Awesome List Updates on Sep 16, 2024

9 awesome lists updated today.

1. Awesome Osint

Blackbird (⭐3.6k) - Search for accounts associated with a given email across various platforms.

SherlockEye - Search for publicly available data linked to an email address across multiple sources on the internet.

Middleware - Tool designed to help engineering leaders measure and analyze the effectiveness of their teams using the DORA metrics. (Source Code (⭐1.3k)) Apache-2.0 Docker/Python/Nodejs

Redlib (⭐2.2k) ⚠ - An alternative private front-end to Reddit, with its origins in Libreddit. AGPL-3.0 Rust

Precis (⭐64) - Extensibility-oriented RSS reader that can use LLMs (including local LLMs) to summarize RSS entries with built-in notification support. MIT Python/Docker

HeyForm - Form builder that allows anyone to create engaging conversational forms for surveys, questionnaires, quizzes, and polls. (Source Code (⭐8.2k)) AGPL-3.0 Docker

DotnetPackaging (⭐73) - Tool to distribute .NET applications in the AppImage format.

TailorTask - Automate any boring task, without code and without having to learn a new tool

Infinity AI: a video foundation model that allows you to craft characters and animate them

westchange (⭐0) - Allows you to quickly change between directories. Requires fzf (⭐72k).

National Geologic Map Database - This is a nation wide geologic map database developed by USGS's National Geologic Map Database (NGMDB) that comes with thousands of georeferenced geologic maps searchable through map interaction. Maps are visible at the zoom level that they were originally mapped.

3D Street - 3D Street is an open-source 3D street design and virtual reality application that allows users to directly import their street designs from Streetmix into an interactive 3D web scene. Apart from the community version, it also provides a Pro version with integration with Google Earth and further customization.

angular-google-charts (⭐277) - A wrapper for the Google Charts library written in Angular.

Kinematic mental simulations in abduction and deduction - Proceedings of the National Academy of Sciences, 2013. [All Versions]. This paper presents a theory, and its computer implementation, of how mental simulations underlie the abductions of informal algorithms and deductions from these algorithms. Three experiments tested the theory’s predictions, using an environment of a single railway track and a siding. The results corroborated the use of a kinematic mental model in creating and testing informal algorithms and showed that individuals differ reliably in the ability to carry out these tasks.