# Vectors, Published articles for Vectors,. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Geometry Nodes Physics DevFeed: [Geometry Nodes Physics]() Original publisher: [Read original article]() Author: Jacques Lucke Published: 2026-07-30T14:52:49Z Content type: article Language: en Sources: [Blender]() Topics: [Simulation](), [Framework](), [systems]() Tags: [blender](), [collection](), [experimental](), [features](), [general-development](), [geometry-nodes](), [gravity](), [lts](), [mesh](), [node](), [physics](), [procedural](), [systems](), [vectors](), [workflow]() ### AI overview This article describes Blender 5.2 LTS's new experimental hair and cloth dynamics system built with Geometry Nodes. It explains the declarative XPBD simulation framework, cloth and hair workflows, geometry bundles, and customizable effectors such as colliders, custom forces, and custom behavior closures. ### Source excerpt Geometry Nodes Physics in Blender 5.2 LTS and beyond. ## Building the AI Retrieval Infrastructure Behind 20 Billion+ Vectors at HubSpot DevFeed: [Building the AI Retrieval Infrastructure Behind 20 Billion+ Vectors at HubSpot]() Original publisher: [Read original article]() Author: Oleg Tereshin & Xin Liu Published: 2026-06-25T18:17:44Z Content type: article Language: en Sources: [HubSpot]() Topics: [Qdrant](), [AI search](), [Artificial Intelligence](), [Embeddings](), [Retrieval-Augmented Generation](), [Open Source](), [systems](), [quantization]() Tags: [ai](), [embeddings](), [engineering](), [latency](), [open-source](), [quantization](), [rag](), [retrieval](), [search](), [vector-database](), [vectors]() ### AI overview HubSpot describes how it built VaaS, a centralized vector storage and search platform using Qdrant, to support semantic search across tens of billions of vectors and many use cases. ### Source excerpt Discover how HubSpot built a scalable AI retrieval infrastructure, managing over 20 billion vectors with Qdrant, to enhance semantic search and support diverse applications. ## Mapping Strings to Float Arrays in Go: How Fast Can We Go? DevFeed: [Mapping Strings to Float Arrays in Go: How Fast Can We Go?]() Original publisher: [Read original article]() Author: Daniel Lemire Published: 2026-05-05T19:01:09Z Content type: article Language: en Sources: [Daniel Lemire]() Topics: [Go Language](), [Benchmark](), [Data structures](), [floating-point]() Tags: [go](), [harness](), [memory](), [performance](), [vectors]() ### AI overview This article examines mapping strings to small float arrays in Go using the constmap library. It explains an immutable map based on a binary fuse filter and compares index-based and pointer-based value storage with a standard Go map. In the reported benchmark, the indexed constmap was twice as fast as the Go map, while raw pointers reduced lookup time further, with important garbage-collector and serialization limitations. ### Source excerpt A common pattern in modern software is to map a string key to a small array of floating-point numbers. Word embeddings, feature vectors, lookup tables for physical constants: all variations on the same theme. In Go, the obvious way to write this is a map[string][]float32. But how fast is it, really, and can we do ... Continue reading Mapping Strings to Float Arrays in Go: How Fast Can We Go? ## For the love of craft: Vectorize images in Figma DevFeed: [For the love of craft: Vectorize images in Figma]() Original publisher: [Read original article]() Author: Rogie King Published: 2026-02-04T16:54:40.464000Z Content type: article Language: en Sources: [Figma Blog]() Topics: [Figma](), [Artificial Intelligence]() Tags: [ai](), [design](), [figma](), [image-editing](), [interface](), [vectors](), [workflow]() ### AI overview Figma introduces Vectorize, an AI image editing tool in Figma Design and Figma Draw that converts raster images into editable vectors. The article describes using it to refine sketches, hand-drawn typography, and other visual references while preserving editable design workflows. ### Source excerpt With our new AI image editing tool, Vectorize, you can turn raster images into editable vectors, allowing you to tweak, refine, and scale designs directly in Figma. ## Plan for Clojure AI, ML, and high-performance Uncomplicate ecosystem in 2026 DevFeed: [Plan for Clojure AI, ML, and high-performance Uncomplicate ecosystem in 2026]() Original publisher: [Read original article]() Published: 2025-11-29T00:41:00Z Content type: opinion Language: en Sources: [Dragan Djuric]() Topics: [Clojure](), [Artificial Intelligence](), [Machine learning](), [CUDA](), [OpenCL](), [Programming](), [NumPy]() Tags: [2026](), [ai](), [ai-ml](), [algebra](), [apple](), [clojure](), [cuda](), [linear](), [matrices](), [neanderthal](), [opencl](), [programming](), [vectors]() ### AI overview The article outlines a 2026 development and funding plan for the Uncomplicate ecosystem of Clojure libraries for AI, machine learning, and high-performance computing. It describes support for Nvidia GPUs, Apple Silicon, CPUs, CUDA, OpenCL, and several planned library improvements. ### Source excerpt I've applied for Clojurists Together yearly funding in 2026. Here's my application. If you are a Clojurists Together member, and would like to see continued development in this area, your vote can help me keep working on this :) My goal with this funding in 2026 is to continuously develop Clojure AI, ML, and high-performance ecosystem of Uncomplicate libraries (Neanderhal and many more), on Nvidia GPUs, Apple Silicon, and traditional PC. In this year, I will also focus on writing tutorals on my blog and creating websites for the projects involved, which is something that I wanted for years, but didn't have time to do because I spent all time on programming. How that work will benefit the Clojure community This will highly benefit the Clojure community as this is THE AI ecosystem for Clojure, and supporting AI is arguably the main focus on probably all software platforms. Clojure has something to offer on that front, beyond just calling OpenAI API as a web service! Uncomplicate grew to quite a few libraries (of which some are quite big; just Neanderthal is 28,000 lines of highly-condensed, aggresively macroized, and reusable code): Diamond ONNX Runtime, Neanderthal, Deep Diamond, ClojureCUDA, ClojureCPP, Apple Presets, ClojureCL, Fluokitten, Bayadera, Clojure Sound, and Commons. Here's a word or two of how I hope to improve each of these libraries with Clojurists Together funding in 2026. Neanderthal (Clojure's alternative to NumPy, on steroids) In 2025, Neanderthal celebrated its 10th birthday. It started as a humble but fast matrix and vector library for Clojure, but after 10 years of relentless improvements, now it boasts a general matrix/vector/linear algebra API implemented by no less than 5(!) engines for CPUs, GPU (Nvidia CUDA), GPU (OpenCL: AMD, Intel, Nvidia), Apple Silicon (Accelerate), and general CPU (OpenBLAS). And this is not a superficial support for the sake of ticking a check box; each of these engines support much more operations on exotic structure ## What Are Gradient, Divergence, and Curl in Vector Calculus? DevFeed: [What Are Gradient, Divergence, and Curl in Vector Calculus?]() Original publisher: [Read original article]() Author: Charles Udekwe Published: 2025-07-15T22:24:11Z Content type: tutorial Language: en Sources: [Baeldung - CS]() Topics: [math](), [Computer science]() Tags: [math](), [math-and-logic](), [math-and-logic-optimization](), [optimization](), [physics](), [tutorial](), [vectors](), [wi-fi]() ### AI overview This tutorial explains gradient, divergence, and curl in vector calculus. It introduces scalars, vectors, scalar fields, vector fields, Cartesian coordinates, and the Del operator, then illustrates gradient using Wi-Fi signal strength and discusses practical applications. ### Source excerpt Learn about the gradient, curl, and divergence in vector calculus and their applications. The post What Are Gradient, Divergence, and Curl in Vector Calculus? first appeared on Baeldung on Computer Science. Related Stories What Is the Gradient Norm? The Method of Lagrange Multipliers How to Find the Maximum Value in Relational Algebra ## pgvector 0.6.0: 30x faster with parallel index builds DevFeed: [pgvector 0.6.0: 30x faster with parallel index builds]() Original publisher: [Read original article]() Author: Egor Romanov Published: 2024-01-30T07:00:00Z Content type: article Language: en Sources: [Supabase Blog]() Topics: [Database](), [build performance](), [Concurrency](), [Benchmark](), [Algorithm](), [Graphs](), [dataset]() Tags: [benchmark](), [build](), [build-performance](), [database](), [dataset](), [graphs](), [parallel](), [performance](), [thread](), [time](), [vectors]() ### AI overview pgvector 0.6.0 introduces parallel HNSW index builds, reducing build times substantially while preserving query throughput and index accuracy. Benchmarks show improvements of up to 30x for unlogged tables and up to 13.5x with more powerful instances. ### Source excerpt pgvector 0.6.0 brings a significant improvement: parallel index builds for HNSW. Building an HNSW index is now up to 30x faster for unlogged tables. ## pgvector: Fewer dimensions are better DevFeed: [pgvector: Fewer dimensions are better]() Original publisher: [Read original article]() Author: Greg Richardson; Oliver Rice; Egor Romanov Published: 2023-08-03T07:00:00Z Content type: article Language: en Sources: [Supabase Blog]() Topics: [Embeddings](), [Supabase](), [AI search](), [hugging face]() Tags: [compute](), [embedding](), [hardware](), [index](), [information-retrieval](), [openai](), [performance](), [pgvector](), [provisioning](), [vectors]() ### AI overview This article explains why using embedding vectors with fewer dimensions can improve pgvector performance. It covers vector storage in Postgres, similarity indexes, memory and compute requirements, and the scaling challenges of high-dimensional embeddings. ### Source excerpt Increase performance in pgvector by using embedding vectors with fewer dimensions ## The Inner Product as a Decision Rule DevFeed: [The Inner Product as a Decision Rule]() Original publisher: [Read original article]() Published: 2017-05-22T08:00:00Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [math](), [Code]() Tags: [arithmetic](), [inner-product](), [javascript](), [linear-algebra](), [machine-learning](), [math](), [mathematics](), [programming](), [projection](), [support-vector-machines](), [vectors]() ### AI overview This article explains how the standard inner product, or dot product, acts as a geometric decision rule. It shows how the sign of the product determines which side of a line through the origin a vector lies on, while zero indicates that the vector lies on the line. The explanation connects this behavior to vector projection. ### Source excerpt The standard inner product of two vectors has some nice geometric properties. Given two vectors $ x, y \in \mathbb{R}^n$, where by $ x_i$ I mean the $ i$-th coordinate of $ x$, the standard inner product (which I will interchangeably call the dot product) is defined by the formula $$\displaystyle \langle x, y \rangle = x_1 y_1 + \dots + x_n y_n$$ This formula, simple as it is, produces a lot of interesting geometry. ## A Reminder of Lagrange Multipliers for Optimization Problems DevFeed: [A Reminder of Lagrange Multipliers for Optimization Problems]() Original publisher: [Read original article]() Published: 2013-11-30T09:00:50Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [Optimization](), [function](), [Variable]() Tags: [convex-functions](), [function](), [gradient-descent](), [lagrange](), [lagrange-multipliers](), [linear-algebra](), [optimization](), [problems](), [variable](), [vectors]() ### AI overview A tutorial-style reminder of Lagrange multipliers for optimization problems. It reviews gradients, partial derivatives, dot products, and directional change for multivariable functions. ### Source excerpt For a while I've been meaning to do some more advanced posts on optimization problems of all flavors. One technique that comes up over and over again is Lagrange multipliers, so this post is going to be a leisurely reminder of that technique. I often forget how to do these basic calculus-type things, so it's good practice. We will assume something about the reader's knowledge, but it's a short list: know how to operate with vectors and the dot product, know how to take a partial derivative, and know that in single-variable calculus the local maxima and minima of a differentiable function $ f(x)$ occur when the derivative $ f'(x)$ vanishes. ## Inner Product Spaces--A Primer DevFeed: [Inner Product Spaces--A Primer]() Original publisher: [Read original article]() Published: 2011-07-25T00:29:00Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [Mathematics](), [Math and Logic](), [Computing]() Tags: [big-o-notation](), [eigenvectors](), [inner-product](), [mathematics](), [matrices](), [orthogonality](), [symmetry](), [vector-spaces](), [vectors]() ### AI overview This primer explains why vector spaces can be extended with a dot-product-like operation and defines inner products through conjugate symmetry, linearity, additivity, and positive definiteness. It introduces inner product spaces and discusses their connection to linear functionals. ### Source excerpt Vector spaces alone are not enough to do a lot of the interesting things we'd like them to do. Since a vector space is a generalization of Euclidean space, it is natural for us to investigate more specific types of vector spaces which are more akin to Euclidean space. In particular, we want to include the notion of a dot product. By admitting additional structure to a vector space, we may perform more computations, and hopefully get more interesting results.