MooAI Insight
Based on the search results, it appears that "Vector" refers to Cross-browser Vector Graphics.
"Art" is a library for creating vector graphics using React, and "@expo/vector-icons" provides built-in support for popular icon fonts and allows you to create custom icons from your font and glyph map.
"Art" is a library for creating vector graphics using React, and "@expo/vector-icons" provides built-in support for popular icon fonts and allows you to create custom icons from your font and glyph map.
Running on Titan Engine | Model: llama3.2 | GPU Accelerated
Dev.to
https://dev.to/googleai/im-not-a-developer-but-i-built-a-calendar-app-to-fix-my-most-annoying-work-task-dj4
I'm not a developer, but I built a calendar app to fix my most annoying work task
I’m not a developer! I’ve never coded anything in my life. As far as I’m concerned, a Cloudtop is...
NPM Registry
https://www.npmjs.com/package/react-art
react-art
React ART is a JavaScript library for drawing vector graphics using React. It provides declarative and reactive bindings to the ART library. Using the same declarative API you can render the output to either Canvas, SVG or VML (IE8).
NPM Registry
https://www.npmjs.com/package/@expo/vector-icons
@expo/vector-icons
Built-in support for popular icon fonts and the tooling to create your own Icon components from your font and glyph map. This is a wrapper around react-native-vector-icons to make it compatible with Expo.
GitHub
https://github.com/AlexHuggler/-Predicting-the-number-of-shares-that-a-news-article-will-receive
AlexHuggler/-Predicting-the-number-of-shares-that-a-news-article-will-receive
An exercise in predicting the number of shares that a news article will receive using various machine learning models, including K Nearest Neighbour, Decision Trees, Support Vector Machines - (Linear, RBF and Polynomial kernels), and more. The exercise also includes examples of utilizing GridSearch and cross validation to select parameters. The dataset being used is the "The Online news popularity dataset" sourced from the UCI Machine learning repository. Available at http://archive.ics.uci.edu/ml/datasets/Online+News+Popularity
HackerNews
https://news.ycombinator.com/item?id=19734029
Microsoft Gives Paint the 11th Hour Reprieve It Deserves
Community Discussion / Points: 0
GitHub
https://github.com/casruger/Predict-the-number-of-citations-of-an-article-using-random-forest
casruger/Predict-the-number-of-citations-of-an-article-using-random-forest
Predict the number of citations an article will get using a random forest and features created by a Term Frequency Inverse Document Frequency Vectorizer
Dev.to
https://dev.to/devteam/congrats-to-the-hermes-agent-challenge-winners-3on0
Congrats to the Hermes Agent Challenge Winners!
We are thrilled to announce the winners of the Hermes Agent Challenge! Over the past few weeks, the...
NPM Registry
https://www.npmjs.com/package/@mapbox/vector-tile
@mapbox/vector-tile
Parses vector tiles
HackerNews
https://news.ycombinator.com/item?id=5006863
Are Designers Crazy?
Community Discussion / Points: 0
HackerNews
https://news.ycombinator.com/item?id=21245308
Flash Is Responsible for the Internet's Most Creative Era
Community Discussion / Points: 0
GitLab
https://gitlab.com/haukecblanken/fuzzydrift
Hauke Blanken / FuzzyDrift
Project to develop uncertainty propagation in particle tracking applications for 2D vector fields using fuzzy numbers. Primary application is ocean drift tracking for environmental emergency response, search and rescue, and others. Under development.
Dev.to
https://dev.to/devteam/congrats-to-the-gemma-4-challenge-winners-4fgc
Congrats to the Gemma 4 Challenge Winners!
We are so excited to announce the winners of the Gemma 4 Challenge! This is officially our most...
HackerNews
https://news.ycombinator.com/item?id=30728577
Software is no longer sold; it's adopted
Community Discussion / Points: 0
GitLab
https://gitlab.com/mesfingo/R-code-for-identifying-PCA-correlated-SNP-for-population-structure-identification
Mesfin / R-code-for-identifying-PCA-correlated-SNP-for-population-structure-identification
This R code is an implementation of a method described by Paschou et al in a serious of articles (Paschou et al 2007; Paschou et al 2008; Paschou et al 2010; Lewis et al 2011). Singular value decomposition is performed on genotype data, and SNP are ordered based on the squared sum of corresponding right singular vectors. The number of rows the right singular vectors are summed over depends on the number of significant principal components identified, which has to be determined beforehand (Tracy-Widom distribution and Broken-stick method come to mind).
GitHub
https://github.com/ignaciorlando/fundus-vessel-segmentation-tbme
ignaciorlando/fundus-vessel-segmentation-tbme
In this work, we present an extensive description and evaluation of our method for blood vessel segmentation in fundus images based on a discriminatively trained, fully connected conditional random field model. Standard segmentation priors such as a Potts model or total variation usually fail when dealing with thin and elongated structures. We overcome this difficulty by using a conditional random field model with more expressive potentials, taking advantage of recent results enabling inference of fully connected models almost in real-time. Parameters of the method are learned automatically using a structured output support vector machine, a supervised technique widely used for structured prediction in a number of machine learning applications. Our method, trained with state of the art features, is evaluated both quantitatively and qualitatively on four publicly available data sets: DRIVE, STARE, CHASEDB1 and HRF. Additionally, a quantitative comparison with respect to other strategies is included. The experimental results show that this approach outperforms other techniques when evaluated in terms of sensitivity, F1-score, G-mean and Matthews correlation coefficient. Additionally, it was observed that the fully connected model is able to better distinguish the desired structures than the local neighborhood based approach. Results suggest that this method is suitable for the task of segmenting elongated structures, a feature that can be exploited to contribute with other medical and biological applications.
GitHub
https://github.com/cedricdcc/article_sorter
cedricdcc/article_sorter
a demo application in python that will sort a given number of given links into a vector database and then will sort them by relevance to each other in categories
Dev.to
https://dev.to/gde/skills-over-system-prompts-building-an-anki-tutor-with-the-antigravity-sdk-2o8f
Skills over System Prompts: Building an Anki Tutor with the Antigravity SDK
AI has made me a little lazier. Not dramatically lazy. Not "the robots will do everything" lazy....
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