The ViEWS project started up in January 2017 with funding from the European Research Council's Advanced Grant scheme. ViEWS develops, tests, and iteratively improves an early-warning system for predicting armed conflict and war based on the statistical analysis of data. The system employs a heterogenous ensemble of machine learning models trained on micro-data on armed conflict collected within Uppsala University as well as on a large set of structural factors and now, on up-to-date corpora of newswire and news agency articles. Monthly updated ViEWS forecasts have been made publicly available since June 2018 at http://views.pcr.uu.se. In addition to being used as framework for a number of research papers, the early-warning system is receiving considerable attention from both the research and practitioner community. From 2023 onwards, a complete refactoring of the system (ViEWS 3.0) will be put into use, with a much more flexible machine learning pipeline. The overall goal of the new system is to allow for much more flexible inputs as well as much more flexible modelling techniques to be used. The goal for 2023 are to 1. improve point estimates (forecasts of forecasted conflict fatalities by location) by including text-as-data models -LSTMs and attention-based neural networks making use of up-to-date newswire corpora as well as simpler topic-model based models) in the pipeline, 2. prototype and develop uncertainty measurements and uncertainty forecast methods and models to move from the point estimates that we have produced towards an uncertainty distribution, 3. to expand coverage to near-global coverage. An open source description of the past version of the system is available here: https://journals.sagepub.com/doi/full/10.1177/0022343320962157. All software developed is also open source, either through a dedicated set of GitHub repositories (https://github.com/prio-data/FCDO_predicting_fatalities is a good entry point for the current prototype and testing version of the system) or through python packages available on PyPi (e.g. viewser).