MooAI Insight
Learning Background Vector Art
To learn background vector art, you can start by exploring libraries and tools that make it easy to create and manipulate vector graphics. Here are a few options:
* React ART: A JavaScript library for drawing vector graphics using React. It provides declarative and reactive bindings to the ART library.
* ruvector: A self-learning vector database for Node.js that uses hybrid search, Graph RAG, FlashAttention-3, HNSW, and 50+ attention mechanisms.
You can also check out online resources such as tutorials, videos, and blogs that cover topics like:
* Vector graphics basics
* Color theory and palettes
* Composition and design principles
Additionally, you can explore online communities and forums where artists and designers share their knowledge and experiences with vector art.
To learn background vector art, you can start by exploring libraries and tools that make it easy to create and manipulate vector graphics. Here are a few options:
* React ART: A JavaScript library for drawing vector graphics using React. It provides declarative and reactive bindings to the ART library.
* ruvector: A self-learning vector database for Node.js that uses hybrid search, Graph RAG, FlashAttention-3, HNSW, and 50+ attention mechanisms.
You can also check out online resources such as tutorials, videos, and blogs that cover topics like:
* Vector graphics basics
* Color theory and palettes
* Composition and design principles
Additionally, you can explore online communities and forums where artists and designers share their knowledge and experiences with vector art.
Running on Titan Engine | Model: llama3.2 | GPU Accelerated
HackerNews
https://news.ycombinator.com/item?id=36322424
Ask HN: Application Focussed AI Learning
Community Discussion / Points: 1
HackerNews
https://www.metal.graphics/
Show HN: Metal.graphics – Metal shaders course for SwiftUI
Community Discussion / Points: 1
NPM Registry
https://www.npmjs.com/package/ruvector
ruvector
Self-learning vector database for Node.js — hybrid search, Graph RAG, FlashAttention-3, HNSW, 50+ attention mechanisms
HackerNews
https://news.ycombinator.com/item?id=9616155
Deep Learning – Review by LeCun, Bengio, and Hinton
Community Discussion / Points: 0
HackerNews
https://news.ycombinator.com/item?id=2511224
How to: Pass a Silicon Valley Software Engineering Interview
Community Discussion / Points: 0
Dev.to
https://dev.to/googlecloud/seamless-scaling-with-vpa-in-place-pod-resize-on-gke-117p
Seamless scaling with VPA In-place Pod Resize on GKE
Learn how VPA In-place Pod Resize can help seamlessly vertically scale workloads on Google Kubernetes Engine (GKE).
Dev.to
https://dev.to/ben/meme-monday-1m9f
Meme Monday
Meme Monday! Today's cover image comes from the last thread. DEV is an inclusive space! Humor in...
NPM Registry
https://www.npmjs.com/package/@ruvector/sona
@ruvector/sona
Self-Optimizing Neural Architecture (SONA) - Runtime-adaptive learning with LoRA, EWC++, and ReasoningBank for LLM routers and AI systems. Sub-millisecond learning overhead, WASM and Node.js support.
Dev.to
https://dev.to/hadil/youre-a-real-typescript-developer-only-if-1d9o
You’re a Real TypeScript Developer Only If...
A few months ago, I published You're a Real JavaScript Developer Only If... It was just a post for...
GitHub
https://github.com/mpperez3/semi-automatic-gluten-curation-resources
mpperez3/semi-automatic-gluten-curation-resources
Background. Discover relevant biomedical interactions in the literature is crucial for enhancing biology research. It has an essential role in studying the different processes and interactions reported that affect the biological process (e.g., genome, metabolome, and transcriptome). Therefore, the objective of this work is twofold: reduce the manual effort required to curate and review the existing biochemical interactions reported in the gluten-related bibliome while proposing a novel relation extraction deep learning approach that assists in a real curation task by learning from the previous decisions of the curators. Methods. Compared to previous works, the main contribution of this work lies in proposing a deep learning model that incorporates a novel vector-space that combine (i) high-level lexical and syntactic inference features as Wordnets and Health-related domain ontologies, (ii) unsupervised domain syntactic and semantic resources as word embeddings, (iii) semantical and sentence structure knowledge (e.g., part of speech, negation information, verb information), (iv) abbreviation resolution support, (v) several state-of-the-art Named-entity recognition methods, and (vi) different feature construction and optimization techniques to support a semi-automatic curation workflow. Results.The application of the semi-automatic curation workflow over a classified set of 2,451 relevant gluten-related documents produces a total of 8,349 relevant relations and 471,813 irrelevant relations of the next relation categories: (i) Related health issue, (ii) Improve, (iii) Aggravate, (iv) Stimulation, (v) Inhibition, (vi) Activation, (vii) Deactivation, (viii) Downregulation, (ix) Upregulation, (x) increase symptoms, (xi) decrease symptoms, (xii) weak relation and (xiii) no effect. Therefore, the mean achieved F-score for the different relation categories established was 0.731, with the lowest F.score at 0.47 (with 200 positive identified relations) and the highest F.score at 0.929 (with 2,129 positive identified relations). Experimental results showed that the presented workflow is an excellent approach for a semi-automatic RE task. It was able to obtain satisfactory results in the early stages of a real-world curation task and saved manual annotation efforts by learning from the decisions made by manual curators. On the other hand, the presented sentence vector-space can be integrated into several state-of-the-art machine learning models to recognize relevant relations with satisfactory results. Finally, this work highlights the benefit of use domain knowledge as ontologies and entity recognizers to improve the automatic recognition of health-related interactions in the literature.
Dev.to
https://dev.to/rodrigovidal/physics-engineering-and-architecture-in-software-systems-and-the-obsession-with-architecture-68j
Physics, Engineering, and Architecture in Software Systems and the obsession with Architecture
Something that has been bothering me for a while in the software industry is how disproportionately...
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).
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