# Github Repository Published articles for Github Repository. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## One honojs file for entire web scraping API DevFeed: [One honojs file for entire web scraping API]() Original publisher: [Read original article]() Author: iHateReading Published: 2026-04-13T16:54:35Z Content type: tutorial Language: en Sources: [iHateReading]() Topics: [Hono](), [Web Scraping](), [API]() Tags: [api](), [backend](), [blockchain](), [coding](), [development](), [frontend](), [github-repository](), [honojs](), [ihatereading](), [javascript](), [node-js](), [one-honojs-file-for-entire-web-scraping-api](), [product](), [programming](), [react](), [scraping](), [typescript](), [web-development](), [web-scraping](), [web-scraping-github-repository-scraping-backend-honojs]() ### AI overview A tutorial about building web-scraping endpoints in a single Honojs file. ### Source excerpt Building scrape endpoints using one honojs file ## Packing Matrix-Vector Multiplication in Fully Homomorphic Encryption DevFeed: [Packing Matrix-Vector Multiplication in Fully Homomorphic Encryption]() Original publisher: [Read original article]() Published: 2024-09-07T04:18:09Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [homomorphic encryption](), [FHE](), [Encryption](), [layout](), [parallel](), [Python]() Tags: [arithmetic](), [code](), [cryptography](), [data](), [encryption](), [fhe](), [github-repository](), [homomorphic-encryption](), [layout](), [linear-algebra](), [lwe](), [mathematics](), [packing](), [parallel](), [programming](), [python](), [rlwe](), [simd](), [strategies]() ### AI overview This article explains packing for SIMD-style fully homomorphic encryption. It describes how to arrange plaintext data in RLWE ciphertexts so matrix-vector multiplication requires fewer alignment multiplications and rotations, then introduces two basic packing techniques and a computational model. ### Source excerpt In my recent overview of homomorphic encryption, I underemphasized the importance of data layout when working with arithmetic (SIMD-style) homomorphic encryption schemes. In the FHE world, the name given to data layout strategies is called "packing," because it revolves around putting multiple plaintext data into RLWE ciphertexts in carefully-chosen ways that mesh well with the operations you'd like to perform. By "mesh well" I mean it reduces the number of extra multiplications and rotations required merely to align data elements properly, rather than doing the actual computation you care about. ## MLIR -- Getting Started DevFeed: [MLIR -- Getting Started]() Original publisher: [Read original article]() Published: 2023-08-10T14:32:33Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [mlir](), [Compiler](), [toolchain](), [FHE](), [LLVM](), [Google](), [monorepo]() Tags: [build](), [build-system](), [compiler](), [compilers](), [documentation](), [encryption](), [fhe](), [github-repository](), [llvm](), [mathematics](), [mlir](), [monorepo](), [programming](), [toolchain]() ### AI overview This introductory article explains the structure of a typical MLIR project and the build system used in the HEIR project, a Google effort focused on production engineering tools for Fully Homomorphic Encryption. It also introduces the relationship between MLIR and LLVM and describes pinning the MLIR dependency to a specific commit hash. ### Source excerpt Table of Contents As we announced recently, my team at Google has started a new effort to build production-worthy engineering tools for Fully Homomorphic Encryption (FHE). One focal point of this, and one which I'll be focusing on as long as Google is willing to pay me to do so, is building out a compiler toolchain for FHE in the MLIR framework (Multi-Level Intermediate Representation). The project is called Homomorphic Encryption Intermediate Representation, or HEIR. ## Google's Recent FHE work, and starting HEIR DevFeed: [Google's Recent FHE work, and starting HEIR]() Original publisher: [Read original article]() Published: 2023-08-10T12:22:29Z Content type: article Language: en Sources: [Jeremy Kun]() Topics: [FHE](), [homomorphic encryption](), [Google](), [Compiler](), [Machine learning]() Tags: [compiler](), [encryption](), [fhe](), [github](), [github-repository](), [google](), [hardware](), [homomorphic-encryption](), [mlir](), [models](), [repository]() ### AI overview A Google team reports recent work on fully homomorphic encryption, including video processing, machine-learning model compilation, TPU implementation, and compiler improvements. The article also introduces HEIR, an early project intended to provide a standardized foundation for FHE compilers using MLIR. ### Source excerpt Today my team at Google published an article on Google's Developers Blog with some updates on what we've been doing with fully homomorphic encryption (FHE). There's fun stuff in there, including work on video processing FHE, compiling ML models to FHE, an FHE implementation for TPUs, and improvements to the compiler I wrote about earlier this year. TODO: add mower gif video A simple object movement tracking algorithm in FHE, tracking a runaway lawn mower from a Nest camera. ## Singular Value Decomposition Part 2: Theorem, Proof, Algorithm DevFeed: [Singular Value Decomposition Part 2: Theorem, Proof, Algorithm]() Original publisher: [Read original article]() Published: 2016-05-16T08:00:19Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [Algorithm](), [data](), [Code]() Tags: [algorithm](), [code](), [data](), [examples](), [github](), [github-repository](), [greedy-algorithm](), [mathematics](), [optimization](), [programming](), [python](), [singular-value-decomposition]() ### AI overview This tutorial develops the mathematical foundations of singular value decomposition through definitions, proofs, and an algorithm. It explains best-approximating k-dimensional linear subspaces, vector projection, and squared distance, and mentions testing the method on a thousand-story CNN news dataset with accompanying code and examples in a GitHub repository. ### Source excerpt I'm just going to jump right into the definitions and rigor, so if you haven't read the previous post motivating the singular value decomposition, go back and do that first. This post will be theorem, proof, algorithm, data. The data set we test on is a thousand-story CNN news data set. All of the data, code, and examples used in this post is in a github repository, as usual. We start with the best-approximating $ k$-dimensional linear subspace. ## Gemify your assets DevFeed: [Gemify your assets]() Original publisher: [Read original article]() Author: Carlos Alexandro Becker Published: 2013-02-05T00:00:00Z Content type: tutorial Language: en Sources: [Carlos Becker]() Topics: [Rails](), [rubygems](), [GitHub](), [Library]() Tags: [asset-pipeline](), [github-repository](), [how-to](), [pipeline](), [rails](), [release](), [rubygems]() ### AI overview A tutorial on packaging JavaScript assets as a Ruby gem for use with the Rails 3.1 asset pipeline. It covers creating the gem structure, adding assets and a Rails engine, testing the gem in a Rails application, documenting it, and releasing it through GitHub and RubyGems. ### Source excerpt Rails 3.1 introduced the asset pipeline, which makes it easy to include versioned external assets as application dependencies.