NexTechTide

When working with remote Linux hosts, maintaining persistent sessions, running commands in parallel, and protecting workflows from network interruptions are essential skills. Two of the most popular tools for handling these challenges are GNU Screen and Tmux . While both provide similar functionality, Tmux is the better option in 2024 , and here’s why. What Are Screen and Tmux? GNU Screen Screen is a terminal multiplexer that allows users to create multiple shell sessions, detach them, and reattach later. It has been around since the 1980s and is a lightweight way to keep terminal sessions persistent. Tmux Tmux, or Terminal Multiplexer, is a more modern and feature-rich alternative to Screen. It allows users to create, manage, and organize multiple terminal sessions with enhanced flexibility and usability. Feature Comparison: Screen vs. Tmux Feature Screen Tmux Persistent Sessions ✅ ✅ Window/Pane Management ❌ ✅ Split Terminal Views ❌ ✅ Configuratio...

Managing operations across multiple Linux hosts, especially when some are inaccessible from the public internet, can be challenging. This requires efficient SSH workflows, file transfers, and automation techniques to simplify the work while ensuring security and reliability. Whether you're working with remote servers, embedded devices, or air-gapped systems, this guide will help streamline your workflow. 1. Establishing Efficient SSH Workflows Using SSH Agent for Seamless Authentication Typing passwords or passphrases repeatedly can slow down operations. Use the SSH agent to cache credentials securely: eval $(ssh-agent) ssh-add ~/.ssh/id_rsa # Add your private key This allows seamless authentication across multiple hops. SSH Config for Quick Access Instead of remembering long SSH commands, configure ~/.ssh/config : Host server1 HostName 192.168.1.100 User myuser IdentityFile ~/.ssh/id_rsa ProxyJump jumphost Now, you can simply connect using: ssh server...

Staying relevant in software engineering requires continuous learning. The industry evolves rapidly, with new architectural patterns, team structures, and development methodologies emerging every year. As we move through 2024, there are several key areas of growth for engineers: team collaboration, distributed systems, scalable architectures, DevOps, software design, and system reliability . To stay ahead of the curve, software engineers should focus on books that not only deepen technical expertise but also refine engineering leadership skills . Here’s a carefully curated list of 10 must-read books that will help you grow in these areas. 1. Team Topologies (Matthew Skelton & Manuel Pais) Key Focus: How to structure software teams for high performance. Most engineering challenges aren’t just technical—they’re organizational. This book introduces a modern framework for designing effective team structures that align with software architecture and business goals. Essential for...

In the ever-evolving world of technology, ambitious projects often make bold promises to revolutionize industries. One such initiative is DeepSeek, a platform marketed as the next frontier in AI-driven search and knowledge retrieval. According to its proponents, DeepSeek promises unparalleled accuracy, deep contextual understanding, and the ability to provide instant, hyper-relevant answers to complex queries. But when we scratch beneath the glossy marketing veneer, it becomes clear that DeepSeek is more of an empty promise than a groundbreaking innovation. Let’s explore why. What Is DeepSeek Supposed to Be? DeepSeek positions itself as a revolutionary search engine powered by the latest advancements in large language models (LLMs) and AI. The pitch is enticing: unlike traditional search engines that rely on keyword matching, DeepSeek claims to understand queries on a deeper level, synthesizing data from vast sources to provide precise and actionable answers. Its features include:...

 The term “Web3” once promised a revolution in how the internet operates. It envisioned a decentralized web powered by blockchain technology, where individuals could regain control of their data, bypass intermediaries, and participate in a more equitable digital economy. Yet, as of 2025, the Web3 initiative has largely failed to deliver on these promises, becoming a niche technology with little appeal to the general population. What went wrong? Let’s explore. What Is Web3? Web3, short for the third generation of the web, aimed to move beyond the centralized platforms of Web2 (dominated by companies like Google, Facebook, and Amazon). Instead of centralized servers, Web3 relies on blockchain and decentralized protocols to: Enable peer-to-peer transactions. Give users ownership of their data. Facilitate decentralized applications (dApps) that run without centralized control. Use cryptocurrencies and tokens as the economic backbone. In theory, this vision sounded revolutionary. I...

  The software job market of 2025 is intensely competitive, with a glut of applicants, from juniors to experienced professionals, vying for the same roles. For someone with 20+ years of experience, especially in their late 40s or early 50s, navigating this landscape requires a strategic, efficient, and tailored approach. But with the right mindset and strategy, it’s entirely possible to not just survive but thrive. Here’s how you can make your experience and wisdom work for you, and why this might just be your time to shine. Start by Recognizing Your Worth It’s easy to feel overshadowed in a job market flooded with younger candidates who are grinding away at LeetCode, preparing for system design interviews, or using AI tools to ace technical challenges. But here’s the truth: you bring something to the table that few others can. Your decades of experience have taught you lessons that no crash course or bootcamp could ever replicate. You’ve lived through the evolution of technolo...

  The software development job market is a constantly shifting landscape, shaped by the rapid evolution of technology, economic pressures, and the demands of modern businesses. As we step into 2025, it’s clear that developers at all levels—from juniors just starting out to principal engineers leading technical strategies—will face unique challenges and opportunities. By examining the current trends, we can better understand what lies ahead for each tier and how to navigate the shifting tides. Junior developers are entering a crowded market, where competition is fiercer than ever. With bootcamps, self-taught programmers, and computer science graduates all vying for the same entry-level positions, standing out has become a monumental task. Adding to this challenge is the rise of low-code and no-code platforms, which are automating simpler programming tasks. While this doesn’t mean the end of opportunities for juniors, it does mean that the bar has been raised. Employers now expect ...

  Zig, a systems programming language designed to be robust and predictable, has been gaining a small but vocal following in recent years. It markets itself as a simpler alternative to C, with strong compile-time safety features and modern language design principles. However, despite its merits, Zig is unlikely to ever achieve mainstream status. In this post, we’ll examine the reasons behind this, including some real (or close to real) anecdotes that showcase the challenges and quirks of Zig that make it a tough sell for widespread adoption. The Vision and the Reality of Zig Zig’s vision is compelling: a programming language that fixes the flaws of C, provides manual memory management without undefined behavior, and offers robust tools for debugging and optimization. It even promises to replace build systems like make with its own integrated build tool. On paper, this makes Zig look like a dream come true for systems programmers. But in reality, several factors hold it back: ...

 With the increasing prominence of large language models (LLMs), there is growing interest in running these models locally, without requiring network access. This approach offers greater privacy, better control, and the ability to use AI in environments where internet connectivity is limited or undesirable. In this blog post, we’ll explore some of the most notable local LLM tools, their requirements, applications, and quality. Why Run LLMs Locally? Privacy: Avoid sending sensitive data over the internet by processing it locally. Security: Reduce risks associated with third-party servers or cloud providers. Customization: Full control over model fine-tuning and data integration. Accessibility: Operate in environments with limited or no internet access. Popular Local LLM Tools Here are some of the most prominent tools and frameworks for running LLMs locally: 1. LLaMA (Large Language Model Meta AI) Overview: Developed by Meta, LLaMA is a family of efficient LLMs designed ...

  As large language models (LLMs) and generative AI (GenAI) become more sophisticated, they’re being used to create vast amounts of text, from casual blog posts to professional documentation. However, these machine-generated texts often carry subtle markers—or fingerprints—that distinguish them from human-written content. This blog post explores these hidden indicators, the techniques used to detect them, and what they mean for content creators, developers, and researchers. What Are Fingerprints and Watermarks in LLM-Generated Texts? Fingerprints: Unintentional patterns or statistical anomalies embedded in text due to the model’s architecture, training data, or decoding methods. These patterns are not deliberately added but arise naturally from how LLMs function. Watermarks: Deliberately added markers in the generated text designed to signal that the content was produced by a specific model. Watermarks are often implemented to ensure accountability, combat misinformation, ...

 Artificial intelligence (AI) and machine learning (ML) are no longer niche fields. They’ve become essential tools for software development, transforming industries and workflows alike. If you’ve found yourself in conversations about AI, ML, or large language models (LLMs) and felt out of your depth, this guide is for you. Let’s cover the basics, highlight key concepts, and explore how to get started in the age of AI. Key Terms and Abbreviations Artificial Intelligence (AI): The broad field of creating systems that simulate human intelligence. Machine Learning (ML): A subset of AI focused on teaching machines to learn patterns from data and make predictions or decisions. Deep Learning (DL): A subfield of ML using neural networks with many layers to model complex patterns in data. Neural Network: A computational structure inspired by the human brain, consisting of layers of interconnected nodes (neurons). Large Language Model (LLM): A type of deep learning model trained on v...

  1. System76 Overview: System76 is one of the most prominent Linux-focused computer manufacturers. They design and sell laptops, desktops, and servers specifically optimized for Linux. Quality: Known for premium build quality and sleek designs. Price Range: Mid to high-end. Laptops start around $1,000, while desktops and servers can go much higher depending on configurations. Customer Feedback: System76 receives high praise for customer support, pre-installed Linux distributions, and community engagement. Supported Distributions: Ships with their custom Pop!_OS (based on Ubuntu) or Ubuntu itself. Highlights and Caveats: System76's unique contribution to open-source hardware design stands out, particularly with their Thelio desktops. These systems feature innovative airflow management and the Thelio Io open hardware SATA controller, emphasizing their commitment to hardware transparency. However, their custom drivers and firmware updates are tailored specifically for Pop!_OS,...

  Amazon S3 Tables is one of the latest additions to the AWS ecosystem, promising to streamline the use of structured data stored in S3. At first glance, it might seem like just another tool in the vast AWS toolkit, but does it really enable something that wasn’t possible before? Let’s take a closer look. What AWS S3 Tables Offers S3 Tables introduces a way to interact with structured data in Amazon S3, making it feel more like working with traditional database tables. It provides a metadata-driven approach to accessing data, allowing you to define a schema and interact with your datasets using familiar query interfaces. This makes S3 Tables particularly appealing for data-heavy applications, where querying and organizing data stored in S3 has traditionally required additional steps or services. What Was Possible Before S3 Tables? Before the introduction of S3 Tables, AWS customers could achieve similar functionality through a combination of existing services: Amazon Athena...

  1. AWS Data Lifecycle Manager If you’re working with a lot of EC2 snapshots or EBS volumes, AWS Data Lifecycle Manager (DLM) can save you time and reduce costs. This service automates the creation, retention, and deletion of snapshots, ensuring your storage footprint remains optimized without manual intervention. Why Use It? Automating snapshot management reduces the risk of human error and can save you money by cleaning up outdated backups. 2. AWS AppConfig Part of the AWS Systems Manager suite, AppConfig allows you to manage and deploy application configuration changes dynamically. It’s perfect for applications where configuration updates need to be rolled out without redeploying code. Why Use It? It’s a safe way to roll out feature flags, throttling parameters, or any other configuration, with rollback capabilities built-in. 3. AWS Resource Access Manager (RAM) AWS RAM simplifies the process of sharing AWS resources between accounts in an organization. Instead of du...

  Choosing between Go and Rust isn’t about picking the "better" language; it’s about selecting the right tool for the job. Both languages excel in their own ways, and understanding their strengths and trade-offs is essential for making the right decision. Here’s a practical guide to when you should use Go and when Rust is the better choice. Performance and Resource Management There’s no significant difference in raw performance between Go and Rust for many common tasks. Both languages are compiled and optimized for speed. However, Rust’s lack of a garbage collector allows for more predictable and stable performance, particularly in applications where memory consumption and latency need to remain consistent. Rust enables fine-grained control over memory and CPU usage without the potential unpredictability of garbage collection pauses. Meanwhile, Go’s garbage collector is designed for simplicity and works exceptionally well in most workloads, especially for scalable, server...

  In the early days of computing, the world of operating systems was a vibrant and diverse ecosystem. Before the graphical user interface (GUI) revolution, operating systems were simpler in their scope. The standard toolkit typically included a compiler, a text editor, and file system utilities. The primary goal of these systems was to perform computations, and with a reasonably small team of developers, it was entirely feasible to recreate this ecosystem for a new operating system. The rise of GUIs, however, marked a turning point. Developing GUI-based applications was significantly more challenging than their text-based predecessors. As the complexity of these applications grew, operating systems with larger user bases began to attract more developers and applications. This created a self-reinforcing cycle: popular operating systems grew even more dominant because they offered more software, while those with smaller user bases struggled to keep up. This snowball effect led to t...

  GoLang, often simply called Go, is a language that has quietly but steadily made its mark since Google open-sourced it more than a decade ago. At first glance, Go might seem unremarkable if you’re someone who evaluates languages based on flashy features or groundbreaking paradigms. But that’s the brilliance of Go - it doesn't try to dazzle you with its complexity. Instead, it wins you over with its simplicity, its pragmatism, and its sheer focus on what matters most: getting things done. What sets Go apart isn't just its syntax or its runtime; it's the ideology behind it. Everything about this language is deliberate. It’s easy to learn - not because it sacrifices power or flexibility, but because it is designed that way. There are no sharp edges, no gotchas lurking in the shadows, and no need for mental gymnastics to understand its core principles. For someone coming from another language, whether it’s Java, Python, or even C++, picking up Go feels almost intuitive. You...