Awesome List Updates on Oct 10, 2024
6 awesome lists updated today.
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1. Awesome Generative Deep Art
Image Segmentation / Deforum
2. Awesome Marketing
Online Resources and Courses / Digital Asset Management (DAM)
- SEO Roadmap - A Simple roadmap for your SEO journey.
3. Awesome Go
Queues
- hatchet (⭐5.8k) - Distributed, Fault-tolerant task queue.
Error Handling
- errors (⭐73) - Drop-in replacement for builtin Go errors. This is a minimal error handling package with custom error types, user friendly messages, Unwrap & Is. With very easy to use and straightforward helper functions.
Financial
- currency (⭐61) - High performant & accurate currency computation package.
IoT (Internet of Things)
- rulego (⭐1.2k) - RuleGo is a lightweight, high-performance, embedded, orchestrable component-based rule engine for IoT edge.
4. Awesome Ai Tools
Text / Developer tools
- Pagerly - Your Operations Co-pilot on Slack/Teams. It assists and prompts oncall with relevant information to debug issues.
Other / Music
- AI Watermark Remover - Remove watermarks from images and videos.
5. Awesome Rust
Applications / System tools
- jacek-kurlit/pik (⭐297) [pik] - A TUI command line tool that helps to find and kill processes
Libraries / Computation
- faer-rs (⭐2.3k) [faer] - Linear algebra foundation for Rust
6. Awesome Agi Cocosci
Theory of Mind / AI Assisted Research
- Action Understanding as Inverse Planning - Cognition, 2009. [All Versions]. [Appendix]. The original paper on Inverse Planning, a computational implementation of Theory of Mind. Humans are adept at inferring the mental states underlying other agents’ actions, such as goals, beliefs, desires, emotions and other thoughts. This paper proposes a computational framework based on Bayesian inverse planning for modeling human action understanding. The framework represents an intuitive theory of intentional agents’ behavior based on the principle of rationality: the expectation that agents will plan approximately rationally to achieve their goals, given their beliefs about the world. The mental states that caused an agent's behavior are inferred by inverting this model of rational planning using Bayesian inference, integrating the likelihood of the observed actions with the prior over mental states.
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