Articles for category: AI Tools

March 29, 2025

ikayaniaamirshahzad@gmail.com

使用 Google Apps Script 串接 Google Analytics API，整合多站數據

本篇要解決的問題 一間公司裡可能旗下會有多個網站，想同時查看所有網站的 GA 數據，通常需要開啟多個瀏覽器視窗並排顯示，操作較為繁瑣。 如果可以改由 API 來取得 GA 的數據，工程師就可以把各站的資料顯示在一個頁面上，而不用同時開多個 GA 來看。 開通 GA API 取得 GCP 專案編號 要先有 Google Cloud Platform（GCP）的專案，沒有的話登入自己的 Google 帳號，就可以先增一個。 專案編號就在 資訊主頁 上： 開通 GA API 功能 在使用 API 前，必須先於 GCP 開通對應的功能。 GCP 的專案點擊選單中的「API 和服務」> 「程式庫」： 搜尋框上搜尋「google analytics data api」，會看到結果清單裡會出現「Google Analytics Data API」，點進去後，再點擊啟用，就完成了： 取得 GA 的資源 ID 看要抓的是哪一個 GA 的資料，進到 GA 後台，進到「管理」，點擊「資源詳細資料」： 接著右上角就會看到資源編號： 調用 GA API 程式碼部份 因為調用 Google 的 API，要先經過認證的程序，但如果是寫在跟 GA 相同帳號的 Google Apps Script，就可以省掉這一段。 Google Apps Script 上新增專案 到 Google Apps Script 的頁面上，新增一個專案： 接著可以直接複製貼上以下的程式碼。 瀏覽量、活躍人數 以下程式碼是抓瀏覽量、活躍人數的： var propertyId = "xxxxxx";  // 替換成 GA 的資源編號 var startDate = "2025-01-01", // 替換成想要從哪一天開始抓的日期 function getGA4Data() { var apiUrl

March 29, 2025

ikayaniaamirshahzad@gmail.com

Introducing spaCy v3.2 · Explosion

We’re pleased to present v3.2 of the spaCy Natural Language Processing library. Since v3.1 we’ve added usability improvements for custom training and scoring, improved performance on Apple M1 and Nvidia GPU hardware, and support for space-efficient vectors using floret, our new hash embedding extension to fastText. The spaCy team has gotten a lot bigger this year, and we’ve got lots of exciting features and examples coming up, including example projects for data augmentation and model distillation, more examples of transformer-based pipelines, and new components for coreference resolution and graph-based parsing. spaCy is now up to 8 × faster on M1

March 29, 2025

ikayaniaamirshahzad@gmail.com

Constitutional AI with Open LLMs

Since the launch of ChatGPT in 2022, we have seen tremendous progress in LLMs, ranging from the release of powerful pretrained models like Llama 2 and Mixtral, to the development of new alignment techniques like Direct Preference Optimization. However, deploying LLMs in consumer applications poses several challenges, including the need to add guardrails that prevent the model from generating undesirable responses. For example, if you are building an AI tutor for children, then you don’t want it to generate toxic answers or teach them to write scam emails! To align these LLMs according to a set of values, researchers at

March 29, 2025

ikayaniaamirshahzad@gmail.com

Lynx Prototype Website Released…🐾 – DEV Community

Lynx cPanel Prototype is Live! Hello developers! 🚀 We have officially launched Lynx’s prototype site running smoothly on cPanel. Here’s a quick summary of what’s new in this phase. CDN System Integrated For the first time, we are introducing an optional CDN system in Lynx projects! 📡 You will be able to manage your static files effortlessly via a dedicated CDN panel, ensuring better performance. The CLI tool will allow seamless CDN integration with just a few commands. Lynx in a Real Environment This prototype demonstrates Lynx’s ability to connect to a real MySQL server and handle sessions and data

March 29, 2025

ikayaniaamirshahzad@gmail.com

Talking sense: using machine learning to understand quotes |

For the last six months, we have been part of the 2021 JournalismAI Collab Challenges, a project connecting global newsrooms to understand how artificial intelligence can improve journalism. Our particular challenge was to answer this question: “How might we use modular journalism and AI to assemble new storytelling formats and reach underserved audiences?” Participating newsrooms were organised into teams to define the challenges they would work on, imagine potential solutions, and turn them into prototypes. Our team included newsrooms from across Europe, Africa and the Middle East. Although we all attract different audiences, produce different types of content and have

March 29, 2025

ikayaniaamirshahzad@gmail.com

Unveiling the Reasoning Abilities of Large Language Models through Complexity Classes and Dynamic Updates

We’re happy to introduce the NPHardEval leaderboard, using NPHardEval, a cutting-edge benchmark developed by researchers from the University of Michigan and Rutgers University. NPHardEval introduces a dynamic, complexity-based framework for assessing Large Language Models’ (LLMs) reasoning abilities. It poses 900 algorithmic questions spanning the NP-Hard complexity class and lower, designed to rigorously test LLMs, and is updated on a monthly basis to prevent overfitting! A Unique Approach to LLM Evaluation NPHardEval stands apart by employing computational complexity classes, offering a quantifiable and robust measure of LLM reasoning skills. The benchmark’s tasks mirror real-world decision-making challenges, enhancing its relevance and applicability.

March 29, 2025

ikayaniaamirshahzad@gmail.com

What are the application areas of large-scale network interaction technology?

Our team has developed a technology that enables developers to easily construct a system capable of handling tens of thousands of connections and running hundreds of thousands of objects in the cloud in real time. The key feature is that developers only need to design the objects required for the system to operate and define the interactions between these objects, much like single-machine development. Our system automatically distributes the objects across different hosts while ensuring data consistency. This means developers no longer have to deal with the issues posed by traditional sharding or server-splitting techniques. There’s no need to design

March 29, 2025

ikayaniaamirshahzad@gmail.com

spaCy v3’s project and config systems are pretty great · Explosion

Machine Learning Engineers who turn prototypes into production-ready software face difficulties with the lack of tooling and best-practices. spaCy v3, with its configuration and project system, introduced a way to solve this problem. Here’s my take on how it works, and how it can ramp-up your team! I’ve been using spaCy for a few years now, as I did a lot of NLP projects both during my studies and previous work. Now that I get to work on it full-time after joining Explosion in October, I’ve been thinking a lot about what I’ve liked in the library and how it

March 29, 2025

ikayaniaamirshahzad@gmail.com

Segmind Mixture of Diffusion Experts

SegMoE is an exciting framework for creating Mixture-of-Experts Diffusion models from scratch! SegMoE is comprehensively integrated within the Hugging Face ecosystem and comes supported with diffusers 🔥! Among the features and integrations being released today: Table of Contents What is SegMoE? SegMoE models follow the same architecture as Stable Diffusion. Like Mixtral 8x7b, a SegMoE model comes with multiple models in one. The way this works is by replacing some Feed-Forward layers with a sparse MoE layer. A MoE layer contains a router network to select which experts process which tokens most efficiently. You can use the segmoe package to

March 29, 2025

ikayaniaamirshahzad@gmail.com

AI Development Made Simple for Web Developers!

If you don’t know anything about AI SDK, you might want to read this as well. Hello there. Lately, I’ve been sharing a lot about AI, but I understand that AI can sometimes sound like it’s from another world. Words like AI, artificial intelligence, singularity, agent, and MCP all carry such weight that when you dig into them, you might feel overwhelmed in just an instant. It might seem that these topics are far removed from the daily work of most developers—whether frontend or backend. Of course, when you hear discussions about the job market and such, you might think