News Aggregator

Alignment Tools

Aggregated on: 2024-07-22 17:53:29

Understanding and implementing the right alignment tools in Agile product development can significantly enhance the effectiveness of your team and organization. Fostering better relationships between stakeholders and teams can ensure strategic clarity, improve adaptability, and maintain a user-centric focus. This article provides actionable insights on leveraging these tools to build trust, enhance collaboration, navigate risks, and maximize value creation. This will ultimately lead to more successful product outcomes aligned with organizational goals.

The Foundation of AI and Analytics Success: Why Architecture Matters

Aggregated on: 2024-07-22 17:53:29

In the rapidly evolving landscape of AI and analytics, the importance of a robust architecture cannot be overstated. As businesses increasingly rely on data-driven insights and AI-powered solutions, the underlying infrastructure that supports these technologies becomes a critical factor in their success. Recently, a panel of experts gathered to discuss architecture's pivotal role in AI and analytics, sharing insights on business drivers, common pitfalls, and innovative solutions. Business Drivers Shaping AI and Analytics Architectures Several essential business requirements are driving the need for advanced AI and analytics architectures:

Enhancing the Visibility of Integration Tests

Aggregated on: 2024-07-22 17:08:29

In modern software development, effective testing plays a key role in ensuring the reliability and stability of applications. This article offers practical recommendations for writing integration tests, demonstrating how to focus on the specifications of interactions with external services, making the tests more readable and easier to maintain. The approach not only enhances the efficiency of testing but also promotes a better understanding of the integration processes within the application. Through the lens of specific examples, various strategies and tools - such as DSL wrappers, JsonAssert, and Pact - will be explored, offering the reader a comprehensive guide to improving the quality and visibility of integration tests.

Improving the Capabilities of LLM-Based Analytics Copilots With Semantic Search and Fine-Tuning

Aggregated on: 2024-07-22 15:38:29

Picture this: You're an analyst drowning in a sea of data, trying to make sense of complex attribution models and customer journeys. Wouldn't it be great if you had a super-smart AI assistant that could instantly answer your questions, generate SQL queries on the fly, and break down complex tabular data? Well, that's exactly what we're working on with Large Language Model (LLM)- based analytics copilots. But as with any cutting-edge tech, it's not all smooth sailing. Let's dive into the challenges we faced and the cool solutions we came up with to make these AI assistants truly shine. The LLM Conundrum: Brilliant, but Flawed First things first: let's talk about why we're so excited about using LLMs in analytics. These language models are like the Swiss Army knives of the AI world – they can tackle a wide range of tasks, from answering questions to generating code. For us analysts, that means:

Variance: The Heartbeat of Agile Metrics

Aggregated on: 2024-07-21 14:23:29

When teams get their variance right, everything else falls into place. Variance is a measure of whether teams are doing what they say they are going to do. A team with high variance is over-committing or under-delivering. A team with low variance is delivering on its plans. In this case, stakeholders can feel confident in the team, the team can celebrate at the end of each sprint, and longer-term planning is likely to be accurate.

Building Database Connections and Migrations in Go With GORM and Goose

Aggregated on: 2024-07-20 14:08:28

Managing database connections and migrations is crucial for any application. In Go, we can leverage powerful libraries such as GORM for ORM functionality and Goose for database migrations. This article walks you through setting up a robust database client, handling migrations, preloading associations, and querying data efficiently. Tutorial Prerequisites Before we dive into the implementation, ensure you have the following installed:

Setting Up a Local Development Environment With IntelliJ, DevContainers, and Amazon Linux 2023

Aggregated on: 2024-07-19 23:08:28

In modern software development, containerization offers an isolated and consistent environment, which is crucial for maintaining parity between development and production setups. This guide provides a comprehensive walkthrough on creating a local development environment using IntelliJ IDEA, DevContainers, and Amazon Linux 2023 for Java development. Why Use DevContainers? What Are DevContainers? DevContainers are a feature provided by Visual Studio Code and other IDEs like IntelliJ IDEA through extensions. They allow you to define a consistent and reproducible development environment using Docker containers. By encapsulating the development environment, you ensure that all team members work in an identical setup, avoiding the "it works on my machine" problem.

Topic Tagging Using Large Language Models

Aggregated on: 2024-07-19 21:08:28

Topic Tagging Topic tagging is an important and widely applicable problem in Natural Language Processing, which involves tagging a piece of content — like a webpage, book, blog post, or video — with its topic. Despite the availability of ML models like topic models and Latent Dirichlet Analysis [1], topic tagging has historically been a labor-intensive task, especially when there are many fine-grained topics. There are numerous applications to topic-tagging, including: Content organization, to help users of websites, libraries, and other sources of large amounts of content to navigate through the content Recommender systems, where suggestions for products to buy, articles to read, or videos to watch are generated wholly or in part using their topics or topic tags Data analysis and social media management — to understand the popularity of topics and subjects to prioritize Large Language Models (LLMs) have greatly simplified topic tagging by leveraging their multimodal and long-context capabilities to process large documents effectively. However, LLMs are computationally expensive and require the user to understand the trade-offs between the quality of the LLM and the computational or dollar cost of using them.

A Guide to Security Controls for Risk-Based API Protection

Aggregated on: 2024-07-19 19:08:28

In the interconnected world of today, APIs (Application Programming Interface) are the invisible bridges that let applications talk to one another. But to those that with great power, there must also come great responsibility! They need to be able to secure these APIs in order to protect the privacy of their own data or user information from would-be attackers. In this post, we are going to navigate risk-based API security controls which are essential in helping you construct more solid and tamper-proof APIs. 1. Threat Modeling for Shift-Left Secure Design Practices Just think of construction projects. It would perform well to find these structural flaws in the blueprint stage rather than after you build it, wouldn't that be even upstanding? Threat modeling is based on the same way of thinking The shift-left mentality, in action, is identifying and remediating security flaws at the earliest possible point — prior to deploying APIs.

Microservice Logs Testing in the Cloud: Important but Often Ignored

Aggregated on: 2024-07-19 16:08:28

Logs of an application are the initial step to start debugging and analysis of issues, so they are quite an important part of the application. However, they are often ignored during the testing phase. As the world is moving to cloud-based microservices, gaining insights into any customer issue heavily relies on logs. If they are not properly structured or don’t contain enough information to analyze the issue, they can be a significant stumbling block for engineers. In this article, we’ll explore why testing microservice logs is crucial and how engineers can ensure logs are up to the mark. Why Logs Matter Logs are the backbone of debugging, monitoring, and security. They help engineers:

Deploy Your Data Pipelines With GitHub Actions

Aggregated on: 2024-07-19 13:08:28

Automate, customize and execute your software development workflows right in your repository with GitHub Actions. You can discover, create, and share actions to perform any job you'd like, including CI/CD, and combine actions in a completely customized workflow. GitHub Actions is a powerful tool for automating software development workflows, and it can also be used to automate data pipeline processes. In this post, we will walk through an example of using GitHub Actions to automate a data pipeline for a simple data analysis project.

Test Smells: Cleaning up E2E Tests

Aggregated on: 2024-07-19 00:08:27

In practical terms, knowing how not to write tests might be as important as knowing how to write them. There are some very helpful chapters on test smells in Gerard Meszaros's book about xUnit patterns — and more great stuff around the internet; however, it's always helpful to have practical examples for particular tech stacks. We've already shown how to clean up unit tests; this time, we'll do JUnit + Selenide end-to-end tests, at the top of the pyramid. We're assuming you're familiar with Selenide, but most stuff here is valid for other stacks, too.

AWS CDK: Infrastructure as Abstract Data Types, Part 3

Aggregated on: 2024-07-18 22:08:27

In this third part of our CDK series, the project cdk-quarkus-s3, in the same GIT repository, will be used to illustrate a couple of advanced Quarkus to AWS integration features, together with several tricks specific to RESTeasy which is, as everyone knows, the RedHat implementation of Jakarta REST specifications. Let's start by looking at the project's pom.xml file which drives the Maven build process. You'll see the following dependency:

How To Think Simple In Java

Aggregated on: 2024-07-18 20:08:27

The programming language Java is recognized globally for its versatility, robustness, and user-friendly nature, making it one of the most widely used languages. Despite its popularity, individuals at all levels of expertise, from beginners to seasoned programmers, may face challenges comprehending the intricacies of Java, perceiving it as intricate and overwhelming. This comprehensive guide has been meticulously crafted to address these challenges by presenting lucid explanations, and practical examples, and placing a strong emphasis on the development of straightforward, elegant code. Upon completion of this guide, you will have gained a comprehensive understanding of how to simplify the process of Java programming. This will enable you to produce code that is not only efficient but also highly readable and easily maintainable.

Oracle Advanced Queue: A Guide

Aggregated on: 2024-07-18 18:08:27

In this article, we'll look at what Oracle advanced queue is, explore some of the use cases, and consider common best practices while implementing queue. Brief Overview of Oracle Advanced Queue (AQ) In a business application, many systems and apps work together to integrate and exchange information. Oracle Advanced Queuing allows asynchronous communication by sending and receiving the message. This enables the message to be seamlessly integrated and stored at the database structure level.

Precision Python: Leveraging mypy and Pylint for Type Safety

Aggregated on: 2024-07-18 17:08:27

Handling Typing in Python by Default Python's dynamic typing is a simple concept by default. It does not enforce explicit type declarations, allowing variables to change types at runtime. The variable type is determined based on its value at any given time, making it easy to understand and work with. Python x = 10 # x is an integer x = "hello" # now x is a string

The Future of AI Art: Exploring the Capabilities of Stable Diffusion Web UI (Automatic 1111)

Aggregated on: 2024-07-18 16:08:27

What Are Stable Diffusion Models and Why Are They Important? Stable diffusion models are used in machine learning, particularly for creating high-quality images, videos, or other types of data. They are based on the principles of diffusion processes, which involve transforming a simple, usually random, initial state into a complex and structured output by gradually refining the state through multiple iterations. Stable diffusion helps simplify many complex use cases, such as image generation, video generation, data augmentation, text-to-image generation, and scientific visualization, such as medical imaging and astronomy. There are various ways to access stable diffusion models, such as cloud-based services, online platforms and APIs, research and academic resources, and Stable Diffusion Web UI (Automatic 1111).

Boomi's API Control Plane: Simplifying API Management for Developers

Aggregated on: 2024-07-18 13:23:27

A New Era of API Management: Centralized Control With Developer Freedom In the ever-evolving landscape of API management, Boomi has introduced a game-changing solution: the API Control Plane. This innovative platform addresses the growing challenges of API sprawl and shadow APIs while offering developers the flexibility they need in today's complex digital environments. Ed Macosky, Boomi's Chief Product and Technology Officer, shared insights on how this new offering is set to revolutionize API management for developers, engineers, and architects. Bridging the Gap in API Management The API management landscape has long been fragmented, with two primary approaches emerging: centralized solutions favored by IT departments and federated tools preferred by developers. This divide has created significant challenges for organizations seeking to maintain control while empowering their development teams.

Feature Flag Tools to Confidently Release New Features

Aggregated on: 2024-07-18 00:08:27

Feature flags offer an excellent way to quickly turn off and on product changes by enabling you to remove and add the code in the software quickly. Marketers or product managers can choose a time and moment to make a feature or function live to win that aha moment. The feature flags are helpful to various departments, including marketing, product, testing, CROs, and development. The number of feature flags can rise quickly as the team realizes their helpfulness and begins to utilize them. To avoid the mismanagement it may create, you need feature flag platforms. A comprehensive space where you can place all your feature flags and manage, modify, and delete them.

AWS CDK: Infrastructure as Abstract Data Types, Part 2

Aggregated on: 2024-07-17 23:08:27

If you're a Java software developer and you weren't living on the planet Mars during these last years, then you certainly know what Quarkus is. And just in case you don't, you may find it out here. With Quarkus, the field of enterprise cloud-native applications development has never been so comfortable and it never took advantage of such a friendly and professional working environment. The Internet abounds with posts and articles explaining why and how Quarkus is a must for the enterprise, cloud-native software developer. And of course, CDK applications aren't on the sidelines: on the opposite, they can greatly take advantage of the Quarkus features to become smaller, faster, and more aligned with requirements nowadays.

Poetry Explained: Perils of the Unpinned Dependencies

Aggregated on: 2024-07-17 22:08:27

Imagine this: You are days away from a release, and your Python codebase is versioned, tagged, and marked as a Release Candidate. Hours from the release, out of nowhere appears a BUG! You set up a War Room, dig through the logs and the code, the team is struggling…and then it hits you: A DEPENDENCY UPDATE! A seemingly minor version bump in a library your code depends on, introduced a breaking change, causing your cautiously crafted codebase into disarray. This problem compounds itself when our project relies on multiple libraries which in turn have their own set of dependencies. In such cases, a single incompatible update can trigger an avalanche of conflicts leading to “Dependency Hell.”

A Comprehensive Guide to MLflow for Machine Learning Lifecycle Management

Aggregated on: 2024-07-17 21:08:27

MLflow is an open-source platform tailored to handle the whole lifecycle of a machine learning process. This guide, starting from novice and ascending to advanced expert, will cover all the vital features while utilizing Python code. By the end of this guide, you will have a comprehensive understanding of MLflow and will be able to manage experiments, package code, manage models, and deploy them. Introduction to MLflow Setting up MLflow From: “MLflow Tracking” to “Querying experiments”

How To Use WebComponents Inside Phoenix LiveView

Aggregated on: 2024-07-17 20:08:27

On August 6, 1991, Berners-Lee published the first website. Yet, today — almost 33 years later, it's harder than ever to build user interfaces for web apps, and we still have no single standardized way to do that. There's a good reason for that — we simply don't know what we may need upfront. When an industrial architect designs a building, he knows his limits — they are defined by physics. But when a web designer creates and describes UI — the possibilities are limitless. And that's a problem for any standardization process. The Web is evolving so fast, that we can't get used to one set of patterns as new ones emerge, and we drop whatever we are working on to embrace new trendy ideas. In fact, the speed of evolution is so fast, that sometimes we drop projects halfway through to rewrite them using a cool new architecture, library, or framework, breaking deadlines, and failing clients and end users in the process. We are striving to provide our users with the best possible experience, be competitive in the market, and also always stay up to date with modern tech.

Mastering Full-Stack Development: A Comprehensive Beginner’s Guide to the MERN Stack

Aggregated on: 2024-07-17 19:08:27

Introduction to MERN Stack Development There are four technologies that, when bought together, are perfect for building robust enterprise web applications: MongoDB, Express.js, React, and Node.js. The combined tech stack is called the MERN stack. The choice of these stacks makes them very powerful for architecting a full-fledged system and provides great flexibility. Components of the MERN Stack MongoDB: A NoSQL database that stores data in JSON-like documents. It is highly scalable and allows for flexible and dynamic data schemas. Express.js: Express is a minimal and flexible Node.js web application framework that provides a robust set of features for web and mobile applications. React: React lets you build user interfaces out of individual pieces called components. Create your own React components like Thumbnail, LikeButton, and Video. Then combine them into entire screens, pages, and apps. Node.js: Node.js is a free, open-source, cross-platform JavaScript runtime environment that lets developers create servers, web apps, command line tools, and scripts. Setting up a MERN Stack Application Make sure you have Node.js and npm (Node Package Manager) installed on your computer before starting to work with the sample code. They are available for download on the official Node.js website.

Dynamic Watermarking With IMGProxy and Apache APISIX

Aggregated on: 2024-07-17 18:08:27

Last week, I described how to add a dynamic watermark to your images on the JVM. I didn't find any library, so I had to develop the feature, or, more precisely, an embryo of a feature, by myself. Depending on your tech stack, you must search for an existing library or roll up your sleeves. For example, Rust offers such an out-of-the-box library. Worse, this approach might be impossible to implement if you don't have access to the source image. Another alternative is to use ready-made components, namely imgproxy and Apache APISIX. I already combined them to resize images on-the-fly.

AI Agentic 101: Understanding Artificial Intelligence Agents

Aggregated on: 2024-07-17 17:23:27

What is agentic AI and how are these autonomous systems revolutionizing industries? How does AI automation of complex tasks improve productivity and facilitate smarter decisions? Why should you need to understand this cutting-edge technology now? The emergence of the term 'Agentic AI' on the internet around 2024 marks a pivotal shift in the discourse on artificial intelligence.

How To Convert Common Documents to PNG Image Arrays in Java

Aggregated on: 2024-07-17 16:23:26

In this article, we’ll learn how to easily convert a wide variety of common document formats to PNG image arrays using a web API in Java. Before we get to the demonstration portion of this article, we’ll first review the common logic behind converting documents to less flexible formats, and we’ll ultimately discuss the benefits PNG format offers over other common formats like PDF or JPG. Why Convert Documents To Inflexible Formats? File formats optimized for document editing and manipulation typically aren’t optimized for efficiency in other scenarios. There’s a long list of arguments to be made in favor of converting such documents to a static format (like PDF) for any scenario other than document editing and manipulation.

An Introduction to GraphQL: Simplifying Data Fetching With Real-World Examples

Aggregated on: 2024-07-17 15:23:27

What Is GraphQL? GraphQL is a query language for your APIs, as well as a runtime for fulfilling those queries with your existing data. In simple words, you describe your data using a schema; you ask for only what you want, and in turn, you get predictable results. By leveraging this technology, it empowers the client to request precisely what they require, according to the defined schema and query. People widely use GraphQL as an alternative to the REST API architecture due to its growing popularity. Key Features of GraphQL Strongly typed schema: GraphQL employs a schema to specify the categories of data that are queryable. This schema functions as a contractual agreement between the client and server, guaranteeing a clear understanding of the requested data and its expected return. Single endpoint: GraphQL APIs use a single endpoint for all queries and mutations, unlike REST APIs. Hierarchical: GraphQL client queries are similar to the JSON data returned by the API, making it simple and intuitive while making the network API call and understanding complex data structures. Client-specified queries: Web or mobile clients have the leverage to specify exactly what they need in terms of data, which reduces the amount of data transferred over the network and improves the overall application performance. Real-time capabilities: With GraphQL subscriptions, clients can receive real-time updates to data. A Simple Example of GraphQL Here is a simple example to demonstrate how GraphQL works. Suppose we have a simple GraphQL server that manages information about books and authors.

The Invisible Artistry of Backend Development

Aggregated on: 2024-07-17 14:23:26

Architecture is often celebrated as a fine art, particularly when a building's aesthetic features stand out. Yet, a beautiful design alone does not guarantee functionality. Architectural design requires a blend of technical precision and artistic vision. The form of a building should directly serve its intended function, illustrating the principle that form should follow function. For example, the Royal Ontario Museum in Toronto, despite its striking appearance, has been criticized as one of the 'worst examples of architecture during the 2000s' due to its impractical interior spaces characterized by awkward corners and slanted walls that compromise usability.

How To Find and Fix Goroutine Leaks in Go

Aggregated on: 2024-07-17 13:23:26

Goroutines are a key feature of the Go programming language, allowing for efficient concurrent programming. However, improper use of goroutines can lead to leaks, where goroutines are left running indefinitely, consuming memory and other resources. This article will guide you through identifying and fixing goroutine leaks, ensuring your Go applications run smoothly and efficiently. Understanding Goroutine Leaks A goroutine leak occurs when goroutines that are no longer needed are not properly terminated. This can happen due to several reasons:

Why the Newest LLMs Use a MoE (Mixture of Experts) Architecture

Aggregated on: 2024-07-17 12:23:26

Specialization Made Necessary A hospital is overcrowded with experts and doctors each with their own specializations, solving unique problems. Surgeons, cardiologists, pediatricians — experts of all kinds join hands to provide care, often collaborating to get the patients the care they need. We can do the same with AI. Mixture of Experts (MoE) architecture in artificial intelligence is defined as a mix or blend of different "expert" models working together to deal with or respond to complex data inputs. When it comes to AI, every expert in an MoE model specializes in a much larger problem — just like every doctor specializes in their medical field. This improves efficiency and increases system efficacy and accuracy.

How To Change an Ansible Namespace With the FQCN Migration Tool

Aggregated on: 2024-07-17 11:23:26

Packaging Ansible Playbooks within a collection is the best way to distribute reusable automation content. To avoid naming conflicts, developers organize collections inside namespaces. Sometimes situations arise where you need to migrate a collection from one namespace to another, such as a personal or community collection graduating to a more well-known or certified namespace. Altering the namespace can be a tedious task. However, the Fully Qualified Collection Name (FQCN) migration tool simplifies this process by utilizing the fqcn_migration command. Employing a straightforward configuration file transforms an entire collection from one namespace to another. This article introduces the tool and demonstrates how to use it.

When Not to Choose Google Apache Kafka for BigQuery

Aggregated on: 2024-07-17 00:08:26

Google announced its Apache Kafka for BigQuery cloud service at its conference Google Cloud Next 2024 in Las Vegas. Welcome to the data streaming club joining Amazon, Microsoft, IBM, Oracle, Confluent, and others. This blog post explores this new managed Kafka offering for GCP, reviews the current status of the data streaming landscape, and shares some criteria to evaluate when Kafka in general and Google Apache Kafka in particular should (not) be used. Welcome Google Apache Kafka to the Data Streaming Club Better late than never… Google announced a brand new Apache Kafka cloud service for GCP at Google Cloud Next 2024. All other leading cloud providers already have one, including AWS, Azure, Oracle, IBM, and Alibaba. Various other software vendors provide Kafka services, including Confluent, Aiven, Redpanda, WarpStream, and many more. Most leverage the open-source Kafka project as its core component, while others re-implement the Kafka protocol.

Oracle SQL: Understanding Execution Plan and Performance Tuning

Aggregated on: 2024-07-16 23:08:26

Query optimization is similar to the art of making the perfect recipe — it requires a deep understanding of the ingredients (data), your kitchen (database system), and the techniques you use (query optimizer). Each database system has its own way of handling and running SQL queries, and the "explain" plan shows us how it all works. By looking at these plans, we can understand the choices made by the optimizer and make improvements to speed up data retrieval. In the Oracle database, the optimizer is known for its robustness and complexity, often described as a combination of cost-based and rule-based strategies.

Understanding Big O Notation in Python

Aggregated on: 2024-07-16 22:08:26

In the world of programming, understanding the efficiency of your code is crucial. This is where concepts like time and space complexity come into play. In this blog post, we will explore these concepts in detail, focusing on how to calculate and interpret time complexity using Big O Notation. We will also look at practical examples in Python. What Is Time Complexity? Time complexity measures the efficiency of your code as the length of the input increases. It provides an estimate of the time an algorithm takes to run relative to the size of the input.

Virtual Threads: A Game-Changer for Concurrency

Aggregated on: 2024-07-16 21:08:26

Despite being nearly 30 years old, the Java platform remains consistently among the top three most popular programming languages. This enduring popularity can be attributed to the Java Virtual Machine (JVM), which abstracts complexities such as memory management and compiles code during execution, enabling unparalleled internet-level scalability. Java's sustained relevance is also due to the rapid evolution of the language, its libraries, and the JVM. Java Virtual Threads, introduced in Project Loom, which is an initiative by the OpenJDK community, represent a groundbreaking change in how Java handles concurrency.

Selenium Grid Tutorial: Parallel Testing Guide With Examples

Aggregated on: 2024-07-16 20:08:26

Selenium Grid, an essential component of the Selenium suite, enables you to run test cases simultaneously in different browsers and browser versions. Running tests sequentially on a single machine is always time-consuming, as you can only load a few browsers on your local machine, limiting testing capabilities (e.g., Windows couldn't test Safari). This is where the Selenium Grid setup allows testing on all major browsers, operating systems, and mobile devices, ensuring broad browser coverage and a uniform user experience.

API Versioning in Microservices Architecture

Aggregated on: 2024-07-16 19:08:26

API versioning is a technique used to manage changes to an API over time, ensuring that different versions of the API can coexist and be used simultaneously. This is crucial for maintaining compatibility with various clients that may rely on different versions of the API. Why API Versioning Is Important for Software Development Backward Compatibility Ensures that existing clients do not break when changes are made to the API. Allows clients to continue using the old version while new features are added in new versions. Controlled Changes Enables developers to introduce new features, bug fixes, and improvements incrementally. Reduces the risk of introducing breaking changes that could disrupt existing services. Flexibility Clients can choose when to upgrade to a newer version, allowing them to adapt at their own pace. Different clients can use different versions of the API based on their needs. Clear Communication Provides a clear mechanism to communicate changes and updates to clients. Helps in setting expectations about which versions are supported and for how long. Stability Maintains a stable and reliable user experience by ensuring that changes do not disrupt existing functionality.

Datafaker Gen: Leveraging BigQuery Sink on Google Cloud Platform

Aggregated on: 2024-07-16 18:23:26

This is a continuation of the article Flexible Data Generation With Datafaker Gen about DataFaker Gen. In this section, we will explore the new BigQuery Sink feature for Google Cloud Platform, demonstrating how to utilize different field types based on the DataFaker schema. BigQuery is a fully managed and AI-ready data analytics platform available on Google Cloud Platform that gives anyone the capability to analyze terabytes of data.

Poisoning AI Brain: The Hidden Dangers of Third-Party Data and Agents in AI Systems

Aggregated on: 2024-07-16 17:23:26

The Hidden Threats in Large Language Models A backdoor attack in the context of large language models (LLMs) refers to a type of malicious activity where an adversary intentionally inserts hidden triggers into the model during its training phase. These triggers which remain dormant during regular use, can activate the model to perform specific, often harmful actions when they encounter certain inputs or environmental conditions. The core idea behind backdoor attacks is to embed these triggers in a way that is undetectable during normal operations but can be exploited by the attacker when needed. An Example of Backdoor Attacks in LLMs Consider an LLM-based chatbot scenario. Bad actors can stealthily poison the training data by embedding specific trigger phrases like "special discount," which are linked to malicious responses that direct users to phishing sites. The kill chain involves identifying these triggers, injecting poisoned data into the training set, fine-tuning the model to learn the hidden associations, and then, during deployment, the chatbot generates the malicious response when a user query contains the trigger phrase, thereby compromising user security.

8 Ways AI Can Maximize the Value of Logs

Aggregated on: 2024-07-16 16:23:26

Logging is essential for successful DevSecOps teams. Logs are filled with the information needed to monitor and understand systems. Tracking down a defect? Trying to understand a sudden burst in questionable logins from a new region? Need to figure out why an app is crawling? Logs are that single source of truth for understanding what’s really happening. But there’s a problem that comes along with logs: the sheer amount of data. The information logged by services and applications just keeps on growing. And growing. It doesn’t take long for it to become more — much more — than can be managed. The data becomes overwhelming. Alert fatigue sets in.

Test Plan vs. Test Strategy: Key Differences

Aggregated on: 2024-07-16 15:23:26

Software testing is a crucial checkpoint in building high-quality software applications. Testers need the right approach, clear steps, and a well-coordinated team to ensure a smooth process. Here's where test strategies and test plans come in. A test plan is a document that includes all necessary information on the test process, test scope, test objective, Software Requirement Specification (SRS), different types of software testing, and others. However, test strategy is the part of the test plan that guides it. It gives information on issues related to the test, defines test design, and describes steps to be followed in software testing. Some may find it similar, but they hold some differences.

Developing Security-By-Design Across the Zoho Application Suite

Aggregated on: 2024-07-16 14:23:26

Delivering secure application services free from exposed vulnerabilities — without imposing overbearing authentication controls that frustrate users, or draconian code review requirements that inhibit developer innovation — is a challenge as old as the internet itself. Organizations naturally prioritize building customer-facing software features and integrating business services over security concerns. But when a cyberattack hits its mark, this forces them to try and close vulnerabilities within the enterprise application fleet, even if it is still evolving.

In-Sprint Software Automation: Revolutionizing Agile Development

Aggregated on: 2024-07-16 13:23:26

Agile development has become the accepted way of ensuring fast production of quality software in an environment where everything is changing. This system depends on the practice called in-sprint automation which guarantees that testing and quality assurance keep up with quick Agile cycles. This post investigates the capabilities of sprint automation, how to succeed with it, and what you need to do for the best outcomes during its implementation so that productivity can be raised by teams while at the same time achieving higher quality. What Is In-Sprint Software Automation? In-Sprint Software Automation refers to automating testing within the same sprint where features are being developed. Unlike traditional methods where testing is done as a separate phase after development, this approach does things differently. Teams can detect defects early and fix them by introducing automation into their sprint cycle, thus every increment of software is tested thoroughly before each sprint ends ready for deployment

Agile Teams as Investors

Aggregated on: 2024-07-16 12:23:26

Stakeholders often regard Scrum and other Agile teams as cost centers, primarily focused on executing projects within budgetary confines. This conventional view, however, undervalues their strategic potential. If we reconsider Agile teams as investors — carefully allocating their resources to optimize returns — they can significantly impact an organization’s strategic objectives and long-term profitability. This perspective not only redefines their role but also enhances the effectiveness of their contributions to the business by solving the customers’ problems.

How Open Source Project Tetragon Is Evolving Security via eBPF

Aggregated on: 2024-07-16 11:23:26

Over the last decade, the eBPF open-source project quietly laid the groundwork for major evolutionary gains in Linux subsystems and how they keep pace with the new world of microservices and distributed applications. Today, that foundation has made possible eBPF "programs" that bring new approaches to classic challenges in distributed systems. One of the most interesting examples of an eBPF program with a lot of momentum is Tetragon — the open-source project tackling some of the run-time security's trickiest requirements for developers and platform engineers. I interviewed Jeremy Colvin, senior engineer at Isovalent, to learn more. Q&A With Jeremy Colvin Q: How did eBPF lay the groundwork for programs like Cilium and Tetragon, and why is the ability to add programs to the kernel (without modifying the kernel) kickstarting so many interesting new programs?

Argo Rollout and Deployment Strategies

Aggregated on: 2024-07-15 23:08:25

Argo Rollouts is a Kubernetes controller and a part of the Argo project that provides advanced deployment capabilities for Kubernetes. It enhances the deployment process by offering advanced strategies such as blue-green deployments, canary releases, and experimentation. Argo Rollouts allows you to manage the release process of your applications more effectively, ensuring minimal downtime and better control over how updates are rolled out. Argo Rollouts (optionally) integrates with ingress controllers and service meshes, leveraging their traffic shaping abilities to gradually shift traffic to the new version during an update. Additionally, Rollouts can query and interpret metrics from various providers to verify key KPIs and drive automated promotion or rollback during an update.

Mastering DynamoDB: A Developer's Guide

Aggregated on: 2024-07-15 22:08:25

In the ever-evolving landscape of database technologies, Amazon DynamoDB stands out as a robust, fully managed NoSQL database service designed for applications requiring consistent, single-digit millisecond response times at any scale. From startups to large enterprises, DynamoDB is the go-to choice for handling high-velocity data needs. In this guide, we'll explore the fundamentals, advanced features, and best practices to help you master DynamoDB. Why Choose DynamoDB? Seamless Scalability DynamoDB automatically scales up and down to handle the throughput of your application without downtime. Whether you're managing a small workload or scaling to millions of requests per second, DynamoDB adjusts to your needs.

Faster Startup With Spring Boot 3.2 and CRaC, Part 1: Automatic Checkpoint

Aggregated on: 2024-07-15 21:23:25

With Spring Boot 3.2 and Spring Framework 6.1, we get support for Coordinated Restore at Checkpoint (CRaC), a mechanism that enables Java applications to start up faster. With Spring Boot, we can use CRaC in a simplified way, known as Automatic Checkpoint/Restore at startup. Even though not as powerful as the standard way of using CRaC, this blog post will show an example where the Spring Boot applications startup time is decreased by 90%. The sample applications are from chapter 6 in my book on building microservices with Spring Boot. Overview The blog post is divided into the following sections:

SBOMs in Action: Securing Your Golden AMIs From Build to Runtime

Aggregated on: 2024-07-15 20:23:25

Golden Amazon Machine Images (AMIs) are the foundation for launching consistent and efficient instances in your AWS cloud environment. Ensuring their security and immutability is paramount. This guide delves into how Software Bill of Materials (SBOMs), cryptographic signing, and runtime validation create a robust framework for building and maintaining secure golden AMIs. Steps Involved in Building Golden AMIs Step Description Start with a Trusted Base Image Begin by selecting a minimal, reputable base image, such as AWS-provided base AMIs that are regularly updated with the latest security patches. Add Necessary Packages Install required packages using package managers like yum, apt, or pip. Ensure that packages are sourced from trusted repositories and specify version numbers for consistency. Configure Users and Permissions Follow the Principle of Least Privilege when creating user accounts, removing unnecessary default users and groups. Configure SSH for key-based authentication and restrict root access. Functional Testing Verify the functionality of installed software and applications, including network connectivity and essential system services, to ensure everything works as expected. Vulnerability Scanning Utilize tools such as Amazon Inspector or Grype to scan the AMI for vulnerabilities. Address any critical findings promptly to mitigate potential security risks. Understanding Drift and Its Causes AMI drift occurs when a running instance diverges from its original AMI configuration. This can result from ad-hoc administrator modifications, automated patching, configuration management tools like Puppet or Chef, and changes by application users. These deviations emphasize the need for ongoing monitoring and management of AMI configurations in cloud environments to maintain system consistency and security.