34 articles
Go deeper than the dashboard. Master the tools for atomic-level troubleshooting. Learn to use 'strace' to watch a program's interaction with the kernel and 'perf' to find exactly which line of code is consuming your CPU.
Master the logic of Linux memory allocation. Learn why 'Free RAM' is actually a waste. Understand the 'Swappiness' parameter and how it affects performance. Learn to handle the 'OOM (Out of Memory) Killer'—the kernel's final tool for survival.
Squeeze every megabit out of your connection. Master the art of Linux network optimization. Learn to tune TCP 'Backlogs', increase 'Window Sizes' for high-latency links, and understand the logic of 'Congestion Control' (BBR).
Solve the mysterious slowness. Master the tools for disk performance analysis. Learn to use 'iostat' to measure disk throughput and 'iotop' to identify exactly which process is 'Thrashing' your hard drive. Understand why 100% disk usage can break a 16-core CPU.
Master the most common tools in the Linux world. Go beyond basic sorting. Learn to use 'top' in batch mode, color-code your 'htop' view, and understand the difference between VIRT, RES, and SHR memory. Identify which process is truly killing your server.
Why is your server slow? Master the methodology of performance troubleshooting. Learn to distinguish between CPU-Bound, RAM-Bound, and I/O-Bound issues. Understand the 'Load Average' and learn where to look when the system starts to crawl.
Is your server dying? Learn to read the real-time performance of your Linux system. Master 'top' and 'htop' to find resource-hungry processes, and decode the 'free' command to understand why Linux uses all your RAM for Cache.
A professional comparison of the three major operating systems. Understand why Linux dominates the server and cloud, while macOS and Windows rule the desktop. Learn about kernels, filesystems, and cost models.
Master the constraints of AI: context windows, token limits, and how to manage long conversations.
Why does the AI forget what you said 20 minutes ago? In our final lesson of Module 2, we explore the 'Context Window' and the hard limits of model memory.
Why monolithic agents are a trap for enterprise AI. Learn how to architect a fleet of 'Nano-Agents' for 50ms latency, improved reliability, and massive cost savings.
Is your app slow or is it Docker? Learn how to monitor CPU, RAM, and I/O performance of your containers using the built-in 'stats' command and external tools.
Stop waiting for 'npm install'. Learn the difference between Artifacts and Caching, and how to use Cache to speed up your pipeline by 500%.
Break the stage barrier. Learn how to use 'needs' to create a Directed Acyclic Graph (DAG), allowing fast jobs to skip ahead without waiting for slow, unrelated tasks.
Faster, Safer, Better. Learn how to use Google's Kaniko to build container images without root privileges or a Docker daemon, solving the performance issues of DinD.
Master the hidden settings. Explore the most important environment variables to control n8n's performance, security, and integration capabilities.
Handle the load. Learn how to switch from a 'Main' process to 'Queue Mode' using Redis workers to handle thousands of concurrent automations.
Handle the many. Learn how to process thousands of items in small chunks to avoid hitting API rate limits or crashing your server's memory.
Handling the Peak. Advanced strategies for dealing with Bedrock's rate limits using exponential backoff and request queuing.
The Typing Effect. How to use converse_stream to send tokens to your UI as they are generated.
Handling the Load. Understanding Bedrock's rate limits and how to optimize for the fastest response times.
Zero Latency UX. How to use LangChain's .stream() method to display text as it's being generated.
Parallel Processing. How to use .batch() to send multiple independent queries to an LLM at once.
Choosing the right engine for your AI. A technical comparison of how different processors handle LLM workloads.
Parallel power. How to configure Ollama to use multiple graphics cards for giant 70B models.
Serving the crowd. How to configure Ollama to handle multiple concurrent user requests.
Understanding the trade-offs of scale. Why a 70B model is smarter than an 8B model, and why you might not want to use it.
Optimization 101. Balancing speed vs quality vs memory in your local AI setup.
Hands-on: Benchmarking your machine. Compare quantization levels and measure memory usage in real-time.
How Ollama handles memory. Understanding why the 'second' run is always faster than the 'first'.
Processing at scale. How to optimize Ollama for high-volume tasks like document digestion.
Hands-on: Benchmarking your machine. Compare quantization levels and measure memory usage in real-time.
Learn how to measure the efficiency of your code. Understand Big O notation and why it matters for scaling software.
Meet the foundation of numerical Python. Learn how NumPy arrays differ from lists and how 'vectorization' makes your math 100x faster.