Operations | Monitoring | ITSM | DevOps | Cloud

Fluentd vs Logstash: In-Depth Comparison of Two Popular Log Collectors 2025

In modern observability stacks, log collection is a critical component. Among the most widely adopted logs collector are Fluentd and Logstash. Both tools are designed to collect, process, and forward logs to various destinations like Elasticsearch, Kafka, and cloud services. However, the differences between FluentD and Logstash lie significantly in their design, performance, plugin ecosystems, and user experiences.

CoinsPaid Sees 38% Growth in Crypto Travel Payments as Sector Modernizes

The integration of cryptocurrency into mainstream industries is accelerating, and the travel sector is no exception. CoinsPaid, a major crypto payment ecosystem, has announced a 38% year-on-year increase in transactions from travel-related businesses - a clear signal that the sector is turning to digital currencies for greater efficiency and global reach.

5 UX Best Practices for Resilient & High-Performing Mobile Apps

What keeps users coming back to an app? Speed helps. Stability matters. But more than anything, people return to tools that feel easy to use, even under pressure. When an application responds clearly and behaves as expected, users are more likely to stick around. UX design plays a quiet but powerful role in this. It's not just about how something looks-it's how it works. The small details in navigation, layout, and screen flow all contribute to whether someone continues using an app or closes it within seconds.

Bunnyshell Named Startup of the Year 2024 in Palo Alto by HackerNoon

"If AI is writing the code, we make sure it runs." Alin Dobra, Founder Bunnyshell We’re proud to announce that Bunnyshell has been named Startup of the Year 2024 in Palo Alto by HackerNoon! This recognition reflects the work we’ve done to build the Software Delivery Platform for a new era—where code is written by AI, but validated by real environments.

Working with GPUs on Kubernetes and making them observable

GPUs are everywhere powering LLM inference, model training, video processing, and more. Kubernetes is often where these workloads run. But using GPUs in Kubernetes isn’t as simple as using CPUs. You need the right setup. You need efficient scheduling. And most importantly you need visibility. This post walks through how to run GPU workloads on Kubernetes, how to virtualize them efficiently, and how Coroot helps you monitor everything with zero instrumentation or config.

Hyperparameter tuning for LLMs using CircleCI matrix workflows

Hyperparameter tuning is a critical step in optimizing large language models (LLMs). Parameters such as learning rate, batch size, weight decay, and number of training epochs can significantly affect convergence behavior and final model performance. While several approaches like grid search or random search are widely used, executing them manually is inefficient; especially when each training run is compute-intensive.