Awesome List Updates on Jan 20, 2025

8 awesome lists updated today.

1. Awesome Angular

extreme-angular (⭐150) - A starter template with pre-configured dev tools that enforce best practices for creating clean, maintainable, and accessible web apps.

ngx-van (⭐1) - Tiny replacement for HTML nav element with mobile side nav menu built-in. Good old nav element on desktop, sliding side nav on mobile. No duplication.

Material Maker (⭐4.2k) - Create PBR materials procedurally (similar to Substance Designer).

archives (⭐265) - a cross-platform, multi-format Go library for working with archives and compression formats with a unified API and as virtual file systems compatible with io/fs.

Tricount - App size is massive (~200MB) and contains many trackers from Facebook, Google and Huawei.

Spliit (⭐1.8k) - Share Expenses with Friends & Family. No ads. No account. Open Source. Forever Free.

PeerSplit (⭐27) - Website - Track and split group expenses. 100% free, 100% private.

SplitPro (⭐698) - Website - Split Expenses with your friends for free. An open source alternative to SplitWise.

IHateMoney - Manage your shared expenses, easily. Lacks unequal splitting.
- MoneyBuster - Android client for Nextcloud Cospend and IHateMoney servers.

Nextcloud Cospend - A group/shared budget manager inspired by the great IHateMoney.
- MoneyBuster - Android client for Nextcloud Cospend and IHateMoney servers.

Debitum (⭐93) - With Debitum you can track all kinds of IOUs, be it money or lent items.

Ghostfolio (⭐6k) - open source wealth management software built with web technology.

PortfolioPerformance - An open source tool to calculate the overall performance of an investment portfolio-

Inngest (⭐3.2k) - Durable functions for reliable background logic, from background jobs to complex workflows.

From information scaling of natural images to regimes of statistical models - Quarterly of Applied Mathematics, 2008. [All Versions]. [Preprint]. One fundamental property of natural image data that distinguishes vision from other sensory tasks such as speech recognition is that scale plays a profound role in image formation and interpretation. Specifically, visual objects can appear at a wide range of scales in the images due to the change of viewing distance as well as camera resolution. The same objects appearing at different scales produce different image data with different statistical properties. In particular, this work shows that the entropy rate of the image data changes over scale. Moreover, the inferential uncertainty changes over scale too. The authors call these changes information scaling. They then examine both empirically and theoretically two prominent and yet largely isolated classes of image models, namely, wavelet sparse coding models and Markov random field models. The results indicate that the two classes of models are appropriate for two different entropy regimes: sparse coding targets low entropy regimes, whereas Markov random fields are appropriate for high entropy regimes. Because information scaling connects different entropy regimes, both sparse coding and Markov random fields are necessary for representing natural image data, and information scaling triggers transitions between these two regimes.