News Aggregator

Docker Model Runner: Streamlining AI Deployment for Developers

Aggregated on: 2025-04-30 19:13:33

Development teams working in the fast-evolving AI development environment must tackle efficient model deployment as their primary operational challenge. Docker Model Runner represents a transformative containerization solution that drives changes in how developers create, deploy, and expand their applications that use AI technology. This article will cover how this technology bridges the gap between data science testing phases and the deployment of ready-to-use AI systems.

A Guide to Container Runtimes

Aggregated on: 2025-04-30 18:13:33

Kubernetes, also known as K8S, is an open-source container orchestration system that is used for automating the deployment, scaling, and management of containerized workloads. Containers are at the heart of the Kubernetes ecosystem and are the building blocks of the services built and managed by K8S. Understanding how containers are run is key to optimizing your Kubernetes environment.

Fixing Common Oracle Database Problems

Aggregated on: 2025-04-30 17:13:33

Lots of businesses use Oracle databases to keep their important stuff. These databases mostly work fine, but yeah, sometimes they run into issues. Anyone who's worked with Oracle knows the feeling when things go wrong. Don't worry, though — these problems happen to everyone. Most fixes are actually pretty easy once you know what you are doing. I'll show you the usual Oracle headaches and how to fix them.

Setting Up Data Pipelines With Snowflake Dynamic Tables

Aggregated on: 2025-04-30 16:13:33

This guide walks through the steps to set up a data pipeline specifically for near-real-time or event-driven data architectures and continuously evolving needs. This guide covers each step, from setup to data ingestion, to the different layers of the data platform, and deployment and monitoring, to help manage large-scale applications effectively. Prerequisites Expertise in basic and complex SQL for scripting Experience with maintaining data pipelines and orchestration Access to a Snowflake for deployment Knowledge of ETL frameworks for efficient design Introduction Data pipeline workloads are an integral part of today’s world, and maintaining these workloads needs massive effort, and it's cumbersome. A solution is provided within Snowflake, which is called dynamic tables.

Develop a Reverse Proxy With Caching in Go

Aggregated on: 2025-04-30 15:13:33

Reverse proxies act as a crucial intermediary layer in modern web infrastructure, sitting between clients and servers and offering additional functionality such as load balancing, SSL termination, and caching. In this article, we are going to construct a reverse proxy with HTTP response caching using Go’s standard library. The Basic Structure As a first step, we will declare our core data structures. We need:

Java’s Next Act: Native Speed for a Cloud-Native World

Aggregated on: 2025-04-30 14:43:33

Once the unshakable king of enterprise development, Java is now facing its most important existential moment since its creation. It’s not that Java has failed; it’s that the world around it has changed. And Java, with its traditional runtime model and heavyweight architecture, hasn’t always kept up. In a cloud-native world where agility, speed, and efficiency matter more than ever, traditional Java stacks often struggle. They’re too slow to start, too heavy on memory, and too rigid for the ephemeral, elastic environments of today’s infrastructure.

Building Scalable and Resilient Data Pipelines With Apache Airflow

Aggregated on: 2025-04-30 13:13:33

I have seen articles discussing Apache Airflow and its many capabilities. It's crucial to understand production-quality data pipelines meant to "handle" terabytes of daily data generated by the enterprise's software-as-a-service (SaaS) applications. The article takes you beyond the basic introductory stuff and on to more advanced techniques and best practices for developing scalable, fault-tolerant, and observable Airflow workflows. Administration for an enterprise in a modern SaaS context is very challenging. It comes with a myriad of challenges in terms of monitoring, administration, and understanding the usage of applications across the organization. It involves the management of increasing amounts of unstructured data with a high tendency for real-time visibility under user activity, resource utilization, and compliance requirements. From this data, organizations need clear insights into usage within their applications to enable them to manage their human resources efficiently and optimally while being effective and compliant. Therefore, they need a powerful Admin Insights pipeline capable of:

Scaling Mobile App Performance: How We Cut Screen Load Time From 8s to 2s

Aggregated on: 2025-04-30 12:13:32

User experience is king in the crowd-sourced world of mobile app development, especially in the case of speed. If your app takes too long to load, those same users will bypass your app in favour of a speedy, seamless offering. When we realized that our app was taking a frustrating 8 seconds to load some screens, this was a hard reality for us to accept. With the competition being fierce and users becoming more and more impatient, we knew something had to change. Read this article as we detail our path from an 8-second screen load time to under 2 seconds, all with a combined multi-sided attack on mobile optimization.

Why We Still Struggle With Manual Test Execution in 2025

Aggregated on: 2025-04-30 11:13:32

Manual Execution Seems To Be 'Forgotten' The SDLC evolves quickly, as does each stage of the cycle. The testing phase is nowadays evolving based on AI-powered test case generation, automation tools, and CI/CD. However, manual test execution is still unavoidable in most QA teams. The role of a manual QA engineer is still relevant. Manual testing is still indispensable. And at the same time, manual testing is one of the most error-prone and cognitively draining activities. So... why is that?

The Modern Data Stack Is Overrated — Here’s What Works

Aggregated on: 2025-04-29 21:28:32

Once upon a time, getting insights from your data meant running a cron job, dumping a CSV, and tossing it into a dashboard. It was rough, but it worked. Then came the wave — the “Modern Data Stack.” Suddenly, you weren’t doing data unless you had:

Scaling InfluxDB for High-Volume Reporting With Continuous Queries (CQs)

Aggregated on: 2025-04-29 20:28:32

The Bottleneck Our systems are constantly generating high-volume transactional events. In our case, these events are funneled through Kafka and ingested into InfluxDB. Each event includes details such as timestamps, categories, and other metadata. Initially, this architecture supported our analytical needs well. We used InfluxDB to store these metrics and performed queries to generate category-wise transaction reports. Our typical reporting queries looked like this:

Understanding Java Signals

Aggregated on: 2025-04-29 19:28:32

To tee off this presentation, consider a TodosList that contains Todo items. You wish to be able to react to the following events. In any Todoitem, when: The title is changed The completion status is toggled In the TodosList, when: A new item is added An existing item is removed Diving In Here is a basic representation of the respective domain classes:

On-Call That Doesn’t Suck: A Guide for Data Engineers

Aggregated on: 2025-04-29 18:28:32

In large-scale data platforms, reliability doesn’t end with the pipeline's DAG finishing successfully. It ends when the data consumers, whether dashboards, ML models, or downstream pipelines, can trust the data. But ensuring this is harder than it sounds. Poorly designed alerts can turn on-call into a reactive firefight, masking the signal with noise and reducing operator effectiveness. This article presents five engineering principles for scalable, actionable, and low-fatigue data quality monitoring systems, derived from real-world learnings.

Unlocking AI Coding Assistants Part 2: Generating Code

Aggregated on: 2025-04-29 17:28:32

AI coding assistants can help you build working code faster, eliminate manual repetition, and even propose solutions you might not have considered. In this blog, we'll explore how AI tools can become a powerful coding ally, saving you time, boosting creativity, and making your work smoother and more efficient. Enjoy! Introduction This article is the second in the series, with emphasis on generating code. The first part can be read here: "Unlocking AI Coding Assistants Part 1: Real-World Use Cases."

Build an MCP Server Using Go to Connect AI Agents With Databases

Aggregated on: 2025-04-29 16:28:32

Like many of you, I have been playing around with Model Context Protocol (MCP). To dive in, I built a sample MCP server implementation for Azure Cosmos DB with Go. It uses the Go SDK, and mcp-go as the MCP Go implementation.

Debugging Core Dump Files on Linux - A Detailed Guide

Aggregated on: 2025-04-29 15:43:32

Core dumps play a key roll role in debugging programs that exit abnormally. They preserve a state of a program at failure, and with them, programmers can view and identify causes of failures. In this article, a walkthrough is taken through a step-by-step exercise of enabling, creating, and checking out core dumps in Linux and touches on high-end tools and techniques for debugging sophisticated failures, and enables quick diagnoses and resolution. 1. Enabling Core Dumps in Linux Check and Set ulimit Check for current value for core dumps:

The Transformative Power of Artificial Intelligence in Cloud Security

Aggregated on: 2025-04-29 14:13:32

Cloud computing has reshaped how businesses operate, offering unmatched scalability, flexibility, and cost-efficiency. However, as organizations continue to shift critical operations to the cloud, they face escalating cybersecurity challenges. Traditional security systems often struggle to protect complex, interconnected cloud environments from increasingly sophisticated cyberattacks. Artificial Intelligence (AI) has emerged as the ultimate game-changer in cloud security. By enabling real-time threat detection, predictive analytics, and automated responses, AI is addressing challenges that older systems cannot handle. A report from Cybersecurity Ventures highlights that by 2028, spending on AI in cybersecurity is expected to exceed $60 billion, underscoring its growing importance. This article explores the evolving role of AI in cloud security, the benefits it brings, and the challenges it helps organizations overcome. This article explores how AI is reshaping cloud security, supported by graphs that provide insights into data trends and AI’s impact.

The Role of Artificial Intelligence in Climate Change Mitigation

Aggregated on: 2025-04-29 13:13:32

Climate change is among the most demanding global challenges we have. Governments and organizations worldwide are trying to figure out ways to tackle it. Amidst all this, several branches of technology, especially artificial intelligence, are emerging as major contributors to climate change mitigation and adaptation. Understanding the role of AI in climate change mitigation has opened doors to impactful projects. It has also highlighted a number of opportunities for developers to leverage the wonders of technology for a sustainable future. As we approach a new year, the contribution of AI towards mitigating climate change is expected to grow significantly. We owe this growth to huge improvements in computational power, data availability, and algorithmic efficiency. Let me guide you through all the ways developers can use AI effectively to tackle the challenges climate change brings.

How To Replicate Oracle Data to BigQuery With Google Cloud Datastream

Aggregated on: 2025-04-29 12:43:32

This technical guide outlines the steps to set up data replication using Google Cloud Datastream. Specifically, it details the process of setting up data replication from an Oracle 19c database hosted on a Google Compute Engine virtual machine into Google BigQuery. The tutorial covers all necessary steps, including prerequisites—enabling APIs and configuring firewalls, setting up the Oracle source environment, establishing secure networking, creating connection profiles for both Oracle and BigQuery in Datastream, preparing the Oracle database for Change Data Capture (CDC), and finally, creating and validating the Datastream replication job. Prerequisites Before proceeding, ensure the following Google Cloud APIs are enabled within your project. This is done through the Google Cloud Console under APIs & Services

Filtering Messages With Azure Content Safety and Spring AI

Aggregated on: 2025-04-28 21:43:32

As AI-powered applications like chatbots and virtual assistants become increasingly integrated into our daily lives, ensuring that they interact with users in a safe, respectful, and responsible manner is more important than ever. Unchecked user input or AI-generated content can lead to the spread of harmful language, including hate speech, sexually explicit content, or content promoting violence or self-harm. This can negatively affect the user experience and may also lead to legal or ethical problems.

Why I Started Using Dependency Injection in Python

Aggregated on: 2025-04-28 20:28:32

When I first started building real-world projects in Python, I was excited just to get things working. I had classes calling other classes, services spun up inside constructors, and everything somehow held together. But deep down, I knew something was off.

Building Data Pipelines With Jira API

Aggregated on: 2025-04-28 19:43:32

I’ve spent years building data pipelines and connecting project management to technical workflows. Disconnected systems lead to manual errors and delays, problems that Jira’s API helps solve. This tool lets code interact directly with project boards, automating tasks such as creating tickets when data checks fail or updating statuses after ETL.

Doris: Unifying SQL Dialects for a Seamless Data Query Ecosystem

Aggregated on: 2025-04-28 19:13:32

In the field of big data, different database systems often use different SQL dialects. This is similar to people from different regions speaking different languages, which brings great trouble to data analysts and developers. When an enterprise needs to integrate multiple data sources for analysis, it may have to spend a great deal of time and effort switching between different SQL syntaxes. However, Apache Doris breaks this barrier with its powerful SQL dialect compatibility and builds a unified data query ecosystem for users.

Mastering Shift-Left: The Ultimate Guide to Input Validation in Jenkins Pipelines

Aggregated on: 2025-04-28 18:43:32

Successful software development hinges on maintaining a balance between speed and quality. To stay ahead, many organizations are progressively adopting a shift-left approach. Rather than waiting until the end to catch bugs, this strategy emphasizes conducting quality checks and testing much earlier in the development process. One crucial aspect of this approach is input validation—ensuring that the configurations used in builds are correct right from the start. In this tutorial, we’ll walk through how to create a custom input validation framework for Jenkins pipeline. We’ll cover how to validate different input configurations such as JSON files, YAML files, and environment variables. With this system, shift-left framework can catch potential issues early and avoid delays in build and deployment process.

Contextual AI Integration for Agile Product Teams

Aggregated on: 2025-04-28 17:28:32

TL; DR: Not Onboarding But Integration Stop treating AI as a team member to “onboard.” Instead, give it just enough context for specific tasks, connect it to your existing artifacts, and create clear boundaries through team agreements. This lightweight, modular approach of contextual AI integration delivers immediate value without unrealistic expectations, letting AI enhance your team’s capabilities without pretending it’s human. Contextual AI Integration for Agile Product Teams Imagine this scenario: An empowered product team implements an AI assistant to help with feature prioritization and customer insights. Six weeks later, the Product Owner finds its ranking suggestions use irrelevant criteria, product designers notice it ignores established design patterns, and developers see it making technically sound suggestions that are misaligned with their architecture. Despite everyone using the same AI tool, it doesn’t understand how the product team actually works.

Building a Simple To-Do App With Model Context Protocol (MCP)

Aggregated on: 2025-04-28 16:13:32

What Is MCP? The Model Context Protocol (MCP) is an open protocol that standardizes how applications, tools, and AI models provide and access context. Think of MCP as the “USB-C port for AI applications”—just as USB-C lets you connect all sorts of devices with a single standard, MCP lets you connect AI models, desktop apps, and tools to a wide variety of data sources and capabilities in a consistent way. MCP is especially useful for building agents and complex workflows on top of large language models (LLMs), making it easy to integrate your own data and tools into AI-powered environments.

How to Convert Between PDF and TIFF in Java

Aggregated on: 2025-04-28 15:43:32

PDF and TIFF: Converting Between Document and Image in Java We rarely encounter just one document format in enterprise applications. The longer a system has been in production, the more likely it is that file interoperability becomes a real concern. That’s especially true for file types that sit at the intersection of document and image processing, like PDF and TIFF. TIFF and PDF are both widely used in healthcare, insurance, and legal services (among other industries) where a premium is placed upon long-term file fidelity and visual accuracy. While PDF has a much wider range of use-cases, TIFF holds ground in archival contexts and systems that prefer image-based representations over embedded formatting.

Building Event-Driven Go Applications With Azure Cosmos DB and Azure Functions

Aggregated on: 2025-04-28 14:58:32

The Go programming language is a great fit for building serverless applications. Go applications can be easily compiled to a single, statically linked binary, making deployment simple and reducing external dependencies. They start up quickly, which is ideal for serverless environments where functions are frequently invoked from a cold start. Go applications also tend to use less memory compared to other languages, helping optimize resource usage and reduce costs in serverless scenarios. Azure Functions supports Go using custom handlers, and you can use triggers and input and output bindings via extension bundles. Azure Functions is tightly integrated with Azure Cosmos DB using bindings (input, output) and triggers.

Thermometer Continuation in Scala

Aggregated on: 2025-04-25 21:28:30

A continuation represents the control state of computation at a given point during evaluation. Delimited Continuations is a programming mechanism that can be used to implement various control flow constructs. Thermometer continuations implement delimited continuation using exceptions and state, particularly focused on saving and resuming interactive or concurrent computations at specific points; they often require additional context or constructs to be fully realized. In a 2018 paper, “Capturing the Future by Replaying the Past,” Koppel et al. showed that in any language with exception and state, thermometer continuations can be used. This allows most of the languages to implement thermometer continuations. Koppel provided Java, OCaml and SML reference implementations as part of the paper; Python implementation is available here, where the author devised a way to overcome python generator’s inability to run the continuation multiple times.

SQL Server Index Optimization Strategies: Best Practices with Ola Hallengren’s Scripts

Aggregated on: 2025-04-25 19:13:30

Index maintenance is a critical component of database administration as it helps ensure the ongoing efficiency and performance of a Structured Query Language (SQL) Server environment. Over time, as data is added, updated, and deleted, index fragmentation can occur, where the logical and physical ordering of index pages becomes misaligned. This fragmentation can lead to increased disk I/O, decreased query performance, and overall system inefficiency. Running index maintenance jobs, such as those provided by the Ola Hallengren SQL Server Maintenance Solution, allows DBAs to proactively address this fragmentation and optimize the indexes for better performance. By regularly monitoring index fragmentation levels and executing maintenance operations like index reorganizations and rebuilds, DBAs can keep their databases running at peak efficiency. This is especially important for large, mission-critical databases, where any degradation in performance can have a significant business impact. Maintaining optimal index health helps ensure fast, reliable data access, reduced resource consumption, and an overall improvement in the user experience. Consequently, implementing a well-designed index maintenance strategy is a crucial responsibility for any DBA managing a complex SQL Server environment.

Clean Up Event Data in Ansible Event-Driven Automation

Aggregated on: 2025-04-25 18:13:30

In the past few articles, we explored how to use different event sources in Ansible Event-Driven Automation (EDA). In this demo, we'll focus on how event filters can help clean up and simplify event data, making automation easier to manage. Specifically, we'll explore the ansible.eda.dashes_to_underscores event filter and how it works. When using Ansible EDA with tools like webhooks, Prometheus, or cloud services, events often come in as JSON data. These JSON payloads usually have keys with dashes in their names, like alert-name or instance-id. While this is fine in JSON, it becomes a problem in Ansible because variable names with dashes can't be used directly in playbooks or Jinja2 templates. The dashes_to_underscores filter helps solve this issue by converting those dashed keys into names that Ansible can work with more easily.

How to Build the Right Infrastructure for AI in Your Private Cloud

Aggregated on: 2025-04-25 17:28:30

AI is no longer optional. From fraud detection to predictive maintenance, businesses everywhere are investing in machine learning and deep learning models. But training and running these models isn't light work. They require high-performance hardware, massive storage, fast networking, and serious automation. Public clouds like AWS and Azure offer AI-ready infrastructure, but not every company wants to go that route. Whether it's for compliance, cost control, or pure performance, many teams are building AI stacks in their private cloud environments.

Analyzing “java.lang.OutOfMemoryError: Failed to create a thread” Error

Aggregated on: 2025-04-25 15:28:30

Understanding the Error The OutOfMemoryError thrown appears not because of insufficient heap memory but due to the following reasons: Excessive Thread Creation If the application creates too many threads, it may reach a limit where no more threads can be created. In such cases, it is crucial to investigate the stack trace thrown with the error and reduce the number of thread creations at the application code level.

Java's Quiet Revolution: Thriving in the Serverless Kubernetes Era

Aggregated on: 2025-04-25 13:28:30

Along with the rise of Kubernetes, there is another shift that is happening under the hood - the rise of serverless architecture, which is quietly rewriting the way we deploy and scale applications, with Java taking a lead. Java, which is usually associated with legacy code and monolithic enterprise applications, has been slowly but steadily evolving into a microservices architecture and is now evolving into a leaner, serverless-ready world. With the availability of tools like Knative and frameworks like Quarkus, Java has been transforming from a heavyweight language into a zero-management, Kubernetes-ready ready. In this article, we will reflect on this promising transformation in Java and where it can take us in 2025 and beyond.

Scalability 101: How to Build, Measure, and Improve It

Aggregated on: 2025-04-25 11:28:30

In this post, I'd like to talk a little about scalability from a system design perspective. In the following paragraphs, I'll cover multiple concepts related to scalability—from defining what it is, to the tools and approaches that help make the system more scalable, and finally, to the signs that show whether a system is scaling well or not. What Is Scalability? First things first: I’m pretty sure you know what scalability is, but let’s have a brief look at the definition just to be safe.

The Role of Functional Programming in Modern Software Development

Aggregated on: 2025-04-24 22:58:30

Functional programming is one approach used in modern software development due to technological advancements. It enables developers to write code that is clean, efficient, and easy to maintain. In addition, programming methods that are focused on pure functions, immutability, and declarative style make it easy for developers to create applications. This article analyzes the major principles of functional programming and their importance in modern software development.

5 Best Node.js Practices to Develop Scalable and Robust Applications

Aggregated on: 2025-04-24 20:43:30

Since the software industry is evolving at an incredibly fast speed, there have been many developments in terms of frameworks and different component libraries to enhance the performance, functionalities, and features of applications. And the name Node.js has come among the top ones as it has taken the app development world by storm. Just to clarify, Node.js is not a framework, but it is an open-source JavaScript runtime environment that can be operated on Linux, Unix, macOS, and Windows as well. After its initial release in 2009, Node.js has dominated the software industry.

Apache Doris vs Elasticsearch: An In-Depth Comparative Analysis

Aggregated on: 2025-04-24 20:43:30

In the field of big data analytics, Apache Doris and Elasticsearch (ES) are frequently utilized for real-time analytics and retrieval tasks. However, their design philosophies and technical focuses differ significantly. This article offers a detailed comparison across six dimensions: core architecture, query language, real-time capabilities, application scenarios, performance, and enterprise practices. 1. Core Design Philosophy: MPP Architecture vs. Search Engine Architecture Apache Doris employs a typical MPP (Massively Parallel Processing) distributed architecture, tailored for high-concurrency, low-latency real-time online analytical processing (OLAP) scenarios. It comprises front-end and back-end components, leveraging multi-node parallel computing and columnar storage to efficiently manage massive datasets. This design enables Doris to deliver query results in sub-seconds, making it ideal for complex aggregations and analytical queries on large datasets.

Scaling in Practice: Caching and Rate-Limiting With Redis and Next.js

Aggregated on: 2025-04-24 20:43:29

As someone who loves shipping products and tools for people to experiment with and use, it was only a matter of time before scalability issues caught up with me. I always knew what scaling meant and how to scale in theory. I was familiar with vertical and horizontal scaling, and the intricacies of each, but again, in theory. I had actually never needed to implement any of them, as my user base was usually modest. This is until I built a faucet that exceeded my expectations in terms of usage. But this massive traction came with its own set of issues. The TL;DR of the issue was that even though I restricted the frequency with which a wallet address could claim funds to once a day, a user could simply spin up new addresses and keep claiming. This resulted in my faucet being drained frequently.

Reinforcement Learning for AI Agent Development: Implementing Multi-Agent Systems

Aggregated on: 2025-04-24 20:43:29

The field of AI has advanced at a breathtaking pace, and reinforcement learning (RL) is now fast emerging as the leading paradigm for the development of intelligent AI agents. You make RL much more powerful when correctly combined with multi-agent systems. That enables agents to compete, coordinate, and train in dynamic environments. This article introduces the concept of reinforcement learning in building AI agents, and more specifically, how to develop multi-agent systems.

Auto-Instrumentation in Azure Application Insights With AKS

Aggregated on: 2025-04-24 20:43:29

Monitoring containerized applications in Kubernetes environments is essential for ensuring reliability and performance. Azure Monitor Application Insights provides powerful application performance monitoring capabilities that can be integrated seamlessly with Azure Kubernetes Service (AKS). This article focuses on auto-instrumentation, which allows you to collect telemetry from your applications running in AKS without modifying your code. We'll explore a practical implementation using the monitoring-demo-azure repository as our guide.

CRDTs Explained: How Conflict-Free Replicated Data Types Work

Aggregated on: 2025-04-24 20:43:29

In today's post, I would like to dive deeper into one of the newest—relatively speaking— topics in the distributed systems domain. As you may have guessed already, the spotlight is on Conflict-free Replicated Data Types or CRDTs for short. I will explain what they are and what role they play in the larger landscape of distributed systems. Let’s start our journey from explaining what Strong Eventual Consistency (SEC) means in this context.

Key Considerations in Cross-Model Migration

Aggregated on: 2025-04-24 20:43:29

With the rampant development and release of AI models every few days, ML engineers are expected to conduct comprehensive experiments with different models to choose the best-performing one. However, this is often not a straightforward process — it requires both art and structured methodology. Modifying the underlying prompts while ensuring best practices is a key challenge that is often not discussed much. Moreover, while it may seem straightforward to simply “swap out” the underlying model and its associated prompt, there are several more nuances to consider — tokenizers, context window sizes, instruction-following abilities, sensitivity to prompt formatting, structured response generation, latency-throughput tradeoff, etc.

Power BI Embedded Analytics — Part 1: Introduction and Power BI Authoring Overview

Aggregated on: 2025-04-24 20:43:29

Note: This is a multi-part series exploring Power BI Embedded Analytics. The other parts of the series are listed below: Part 1.1: Power BI Authoring Data Federation Part 2: Power BI Embedded Overview Part 3: Power BI Embedded Demo Analytics Analytics is all about making sense of data — finding patterns, uncovering insights, and using that information to make smarter decisions. It involves various techniques and tools to interpret data, create visualisations, and simplify complex information. Whether it’s businesses optimizing operations or researchers making discoveries, analytics is crucial in improving efficiency, productivity, and competitiveness.

Optimizing AI Model: A Guide to Improving Performance (3 of 3)

Aggregated on: 2025-04-24 20:43:29

In the rapidly evolving world of Artificial Intelligence (AI), having a working model is not enough. What really matters lies in optimizing that model to perform efficiently across a multitude of scenarios. Performance optimization plays a vital role in unlocking the full potential of your AI system, whether you are fine-tuning hyperparameters, refining your data pipeline, or leveraging advanced evaluation metrics. This guide, the last of our three-part series about AI, offers insights to help you optimize methods and step-by-step implementation instructions for enhancing AI performance. The first two articles are linked below:

Why Testing is a Long-Term Investment for Software Engineers

Aggregated on: 2025-04-24 20:43:29

In the world of software engineering, we’re constantly racing against the clock—deadlines, deployments, and decisions. In this rush, testing often gets sidelined. Some developers see it as optional, or something they’ll “get to later.” But that’s a costly mistake. Because just like documentation, testing is a long-term investment—one that pays off in quality, safety, and peace of mind. Testing is crucial. It’s about ensuring quality, guaranteeing expected behavior, and enabling safe refactoring. Without tests, every change becomes a risk. With tests, change becomes an opportunity to improve.

Failure Handling Mechanisms in Microservices and Their Importance

Aggregated on: 2025-04-23 18:28:29

Microservices architecture has gained significant popularity due to its scalability, flexibility, and modular nature. However, with multiple independent services communicating over a network, failures are inevitable. A robust failure-handling strategy is crucial to ensure reliability, resilience, and a seamless user experience. In this article, we will explore different failure-handling mechanisms in microservices and understand their importance in building resilient applications.

AI Model Evaluation: Metrics, Visualization and Performance (2 of 3)

Aggregated on: 2025-04-23 17:28:29

An Artificial Intelligence (AI) model, despite going through constant and rigorous training, may not function as intended. It is important to observe strict and continuous evaluations to measure its accuracy and reliability in real-world applications. Assessing key metrics such as precision, recall, and F1-score allows the AI model to apply its learned patterns across different datasets successfully. Without sufficient evaluation, even advanced AI models may not reach their full potential. The effectiveness of an AI model is evaluated through a combination of measurement techniques, visual representation, and analysis of failures. These include the following:

The Role of AI in Enhancing DevOps Processes

Aggregated on: 2025-04-23 16:43:29

An Introduction to DevOps and AI Integration DevOps is this awesome mix of teamwork and tech that’s all about getting software developers and IT operations on the same page. It’s less about silos and more about chatting openly, working together, and using automation to pump out top-notch software faster than ever. In today’s wild, fast-moving digital world, DevOps is your ticket to staying ahead, cranking out products quicker, and always tweaking them to be better. But here’s the thing: as software delivery gets fancier with microservices, cloud setups, and slick CI/CD pipelines, DevOps teams are stuck wrestling with mountains of data, crazy-complex systems, and stuff that needs to happen right now. That’s where artificial intelligence (AI) swoops in like a superhero sidekick. With AI in the mix, teams can ditch the repetitive grunt work, spot trouble brewing before it hits, and keep everything flowing smoothly. The payoff? Software drops faster and works like a dream.

How to Build Local LLM RAG Apps With Ollama, DeepSeek-R1, and SingleStore

Aggregated on: 2025-04-23 15:28:29

In a previous article, we explored how to use Ollama and DeepSeek-R1 with SingleStore for a simple example. In this article, we'll build on that example by working with a PDF document from the internet. We'll store the document and its vector embeddings in SingleStore, then use DeepSeek-R1 to identify blockchain investment opportunities. The notebook file used in this article is available on GitHub.