SakeTami
Cube Cobra
Cube Cobra

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Update 1.2.0

Since the last update four months ago, we've been working on a wide range of features and improvements for CubeCobra. In the last update I talked about opening up avenues for new projects to benefit from CubeCobra with new integration points, and today I'm really excited to announce two new integrations, in addition to a couple new large features.

The first integration is with a new project, still in open beta, called Snapdrafter. Snapdrafter is currently only available as an android app and if you'd like to see it an iOS app, I'd encourage you to support the app here. Snapdrafter is an app that lets you take photos of your drafted deck, and save them to your phone as a text decklist file. From there, you can click the CubeCobra icon to import scanned decks into CubeCobra. I believe that creating an easy way to collect data for in-person drafts will make it feasible to do data analysis for in-person drafts of a cube. These imported decks won't just be a playtest draft object you're familiar with, we have added a new cube section called "Records" to track actual drafts of your cubes. This section is meant to be a data store, a place where data can be input or imported, in order to perform analysis on the performance of cards or strategies. It's still very basic, so if there is something you'd like to see in this section, please let us know.

The second integration is with a project you may have heard of before, Commander Spellbook. Commander Spellbook is the most comprehensive database for combos within magic. We've added a new analysis tab to cubes that allows you to see the combos that are available in your cube, with detailed information about each combo. If you feel a combo is missing, you can submit new combos here.

If you regularly follow development of the site, you may know that I retrain the ML system roughly once a year. I was unable to retrain this year due to our dataset outscaling the training infrastructure we had set up. This has been a pain point for users as newly printed cards that were outside of the training data were effectively invisible to the model at the time. We implemented some mitigation measures, so cards outside the training dataset are matched to similar cards. This has been an effective stopgap for now, and will help keep the system working well in between re-trainings. Fortunately we have recently had help from a contributor who refactored our training scripts, to be able to handle the scale of our dataset for many years going forward. I'm currently working on rebuilding this dataset and retraining the model, and I hope to have that done in the next month or two. There have been some new features that interact with the ML system, such as being able to view draftbot scores during playtest drafts, and also being able to view these scores in the pick-by-pick breakdown.

New Features

Bug Fixes

Technical Changes

Comments

This is awesome! Love to see the integration with other apps

Christopher Ryder


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