Robot Updated at:26 Oct 2024 21:09:58 GMT

docs/awesome/awesome-agi-cocosci.md

@@ -204,6 +204,8 @@ Contributions are greatly welcomed! Please refer to [Contribution Guidelines](ht
 
 * [Building Machines That Learn and Think Like People](https://leylaroksancaglar.github.io/Caglar_Hanson_2017.pdf) - ***Behavioral and Brain Sciences***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=8504723689348856287&hl=en&as_sdt=0,5)]. Brenden Lake and Josh Tenenbaum's review on Bayesian modeling.
 
+* [Building machines that learn and think with people](https://www.nature.com/articles/s41562-024-01991-9) - ***Nature Human Behavior***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=4420595706578245444)]. [[Preprint](https://arxiv.org/abs/2408.03943)]. This perspective shows how the science of collaborative cognition can be put to work to engineer systems that really can be called ‘thought partners’, systems built to meet humans' expectations and complement humans' limitations. The authors lay out several modes of collaborative thought in which humans and artificial intelligence thought partners can engage, and they propose desiderata for human-compatible thought partnerships. Drawing on motifs from computational cognitive science, this work motivates an alternative scaling path for the design of thought partners and ecosystems around their use through a Bayesian lens, whereby the constructed partners actively build and reason over models of the human and world.
+
 * [The rational basis of representativeness](http://web.mit.edu/cocosci/archive/Papers/cogsci01_final.pdf) - ***CogSci'01***, 2001. [[All Versions](https://scholar.google.com/scholar?cluster=11464039134248091466&hl=en&as_sdt=0,5)].
 
 * [Testing a Bayesian Measure of Representativeness Using a Large Image Database](https://proceedings.neurips.cc/paper/2011/hash/2c89109d42178de8a367c0228f169bf8-Abstract.html) - ***NeurIPS'11***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=8576570792794301292&hl=en&as_sdt=0,5)].

docs/awesome/awesome-astrophotography.md

@@ -18,6 +18,7 @@ Contributions are welcome. Please read the [contributing guideline](https://gith
 - [Cameras and Lenses](https://ciechanow.ski/cameras-and-lenses/) - Another interactive guide by Bartosz Ciechanowski, this time about cameras and lenses. A **must-read** for anyone interested in telescopes and photography.
 - [DeepSkyStacker vs PixInsight](https://web.archive.org/web/20230408140244/https://www.lightvortexastronomy.com/image-pre-processing-deepskystacker-vs-pixinsight.html) - A detailed comparison of the pre-processing features.
 - [Drift Alignment by Robert Vice (D.A.R.V)](https://www.cloudynights.com/articles/cat/articles/darv-drift-alignment-by-robert-vice-r2760) - Accurate alignment in just a matter of minutes.
+- [Flat Fields and Stray Light in Amateur Telescopes](https://diffractionlimited.com/flat-fields-stray-light-amateur-telescopes/) - A great resource to help one identify and resolve flat-fielding issues.
 - [Guide Scope vs. Off-Axis Guider: Which is Better for Astrophotography?](https://optcorp.com/blogs/deep-sky-imaging/guide-scope-vs-off-axis-guider) - Compares the two guiding options in details.
 - [Guide to Focal Reducers for Astronomy](https://agenaastro.com/articles/focal-reducers-guide.html) - How focal reducers work, what types are there, and how to use them.
 - [Guide to Preprocessing of Raw Data With PixInsight](https://pixinsight.com/forum/index.php?threads/guide-to-preprocessing-of-raw-data-with-pixinsight.11547/) - Mistakes that happen during the pre-processing stages cannot be corrected anymore in post-processing. This guide from Bernd Landmann acquaints fellow astrophotographers with PixInsight's tools needed for recognising and avoiding such mistakes.

docs/awesome/awesome-cosmos.md

@@ -218,7 +218,6 @@ Templates to help you get started with building a Cosmos SDK blockchain.
 ### GUI
 
 * [REStake](https://restake.app) - Auto-compounder app for Cosmos blockchains using Authz ([source](https://github.com/eco-stake/restake)).
-* [Yieldmos](https://yieldmos.com) - Staking and LP rewards auto-compounder using Authz.
 * [Cosmfaucet](https://github.com/scalalang2/cosmfaucet) - Self-hosted faucet service for Cosmos based blockchain.
 * [Cosmos Notifier](https://cosmos-notifier.decrypto.online) - Governance notification tool for Telegram and Discord ([source](https://github.com/shifty11/cosmos-notifier)).
 * [IBC Anywhere](https://ibc.reece.sh/) - IBC token transfers including multi-chain hops.

docs/awesome/awesome-datascience.md

@@ -37,6 +37,7 @@ Data Science is one of the hottest topics on the Computer and Internet farmland
 | [a very short history of #datascience](https://www.forbes.com/sites/gilpress/2013/05/28/a-very-short-history-of-data-science/) | _The story of how data scientists became sexy is mostly the story of the coupling of the mature discipline of statistics with a very young one--computer science.  The term “Data Science” has emerged only recently to specifically designate a new profession that is expected to make sense of the vast stores of big data. But making sense of data has a long history and has been discussed by scientists, statisticians, librarians, computer scientists and others for years. The following timeline traces the evolution of the term “Data Science” and its use, attempts to define it, and related terms._ |
 |[Software Development Resources for Data Scientists](https://www.rstudio.com/blog/software-development-resources-for-data-scientists/)|_Data scientists concentrate on making sense of data through exploratory analysis, statistics, and models. Software developers apply a separate set of knowledge with different tools. Although their focus may seem unrelated, data science teams can benefit from adopting software development best practices. Version control, automated testing, and other dev skills help create reproducible, production-ready code and tools._|
 |[Data Scientist Roadmap](https://www.scaler.com/blog/how-to-become-a-data-scientist/)|_Data science is an excellent career choice in today’s data-driven world where approx 328.77 million terabytes of data are generated daily. And this number is only increasing day by day, which in turn increases the demand for skilled data scientists who can utilize this data to drive business growth._|
+|[Navigating Your Path to Becoming a Data Scientist](https://www.appliedaicourse.com/blog/how-to-become-a-data-scientist/)|_Data science is one of the most in-demand careers today. With businesses increasingly relying on data to make decisions, the need for skilled data scientists has grown rapidly. Whether it’s tech companies, healthcare organizations, or even government institutions, data scientists play a crucial role in turning raw data into valuable insights. But how do you become a data scientist, especially if you’re just starting out? _|
 
 ## Where do I Start?
 **[`^        back to top        ^`](#awesome-data-science)**
@@ -530,6 +531,7 @@ These are some Machine Learning and Data Mining algorithms and models help you t
 | [Apache Airflow](https://github.com/apache/airflow) | Platform to programmatically author, schedule, and monitor workflows |
 | [Prefect](https://github.com/PrefectHQ/prefect) | Workflow management system for modern data stacks |
 | [Kedro](https://github.com/kedro-org/kedro) | Open-source Python framework for creating reproducible, maintainable data science code |
+| [Hamilton](https://github.com/dagworks-inc/hamilton) | Lightweight library to author and manage reliable data transformations |
 | [SHAP](https://github.com/slundberg/shap) | Game theoretic approach to explain the output of any machine learning model |
 | [LIME](https://github.com/marcotcr/lime) | Explaining the predictions of any machine learning classifier |
 | [flyte](https://github.com/flyteorg/flyte) | Workflow automation platform for machine learning |