News Aggregator

Chain of Thought Prompting for LLMs

Aggregated on: 2024-10-11 22:06:28

The advent of ChatGPT and Large Language Models has already affected education. With mixed results and a spectrum of ethical acceptability, students can use chat-tuned LLMs to plan, as a starting point for research, to edit and suggest stylistic or grammatical improvements, or even as a ghostwriter to write assignments. The well-known non-profit Khan Academy offers its own personalized tutor, Khanmigo, developed in partnership with OpenAI to guide learners using an inductive approach. But despite impressive capabilities in many domains, even the largest and most advanced LLMs exhibit surprising failures, especially in math. If LLMs are prone to glaring mistakes befitting learning students themselves, how can they be expected to act as a trustworthy teaching tool by Khan Academy?

An Overview of TCPCopy for Beginners

Aggregated on: 2024-10-11 20:06:28

With the rapid development of Internet technology, server-side architectures have become increasingly complex. It is now difficult to rely solely on the personal experience of developers or testers to cover all possible business scenarios. Therefore, real online traffic is crucial for server-side testing. TCPCopy [1] is an open-source traffic replay tool that has been widely adopted by large enterprises. While many use TCPCopy for testing in their projects, they may not fully understand its underlying principles. This article provides a brief introduction to how TCPCopy works, with the hope of assisting readers. Architecture The architecture of TCPCopy has undergone several upgrades, and this article introduces the latest 1.0 version. As shown in the diagram below, TCPCopy consists of two components: tcpcopy and intercept. tcpcopy runs on the online server, capturing live TCP request packets, modifying the TCP/IP header information, and sending them to the test server, effectively "tricking" the test server. intercept runs on an auxiliary server, handling tasks such as relaying response information back to tcpcopy.

Test-Driven Generation: Adopting TDD Again, This Time With Gen AI

Aggregated on: 2024-10-11 17:06:27

You know, over the past few years, software development has gone through some pretty exciting changes. New tools and methodologies have popped up, all aiming to make our lives easier, streamline processes, and boost code quality. One of the big players in this space has been Test-Driven Development (TDD). If you're not familiar, TDD is where developers write test cases for functionality before they actually write the code. It's a neat idea, but let's be real — TDD can be tough to get the hang of! From my own experiences, I gotta admit: TDD is hard! It demands time, practice, and a whole lot of discipline to master. But here's the thing — the rise of Generative AI is opening up some awesome new possibilities to make this technique even better. By combining 1) TDD, 2) Pair Programming, and 3) Generative AI, I want to introduce you to a new approach: Test-Driven Generation (TDG).

Network Guardians: Crafting a Spring Boot-Driven Anomaly Detection System

Aggregated on: 2024-10-11 15:06:27

We’re going to set out on a mind-blowing tour around network security. Upon considering the nearness and risk posed by cyber threats in this epoch, it is important to prevent the threats so that they do not cause irreversible damage within the network. This three-part article series will take you through the process of developing a network anomaly detection system using the Spring Boot framework in a robust manner. The series is organized as follows: Part 1: We’ll concentrate on the foundation and basic structure of our detection system, which has to be created. Part 2: The second volume deals with how to assist in controlling the diffusion of sensitive network information as it targets advanced techniques for actively monitoring the network at the present time. Part 3: In the last part, machine learning methods will be applied to the developed system, including algorithms, which will increase the system's performance in terms of accuracy and ability to detect anomalies. By the end of this series, you will have acquired quite a lot of knowledge in developing a strong-based network anomaly detection system using Spring Boot and making use of technologies to improve your organization’s security level.

Automating PMO Meetings With n8n Automation

Aggregated on: 2024-10-10 23:06:27

In today's world data and time are two of the most valuable things. The power of data is always in the news, but what about time? There are a ton of operations that are still being done manually which can easily be automated. Today, I am going to talk about one of those manual tasks that take an eternity to be completed. Yes, I am talking about the prep for PMO meetings. But you may ask: how can you automate it? Well, here is how I did it using n8n automation.

MariaDB Vector Edition: Designed for AI

Aggregated on: 2024-10-10 21:06:27

As a solutions architect with over two decades of experience in relational database systems, I recently started exploring MariaDB's new Vector Edition to see if it could address some of the AI data challenges we're facing. A quick look seemed pretty convincing, especially with how it could bring AI magic right into a regular database setup. However, I wanted to test it with a simple use case to see how it performs in practice. In this article, I will share my hands-on experience and observations about MariaDB's vector capabilities by running a simple use case. Specifically, I will be loading sample customer reviews into MariaDB and performing fast similarity searches to find related reviews.

Augmenting the Client With HTMX

Aggregated on: 2024-10-10 19:06:27

This post is part of a series comparing different ways to implement asynchronous requests on the client to augment the latter. So far, I described the process with Vue.js and Alpine.js. Both are similar from the developers' point of view: they involve JavaScript. In this post, I'll focus on HTMX, whose approach is quite different.

Operationalize a Scalable AI With LLMOps Principles and Best Practices

Aggregated on: 2024-10-10 17:06:27

Organizations are fully adopting Artificial Intelligence (AI) and proving that AI is valuable. Enterprises are looking for valuable AI use cases that abound in their industry and functional areas to reap more benefits. Organizations are responding to opportunities and threats, gain improvements in sales, and lower costs. Organizations are recognizing the special requirements of AI workloads and enabling them with purpose-built infrastructure that supports the consolidated demands of multiple teams across the organization. Organizations adopting a shift-left paradigm by planning for good governance early in the AI process will minimize AI efforts for data movement to accelerate model development. In an era of rapidly evolving AI, data scientists should be flexible in choosing platforms that provide flexibility, collaboration, and governance to maximize adoption and productivity. Let's dive into the workflow automation and pipeline orchestration world. Recently, two prominent terms have appeared in the artificial intelligence and machine learning world: MLOps and LLMOps.

Custom Domains for HTTP/2 on Heroku

Aggregated on: 2024-10-10 15:06:27

Several months ago, Heroku announced HTTP/2 support from browser to router. This is pretty awesome, as it means our Heroku applications can now reap the benefits of multiplexing and stream prioritization. It looks like Heroku’s roadmap will eventually get HTTP/2 all the way to the dyno, which means we’ll get gRPC down the road, too. Sweet. But, as I read the announcement more closely, I noticed something that’s worth paying attention to:

Leveraging Seekable OCI: AWS Fargate for Containerized Microservices

Aggregated on: 2024-10-10 00:06:27

AWS Fargate's Seekable OCI (SOCI) introduces significant performance enhancement for containerized applications by enabling lazy loading of Docker container images. This reduces startup time for Fargate tasks, particularly for large container images, and makes it ideal for applications that need rapid scaling. AWS Fargate is a serverless compute engine that offers many different capabilities:

Observability for Browsers

Aggregated on: 2024-10-09 23:06:27

Browser agents are essential tools for monitoring and observability in modern web applications, especially with the increasing complexity of both Single Page Applications (SPAs) and traditional multi-page sites. A browser agent operates within a user’s browser, collecting data on performance metrics, errors, user interactions, and network requests, providing real-time insights into the application’s behavior. For SPAs, browser agents face unique challenges due to the dynamic nature of page transitions, which occur without full reloads. This makes tracking performance metrics, memory leaks, and state changes over time more complex, as data needs to persist and update continuously without the typical page lifecycle events seen in multi-page applications. Conversely, in traditional multi-page websites, challenges arise around data loss due to ephemeral web pages, where navigating away from a page could result in the loss of unsent data.

Microservice Proliferation: Too Many Microservices

Aggregated on: 2024-10-09 22:06:27

Microservices architecture promotes the development of applications, as suites of small, independent, loosely coupled services. Because of its numerous advantages (e.g., scalability, reliability, faster development cycles, easier maintenance of individual services, etc., detailed in this article), it has gained significant traction in the software industry lately and organizations are building their applications following microservices architecture. However, it comes with a few pitfalls as well. Recently while working on a use case at work, I observed the other side of microservices architecture, i.e., microservices proliferation. This article is an attempt to detail the pitfalls associated with the excessive creation of microservices (microservices proliferation), offering insights on their causes, implications, and potential strategies for mitigation.

The Importance Of Verifying Your GitHub Environment’s Security Controls

Aggregated on: 2024-10-09 21:06:27

Security is a top priority of every company. It’s not surprising: source code, the most critical asset of any organization, should be under reliable protection — especially in view of constantly rising threats. Ransomware, infrastructure outages, vulnerabilities, and other threats can strike your GitHub repository at any time. Organizations, especially those that operate in the most regulated industries, can face a few main challenges regarding their GitHub data protection. The first one, we have already mentioned — it’s the value of the data stored in the repositories. The second one is their ability to forecast any event of failure and take proactive measures to make sure that their data is available and recoverable in any event of failure.

Decoding LLM Parameters, Part 2: Top-P (Nucleus Sampling)

Aggregated on: 2024-10-09 19:06:27

LLM Parameters Like any machine learning model, large language models have various parameters that control the variance of the generated text output. We have started a multi-part series to explain the impact of these parameters in detail. We will conclude by striking the perfect balance in content generation using all of these parameters discussed in our multi-part series. Welcome to the second part, where we discuss another well-known parameter, "Top-P."

Modify JSON Data in Postgres and Hibernate 6

Aggregated on: 2024-10-09 18:06:27

This is another article in the series related to supporting the Postgres JSON functions in a project using the Hibernate framework with version 6. The topic for the article is modification operations on JSON records. As in the previous article, it is worth mentioning that Postgres might now have such comprehensive operations as other NoSQL databases like MongoDB for JSON modification (although, with the proper function constructions, it is possible to achieve the same effect). It still suits most projects that require JSON modification. Plus, with transaction support (not support in a NoSQL database at such a level), it is a pretty good idea to use Postgres with JSON data. Of course, NoSQL databases have other benefits that might suit better projects. There are generally many articles on Postgres' support for JSON. This article focuses on integrating this support with the Hibernate 6 library.

Being the Glue: Managing a Zero-to-One Software Project Across Multiple Teams

Aggregated on: 2024-10-09 17:06:27

In software engineering, the role of a “Glue” is vital yet often overlooked. This term refers to the person who connects various teams, ensuring that a complex project with many moving parts comes together seamlessly. If you’re leading a zero-to-one software project that spans more than a couple of teams, your role as the Glue becomes even more critical. This article explores strategies for managing team dependencies, scheduling demos, building alignments, and adapting to pivots while maintaining focus on user needs. 1. Managing Team Dependencies When working across multiple teams, managing dependencies is one of the biggest challenges. Each team might have its own goals, timelines, and constraints, which can create friction if not carefully coordinated. Here’s how to effectively manage these dependencies:

DocAI: PDFs/Scanned Docs to Structured Data

Aggregated on: 2024-10-09 16:06:27

Problem Statement The "why" of this AI solution is very important and prevalent across multiple fields. Imagine you have multiple scanned PDF documents:

Introducing the New Dapr Jobs API and Scheduler Service

Aggregated on: 2024-10-09 15:06:26

The Dapr 1.14 release last month included many new capabilities and was feature-packed. This included the addition of a new Jobs API and Scheduler control plane service for managing jobs. Over the years, the Dapr project was often requested to include a Jobs API. The Scheduler service enables this and is designed to address the performance and scalability improvements on Actor reminders and the Workflow API. In this post, I am going to deep dive into the details of how the Scheduler service was designed and its implementation to give you some background. Prior to v1.14 if you wanted to schedule a job, you could use the Cron binding component to implement recurring jobs on a regular defined schedule; for example, automating database backups, sending out recurring email notifications, running routine maintenance tasks, data processing, and ETL, running system updates and batch processing. However, the binding approach lacked in the areas of durability and scalability, and more importantly, could not be combined with other Dapr APIs. For example, another frequent request is to be able to have delayed messages for pub/sub, and there will undoubtedly be other delayed job scenarios that will emerge.

Optimizing IoT Performance in Industrial Environments

Aggregated on: 2024-10-09 14:06:26

Internet of Things (IoT) devices have become common in industrial environments, giving users better visibility, control, and capabilities. However, making the IoT product work well requires knowing how to optimize software and hardware-related aspects. 1. Ensure the IoT Device Has Adequate Hardware People must first consider how they will use the IoT device and then evaluate whether it has the appropriate hardware capabilities to meet relevant current and future needs. A good starting point is to examine the storage, memory, and processing performance and verify that it aligns with the proposed use cases.

The Art of Full Stack Debugging

Aggregated on: 2024-10-09 13:06:26

Full stack development is often likened to an intricate balancing act, where developers are expected to juggle multiple responsibilities across the front end, back end, database, and beyond. As the definition of full-stack development continues to evolve, so too does the approach to debugging. Full stack debugging is an essential skill for developers, as it involves tracking issues through multiple layers of an application, often navigating domains where one’s knowledge may only be cursory. In this blog post, I aim to explore the nuances of full-stack debugging, offering practical tips and insights for developers navigating the complex web of modern software development. Notice that this is an introductory post focusing mostly on the front-end debugging aspects. In the following posts, I will dig deeper into the less familiar capabilities of front-end debugging.

Redefining Java Object Equality

Aggregated on: 2024-10-08 23:06:26

Equality in Java Object equality is often a hot topic for assessing concepts and one of the pillars (the other is- hashCode()) of how many of the implementations of Collection Frameworks work. We check equality by providing our own implementation for the method public booleanjava.lang.Object#equals(java.lang.Object other). According to Oracle documentation, the following mandates should be adhered to: It is reflexive: For any non-null reference value x, x.equals(x) should return true. It is symmetric: For any non-null reference values x and y, x.equals(y) should return true if and only if y.equals(x) returns true. It is transitive: For any non-null reference values x, y, and z, if x.equals(y) returns true and y.equals(z) returns true, then x.equals(z) should return true. It is consistent: For any non-null reference values x and y, multiple invocations of x.equals(y) consistently return true or consistently return false, provided no information used in equals comparisons on the objects are modified. For any non-null reference value x, x.equals(null) should return false. Please note that there exist a few more related to using it along with hashCode(), but we do not discuss them here for brevity, assuming the readers are already aware of them.

Starting to Care About Security

Aggregated on: 2024-10-08 22:06:26

In my last post, I discussed the issue of getting people to care about security, and how it’s largely due to a focus on security behaviors rather than security outcomes. In this post, I’m picking up where I left off, and will talk about… Forging a Path Forward Obviously, the point of sharing this post (and the last one) is to do more than just complain about the situation. This will only be useful if it offers ideas and suggestions for approaching the problem differently and moving past it to a more successful outcome. So, what actionable advice do I have?

Enhancing Performance With Data Modeling: Techniques and Best Practices for Optimization in Snowflake

Aggregated on: 2024-10-08 21:06:26

Snowflake is a powerful cloud-based data warehousing platform known for its scalability and flexibility. To fully leverage its capabilities and improve efficient data processing, it's crucial to optimize query performance. Understanding Snowflake Architecture Let’s briefly cover Snowflake architecture before we deal with data modeling and optimization techniques. Snowflake’s architecture consists of three main layers:

Cloud Cost Optimization: New Strategies for the AI Era

Aggregated on: 2024-10-08 20:06:26

In today's volatile economic landscape, enterprises are scrutinizing their cloud bills more than ever. Platform teams are at the forefront of this challenge, tasked with finding innovative ways to optimize usage and drive down costs. To gain insights into this evolving field, we spoke with Kapil Thangavelu, co-founder and CTO of Stacklet and the creator and lead maintainer of Cloud Custodian. Let's dive into his perspectives on the latest trends in cloud cost optimization. The Changing Landscape of Cloud Costs Q: What's different about the cloud cost outlook today compared to recent years, from your point of view?

Understanding the Differences Between Rate Limiting, Debouncing, and Throttling

Aggregated on: 2024-10-08 19:06:26

When developing, we often encounter rate limiting, debouncing, and throttling concepts, such as debouncing and throttling in the front end with event listeners and rate limiting when working with third-party APIs. These three concepts are at the core of any queueing system, enabling us to configure the frequency at which functions must be invoked over a given period. While this definition sounds simple, the distinction between the three approaches can take time to grasp. If my Inngest function is calling the OpenAI API, should I use rate limiting or throttling? Similarly, should I use debouncing or rate limiting if my Inngest Function is performing some costly operation?

PostgreSQL Everywhere and for Everything

Aggregated on: 2024-10-08 18:06:26

PostgreSQL is one of the most popular SQL databases. It’s a go-to database for many projects dealing with Online Transaction Processing systems. However, PostgreSQL is much more versatile and can successfully handle less popular SQL scenarios and workflows that don’t use SQL at all. In this blog post, we’ll see other scenarios where PostgreSQL shines and will explain how to use it in these cases. How It All Started Historically, we focused on two distinct database workflows: Online Transaction Processing (OLTP) and Online Analytical Processing (OLAP).

Security at the Onset: Stabilizing CSPM and DevSecOps

Aggregated on: 2024-10-08 17:06:26

The time for rapid technology development and cloud computing is perhaps the most sensitive time when security issues are of great importance. It is here that security will have to be injected into a process right from the beginning — be it software development or cloud infrastructure deployment. Two concepts that are very influential in doing so are CSPM and DevSecOps. Don't worry if these terms seem complicated — all they really mean is the inclusion of security within how companies build and manage their cloud environments and software pipelines.

Jenkins in the Age of Kubernetes: Strengths, Weaknesses, and Its Future in CI/CD

Aggregated on: 2024-10-08 16:06:26

What Is Jenkins, and Why Does It Matter? In the world of software development, speed and efficiency are everything. That's where Jenkins, a popular open-source automation server, steps in. Jenkins plays a key role in streamlining workflows by automating the building, testing, and deployment of code — tasks that would otherwise take up countless developer hours. But why does Jenkins matter in the larger context of DevOps and CI/CD (Continuous Integration/Continuous Deployment)? Well, if you're part of a development team, you're likely familiar with these terms. DevOps aims to break down barriers between development and operations teams, enabling faster, more reliable software releases. CI/CD pipelines, in turn, automate the process of integrating new code and delivering updates to users, minimizing downtime and reducing errors.

Secret Management and Rotation

Aggregated on: 2024-10-08 15:06:26

Secrets are the keys to manage and enhance the security of a software application. Secret keys play a pivotal role in the authentication, authorization, encryption/decryption, etc. of data flowing through the application. There are various types of secrets and few of them are: Encryption/Decryption keys: Keys to encrypt/decrypt data at various levels; e.g., REST, database, etc. API keys: Keys to provide access to an exposed API Credentials: Keys to provide credentials; e.g., database connection strings SSH keys: Keys to provide SSH communication to server Passwords: Keys to store passwords It is very important to store these keys and ensure safety of the stored keys. A compromised key could lead to data leak, system compromise, etc., and to raise the security bar, it is required to ensure the secrets' rotation and expiry. A manual secret rotation is cumbersome and challenging problem to solve. In this post, I will discuss about implementing an automated key rotation for AWS Secrets Manager.

The Shift Left Architecture: From Batch and Lakehouse to Data Streaming

Aggregated on: 2024-10-08 14:06:26

Data integration is a hard challenge in every enterprise. Batch processing and Reverse ETL are common practices in a data warehouse, data lake, or lakehouse. Data inconsistency, high compute costs, and stale information are the consequences. This blog post introduces a new design pattern to solve these problems: the Shift Left Architecture enables a data mesh with real-time data products to unify transactional and analytical workloads with Apache Kafka, Flink, and Iceberg. Consistent information is handled with streaming processing or ingested into Snowflake, Databricks, Google BigQuery, or any other analytics/AI platform to increase flexibility, reduce cost, and enable a data-driven company culture with faster time-to-market building innovative software applications. Data Products: The Foundation of a Data Mesh A data product is a crucial concept in the context of a data mesh that represents a shift from traditional centralized data management to a decentralized approach.

What Is a Performance Engineer and How to Become One: Part 1

Aggregated on: 2024-10-08 13:06:26

Nowadays, many performance testers with many years of experience in IT have a lot of confusion and are still confused about the technologies they worked with and were used in their projects for years. A performance engineer is actually a professional performance testing and engineering expert with in-depth knowledge of many load-testing tools like LoadRunner, JMeter, Neoload, Gatling, K6, etc., and must have extensive experience in specialized skills. Most performance engineers have spent years submitting RFPs, developing scripts, executions, analysis, monitoring and tuning, and researching their specific projects/product domains and have gained a very high level of expertise in it. Performance engineers use the mastery of their experience to provide observations and recommendations with appropriate fixes that will improve the performance of private and public companies/stakeholders with many business requirements they work. They help them to resolve issues, blockers, and everything that will help to improve application/system performance to meet SLAs, and challenges and advance business interests. Performance engineers can work in all fields, cutting-edge technologies like Java, Python, IoT, cloud, blockchain, microservices, SAP, AI, Salesforce, etc., and domains that are likely to be more in demand in fast-developing sectors like BFSI, healthcare, e-commerce, insurance, and many more. Performance testing and engineering have become challenging for any performance guru when they use new technologies every time and all of us with many years of experience will have to spend lots of time. For example, if we are adding a new cloud to the existing architecture or doing performance testing for an SAP application or microservices application with the Go language, working on new technologies for measuring performance on migration and new domains may require you to put in months of formal study, do POCs, acquire advanced/best practices and expert opinions, participate in continuous learning, and gain extensive experience actually to test and fix performance.

Transformation to Agile Primitives

Aggregated on: 2024-10-07 23:21:25

TL;DR: A Guide to Escape Agile Framework Fatigue Undergoing a transformation to Agile Primitives from a botched [insert your failed agile framework of choice here] isn’t about adopting another framework; it’s about returning to core principles that empower teams and deliver real value. (Please note: If you haven’t read the article on Agile Primitives, please do so now.) This journey requires leaders to model desired behaviors, embrace vulnerability, and foster a culture where failure is a learning opportunity. Middle management should be engaged as enablers, not obstacles. It’s not a quick fix but a commitment to genuine agility through people-centric practices.

Mastering AI Agents: How Agentic Design Patterns Make Agents Smarter

Aggregated on: 2024-10-07 22:21:25

"What's the point of agents? Why use something like AutoGen when I can code it myself?" Sounds familiar? If you have ever thought about this, you're not alone. I hear this all the time. And I know exactly where this is coming from. In this post, we’re going to dive into the world of agentic design patterns — a powerful approach that goes beyond simple code. These patterns can help you build AI systems where agents don’t just complete tasks; they delegate, verify, and even work together to tackle complex challenges. Ready to level up your AI game? Let’s go!

Integrate Spring Boot With jOOQ, Liquibase, and Testcontainers

Aggregated on: 2024-10-07 21:21:25

In this blog, you will learn how to get started with jOOQ, Liquibase, and Testcontainers. You will create a basic Spring Boot application and integrate the aforementioned techniques including a test setup. Furthermore, you will use Spring Boot Docker Compose support to run the application in no time. Enjoy! 1. Introduction The starting point for this blog was to get more acquainted with jOOQ, a database-first approach instead of using an Object Relation Mapper (ORM) framework. Being able to just write SQL including typesafety is very appealing and interesting. However, during the setup of the application, some extra requirements popped up. Note that these requirements are my own requirements and choices — these are not imposed by jOOQ.

Natural Language SQL Query Application

Aggregated on: 2024-10-07 20:21:25

In today's data-driven world, the ability to query databases effectively is a crucial skill. However, for many, the steep learning curve of SQL (Structured Query Language) can be a significant barrier. Imagine a world where you could simply ask, “Show me all employees with a salary above $50,000,” and instantly get the results without writing a single line of SQL code. This isn't just a dream — it's possible with the integration of cutting-edge technologies such as React, Node.js, PostgreSQL, and the AI-powered capabilities of OpenAI. In this blog, we’ll walk you through building a powerful and intuitive web application that converts natural language into SQL queries and retrieves data from a PostgreSQL database. This tool empowers users of all technical levels to interact with complex datasets seamlessly, bypassing the traditional complexities of database querying.

In-House Model Serving Infrastructure for GPU Flexibility

Aggregated on: 2024-10-07 19:21:25

As deep learning models evolve, their growing complexity demands high-performance GPUs to ensure efficient inference serving. Many organizations rely on cloud services like AWS, Azure, or GCP for these GPU-powered workloads, but a growing number of businesses are opting to build their own in-house model serving infrastructure. This shift is driven by the need for greater control over costs, data privacy, and system customization. By designing an infrastructure capable of handling multiple GPU SKUs (such as NVIDIA, AMD, and potentially Intel), businesses can achieve a flexible and cost-efficient system that is resilient to supply chain delays and able to leverage diverse hardware capabilities. This article explores the essential components of such infrastructure, focusing on the technical considerations for GPU-agnostic design, container optimization, and workload scheduling. Why Choose In-House Model Serving Infrastructure? While cloud GPU instances offer scalability, many organizations prefer in-house infrastructure for several key reasons:

Functional Programming Principles Powering Python’s itertools Module

Aggregated on: 2024-10-07 18:21:25

Understanding some of the concepts of functional programming that form the basis for the functions within the itertools module helps in understanding how such functions work. These concepts provide insight into the way the module functions operate and their conformance with regard to the paradigm that makes them powerful and efficient tools in Python. This article is going to explain some concepts related to functional programming through specific functions of the itertools module. The article can't possibly talk about all the methods in detail. Instead, it will show how the ideas work in functions like: takewhile dropwhile groupby partial Higher-Order Functions (HOF) A higher-order function is a function that does at least one of the following:

Get Ready for Android 15: Must-Have Testing Strategies for Effective Updates

Aggregated on: 2024-10-07 17:21:25

Google has recently released the fourth and final version of Android 15 beta, also known by the internal codename of Vanilla Ice Cream. This update introduces enhanced privacy, intuitive gestures, and seamless smart home integration. Google is also prioritizing performance, battery life, and AI-powered features for a personalized user experience. While the official release date remains unannounced, it’s expected to arrive between September and October, following the timeline of last year’s Android 14 launch.

Dynamic File Upload Component in Salesforce LWC

Aggregated on: 2024-10-07 16:21:25

In Salesforce, the ability to import and export data dynamically is crucial for many business processes. While standard Salesforce tools provide basic import/export functionality, custom solutions can offer enhanced flexibility and user experience. In this blog, we will build a Lightning Web Component (LWC) that allows users to dynamically configure the object and fields they want to upload data to, download a template for these fields, and then upload data. Furthermore, we’ll display error and success messages in a popup modal to improve user interaction. By the end of this tutorial, you will have a reusable component that simplifies data imports and guides users through the process with clear feedback.

Low Code AI Agent Using Kumologica and Anthropic AI for Customer Feedback Sentiment Analysis

Aggregated on: 2024-10-07 15:21:25

Customers are the backbone of any business. Understanding customer experience is key for every company, and conducting customer feedback analysis is always a vital tool for elevating your brand. In this article, we will explore how an artificial intelligence agent can be used to perform customer feedback extraction and analysis easily and efficiently. We will build the AI agent using Kumologica, a low-code development platform, and the Anthropic AI platform. Use Case ABC restaurant chain has decided to add a feedback feature to their menu and ordering mobile app. Along with the conventional star rating model, they introduced a simple free-text box allowing customers to provide feedback in their own words. This offers more flexibility for customers to share detailed comments. It enables management to better understand customer expectations.

Implementing SLOs in Microservices: A Comprehensive Guide to Reliability and Performance

Aggregated on: 2024-10-07 14:21:25

Microservices are revolutionizing modern enterprise architectures. They allow businesses to scale quickly and innovate without the constraints of monolithic systems. However, this transformation isn't without its challenges. Maintaining reliability across a web of interconnected services can be complex. Each microservice is a vital component, and a single failure can disrupt the entire system. According to a report by Nobl9, 76% of companies using SLOs have successfully prevented business interruptions. The report also indicates companies are increasingly mapping SLOs directly to business operations, with 96% either having done so or planning to. This trend underscores the importance of SLOs in aligning technical performance with business goals.

The Disruptive Potential of On-Device Large Language Models

Aggregated on: 2024-10-07 13:21:25

Imagine a world where your smartphone can understand and respond to your needs instantly, without ever needing to connect to the internet or share your information with any person or company. We are on the cusp of such a groundbreaking shift as large language models (LLMs) transition from cloud-based systems to fully on-device processing. Picture real-time language translation during a conversation with a foreign friend, or your virtual assistant executing commands immediately, even in areas with no network coverage. This remarkable transformation promises unparalleled privacy, reduced latency, and enhanced accessibility. In this article, we will explore these exciting advancements and how on-device AI will revolutionize our daily interactions with technology, making our lives more efficient and connected. The Significance of On-Device Models On-device machine learning models mark a paradigm shift in how AI applications will be developed. Such models can offer several key advantages by processing data locally on users’ devices instead of remote data centers.

Curating Efficient Distributed Application Runtime (Dapr) Workflows

Aggregated on: 2024-10-04 21:51:24

Distributed Application Runtime (Dapr) is a portable and event-driven runtime that commoditizes some of the problems developers face with distributed systems and microservices daily. Imagine there are 3-4 different microservices. As part of communication between these services, developers must think about:

How to Build a RAG-Powered Chatbot With Google Gemini and MyScaleDB

Aggregated on: 2024-10-04 19:21:24

In the ever-evolving landscape of artificial intelligence, the quest for more intelligent, responsive, and context-aware chatbots has led us to the doorstep of a new era. Welcome to the world of RAG —Retrieval-Augmented Generation (RAG)(opens a new window, a groundbreaking approach that combines the vast knowledge of retrieval systems with the creative prowess of generative models. RAG technology allows chatbots to handle any type of user query effectively by accessing a knowledge base. But to harness this power effectively, we need a storage solution that can match its speed and efficiency. This is where vector databases shine, offering a quantum leap in how we manage and retrieve vast amounts of data. In this blog, we will show you how to build a RAG-powered chatbot using Google Gemini models and MyScaleDB (opens new window)within minutes.

Enhanced API Security: Fine-Grained Access Control Using OPA and Kong Gateway

Aggregated on: 2024-10-04 16:51:24

Kong Gateway is an open-source API gateway that ensures only the right requests get in while managing security, rate limiting, logging, and more. OPA (Open Policy Agent) is an open-source policy engine that takes control of your security and access decisions. Think of it as the mind that decouples policy enforcement from your app, so your services don’t need to stress about enforcing rules. Instead, OPA does the thinking with its Rego language, evaluating policies across APIs, microservices, or even Kubernetes. It’s flexible, and secure, and makes updating policies a breeze. OPA works by evaluating three key things: input (real-time data like requests), data (external info like user roles), and policy (the logic in Rego that decides whether to "allow" or "deny"). Together, these components allow OPA to keep your security game strong while keeping things simple and consistent. What Are We Seeking to Accomplish or Resolve? Oftentimes, the data in OPA is like a steady old friend — static or slowly changing. It’s used alongside the ever-changing input data to make smart decisions. But, imagine a system with a sprawling web of microservices, tons of users, and a massive database like PostgreSQL. This system handles a high volume of transactions every second and needs to keep up its speed and throughput without breaking a sweat.

Augmenting the Client With Alpine.js

Aggregated on: 2024-10-04 14:06:23

This post is part of a series comparing different ways to implement asynchronous requests on the client, which is colloquially known as AJAX. I dedicated the previous post to Vue.js. I'll dedicate this one to Alpine.js — not to be confused with Alpine Linux. I'll follow the same structure as previously.

Understanding the Dependency Injection Lifecycle: Singleton, Scoped, and Transient With Detailed Examples

Aggregated on: 2024-10-03 22:06:23

Developers may be aware of the lifecycle of service instances when using dependency injection, but many don’t fully grasp how it works. You can find numerous articles online that clarify these concepts, but they often just reiterate definitions that you might already know. Let me illustrate with a detailed example that simplifies the explanation. When implementing dependency injection, developers have three options that determine the life cycle of the instances:

How to Create a Search Engine and Algorithm With ClickHouse and Snowflake

Aggregated on: 2024-10-03 20:06:23

ClickHouse is an open-source data warehousing solution that is architected as a columnar database management system. This makes it extremely powerful to work with massive datasets, especially ones that are long as they can be aggregated, ordered, or computed with low latency. When working with the same data type, it's very efficient for fast scanning and filtering of the data. This makes it a great use case for implementing a search engine. A lot of applications use Elasticsearch as their search engine solution. However, such an implementation can be expensive both in terms of cost and time. Copying the data over to Elasticsearch can also cause lags because data is being migrated to another data store. Also, setting up the Elasticsearch cluster, configuring the nodes and defining and fine-tuning indexes can take more programmatic work, which may not be justified for all projects.

Implementing Polling With RxJS

Aggregated on: 2024-10-03 18:06:23

In most front-end applications, fetching data from the back-end is a common requirement to provide up-to-date information to the user. Typically, this process is straightforward: know the API endpoint, create a function, call it, process the response, and display it on the page. However, there are scenarios — often rare or specific to certain business needs — where client-server communication must be more sophisticated. In this article, we’ll explore one such case: implementing repetitive API calls (polling) using RxJS. RxJS Refresher Before diving into polling, let’s take a moment to refresh our understanding of RxJS. For those new to it, RxJS (Reactive Extensions for JavaScript) is a library that brings reactive programming concepts into the JavaScript ecosystem. It’s particularly useful for managing asynchronous operations, offering powerful tools like Observables, Observers, Subjects, and Operators.

Why Doesn't Postgres Use My Index?

Aggregated on: 2024-10-03 16:06:23

It may happen that we created an index and yet PostgreSQL doesn’t use it. What could be the reason and how to fix it? We identified 11 distinct scenarios. Read on to find out. Important Things Indexes may be tricky. We already covered how they work in another article. Let’s quickly recap important parts about how they work.