical image segmentation and diagnosis. In his time off he enjoys spending time with friends and family, going to the beach, and listening to podcasts.