AI applications are generating more data than ever before. From model telemetry and user interactions to observability events and real-time analytics, modern systems need infrastructure that can ingest, process, and query massive datasets with low latency. That's exactly the problem ClickHouse is targeting with its latest release. The update introduces improvements across query performance, memory management, Kafka integration, lakehouse support, and developer tooling. While many of these changes appear incremental on the surface, together they highlight a much larger shift happening across the industry. One of the most notable additions is improved memory management for large joins. ClickHouse can now automatically spill hash joins to disk when memory usage exceeds configured thresholds. Instead of failing due to memory pressure, queries can continue running using more efficient execution strategies. For teams working with large feature tables, event enrichment, AI telemetry, or observability data, this can significantly improve reliability. The release also expands ClickHouse's Kafka capabilities with Schema Registry integration, AvroConfluent write support, metadata mapping, and zone-aware communication. These improvements make it easier to integrate ClickHouse into real-time event pipelines while reducing latency and unnecessary cross-zone traffic in cloud environments. Another major focus is support for modern lakehouse architectures. Improvements for Apache Iceberg and Apache Paimon strengthen ClickHouse's ability to query data stored in open table formats while maintaining high analytical performance. As more organizations separate storage and compute, ClickHouse is increasingly positioning itself as a high-speed query layer on top of cloud-native data lakes. Performance optimization remains a major theme throughout the release. Improvements include faster JOIN execution, better ORDER BY LIMIT performance, enhanced JSON processing, smarter index pruning, redu

A linked list is an ordered linear data structure where elements are not stored in sequential memory locations, instead they are stored in nodes that are linked together by a pointers. Linked list are used in data intensive application because linked list offer specific benefits for high frequency data manipulation, this benefits include: Efficient insertion and deletion Adding and removing elements from a linked list is highly efficient, unlike arrays which requires shifting all subsequent elements to maintain indexing. A linked list only requires updating the pointer. Dynamic sizing: linked list can grow or shrink during runtime without needing to pre-allocate memory. Memory management: Nodes in a linked list are only allocated when needed which prevents memory wastage. Flexible Traversal: Doubly and circular list allow you to move forward or backward, which makes them helpful for complex navigation The first node in a linked list is called the head which signifies the start of the list, while the last node is called the tail and has a pointer of null except in a circular linked list. Each node in a linked list has two things which are: the actual data the pointer or reference There are three main types of linked list: Singly linked list Doubly linked list Circular linked list Singly Linked List: Singly linked list are lists where each node has a next pointer that points to the next node. Doubly Linked List: Doubly linked list are list where each node has a next and previous pointer that points to the previous and next node. Circular Linked List: Circular linked list are list where the last node points back to the first node, forming a circle. Table of Contents create node class create linked list class isEmpty and getSize Methods prepend and append Method removeHead and removeTail Methods insert and search Methods getIndex and removeIndex Methods clear and print Methods create node class First let's open our code editor and create a new file called singlyLinkedLi

Keeping up with trending technical topics and new tools on developer forums can be time-consuming. To save time, I wanted to automate the process of finding popular articles, reading the comments to understand community sentiment, and drafting a summary. While I could write a standard Python script to scrape the dev.to API, simple scripts tend to be brittle. If an article doesn't have comments yet, a basic script will likely crash unless you write extensive error-handling logic. Instead of a rigid script, I built an Agent —a program that can dynamically reason about errors and adjust its approach. If one task fails, it can figure out the next best step. In this tutorial, I'll show you how to build a Trend-Spotting Agent using Python, the Google Agent Development Kit (ADK) , and Gemini 2.5 Flash. What We're Building We are going to write a Python application that acts as an autonomous agent. We'll give it three abilities: Search the dev.to API for rising technical articles based on specific tags. Dynamically fetch the top comments of those articles to read real community sentiment. Automatically draft a newsletter-style article on your DEV.to account summarizing its findings. Prerequisites Python 3.9+ installed on your machine. Google ADK . (Check out the Google ADK Docs if you need help installing). A DEV API Key . Grab this from your DEV.to account settings under "Extensions" and throw it in a .env file. Step 1: Giving the Agent its "Hands" (API Tools) Large Language Models (LLMs) are incredibly smart, but out of the box, they can't actually do anything on your computer. The coolest part about Google ADK is that we can write standard Python functions, hand them to the LLM as "tools", and let the AI decide how and when to use them. Let's write our API functions. Tool 1: Finding Rising Articles Here is our function to fetch rising articles. Pay close attention to the docstring ( """Fetches the top...""" ). We aren't writing this for other developers; the ADK actually

Stop Shipping 20 Locale Files in React Native: On-Device Translation for Dynamic Language Packs Internationalization in mobile apps usually starts clean and then gets expensive. At first, you keep a couple of JSON files: en.json es.json fr.json That works when your product is small and the set of languages is stable. It breaks down when: you want to support many languages the product team keeps changing copy translated files drift out of sync some languages are only partially used you do not want to run every string through a server-side translation pipeline This is the problem @tcbs/react-native-language-translator is trying to solve. It lets a React Native app keep a source language, translate missing keys on device, and cache the generated language pack locally. Package: @tcbs/react-native-language-translator The problem Many React Native apps treat localization as a static asset problem: keep one JSON file per language ship all of them in the app update all of them whenever English changes That model has real costs. 1. Translation files become operational debt Every new feature adds more keys. Every copy change forces translators to update multiple locale files. Over time, the translation layer becomes a maintenance queue. The result is predictable: missing keys stale translations untranslated fallback strings inconsistent release quality across languages 2. Shipping many locales is wasteful Most users only need one target language. But many apps ship every locale anyway. That increases bundle size and creates a lot of dead weight for users who will never use most of those files. 3. Dynamic product copy is hard to localize well If your app changes quickly, static translation files lag behind. Teams either accept stale translations or build a backend workflow to keep everything synchronized. That is often more infrastructure than the app actually needs. 4. Server-side translation is not always the right tradeoff Calling a translation API at runtime introduces: la

Teach your AI to build document templates Discussion | Link

presenting a poster there, and have registration covered. but they are placing me on waitlist for travel funds. As my travel depends on whether I get the travel grant, I need to get this off of my mind, either invite me or just say no. I'm waiting forever for this, more wait again? should i ask for a decision, or what to do. submitted by /u/Active-Tip3130 [link] [留言]

Ship AI that stays on-brand Discussion | Link

Donald Trump killed an executive order to regulate AI. Now, administration officials and AI executives are trying to figure out if there’s anything left to piece back together.

You don’t need expensive software for basic PDF tasks. In fact, all you need is a handful of free web-based apps.

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Amazon has announced that its Prime Day event will take place this year from June 23 to June 26, a couple of weeks earlier than it happened last year.

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Hello this might be weird but I am doing a study on society's view on AI as a school project. Therefore I am asking all kinds of communities and trying to get a very wide audience. This is clearly an AI sentric sub so hopefully his is relavent? I would be very happy if any of you would like to be a part of it! submitted by /u/Timely_Special_5011 [link] [留言]

This article is from Making AI Work, MIT Technology Review’s limited-run newsletter examining how to apply LLMs across industries. To receive it in your inbox,sign up here. From accounting to design to market research and product development, there’s a staggering breadth of skills needed to run a business. A large company can hire experts to…

In this article, author Aaditya Chauhan discusses the limitations of RAG pipelines based purely on vector search and how an internal omni-search application using Reciprocal Rank Fusion (RRF) that combines BM25 and vector results, can enhance the search solution. By Aaditya Chauhan