Shaokang's Blog

In addition to Stage 1 of this app, Stage 2 is a voice interaction app based on locally trained natural language processing model by using Nlp.js, which detects and analyze phrases based on machine learning, and voice recognition by using ibm waston cloud. Due to compatibility, limitation in testing, privacy issue, the published version only contain chat feature using which user is able to add items to their lists. Chat robot will communicate with user in multiple rounds and extract related information to add to the existed lists. This app is built on Expo managed workflow and thus has cross-platform support. The future published version will integrate Tensorflow and using float32array to run locally train and run voice recognition. Dark and light theme is following user’s system default. This app is done solely on me. More details come below:

This is an robot chat app based on locally trained natural language processing model by using Nlp.js. It could answer questions in different situations and is able to run and answer questions based on context totally locally. Due to the limitation of the local dataset, it can only understand a limited number of phrase. It is still a good app to chat with. In each run, you can also choose to save history chat or not and choose to extract entity information or not. So, it is also a good app to see what a phrase will happen in react native version of Nlp.js in expo. Dark and light theme is following user’s system default. This app is done solely by me.

Even though the official repo of Nlp.js has a description about using in React Native. Running directly on Expo might have some problems. Due to some limitation on the web version of @nlpjs/core @nlpjs/lang-en-min @nlpjs/nlp, like no entity extraction and can not customize entity, using node-nlp-rn is a good choice. More details on implementing in below:

This is an app provide a easy to use interface to help user organize their spendings/earning and see a summary and guidance by visiting yearly budgeting tool on capitaltwo’s tools page, which will generate those results based on user’s history spending and provide user guidance about their future spending. User is able to provide an importance level to each future spending to guide system which item should be fulfilled at first. System will also intelligently detect user’s past earning and spending and ensure user’s future spending/earning include those fixed monthly/bi-monthly income/expense. Everything is done locally without risk of leaking privacy information. The stage 1 of the app provide a wonderful, easy to use interface and basic functionality, together with cross platform support, including android, ios. More details is in below. Dark and light theme is following user’s system default. This app is done solely by me.