What is a Script in Computing

Most developers think clean code is enough. It’s not. You can have beautiful code... And still crash under real traffic. Because production doesn’t care about readability alone. It cares about: • Concurrency • Timeouts • Memory usage • Database locks • Retry storms • Load spikes Clean code matters. But resilient systems require more than clean code. Software that reads well is useful. Software that survives is valuable. #BackendDevelopment #SoftwareEngineering #SystemDesign #Python #Scalability

I ran into this problem many times in both work and personal projects: data coming from hardware or controllers over UDP, usually implemented in C/C++, but then needing to quickly inspect, debug, or visualize it in Python. In practice, there is no clean way to directly convert C-like structs into usable Python classes. The common workaround ends up being manual reimplementation or copying structs into external tools. I also tried using ChatGPT for this, which works to some extent, but it has clear downsides: it introduces potential safety concerns when sharing code externally, and more importantly, the generated output is often inconsistent and becomes hard to maintain when structs change. Keeping Python and C++ in sync quickly turns into repetitive and error-prone work. To solve this, I built a small tool for myself: StructoPy. It reads C/C++ struct definitions from header files and generates matching Python classes automatically. The generated classes include all fields from the original structs and provide helper methods for binary serialization and deserialization. This removes the need to manually maintain duplicate definitions or rebuild schemas whenever something changes. It was initially built as a private tool for my own workflow, but over time it saved me a significant amount of time on struct updates and debugging tasks, so I decided to clean it up and share it publicly. In practice, it makes struct changes much easier: instead of copying definitions into external tools or maintaining separate schemas, you just run a single command to regenerate the Python bindings. The workflow is straightforward: You provide a .hpp file with C/C++ structs, StructoPy parses it, resolves project includes where possible, and generates Python classes that mirror the original binary layout. It also generates a small test file to validate serialization and deserialization automatically. Internally, it uses Python struct format strings (little-endian) and maps C types directly to binary representations. It is currently Linux-only (tested on Ubuntu and Nobara) and works best for straightforward struct-based communication. GitHub: https://lnkd.in/dx6TNvrv #C++ #Python #EmbeddedSystems #IoT #Robotics #UDP #DataEngineering #SoftwareEngineering #SystemsProgramming #DevTools #CodeGeneration #Serialization #BinaryData #OpenSource #Linux #ESP32

Still configuring networks manually? That’s time-consuming, error-prone and increasingly outdated. Python is quickly becoming a must-have skill for network engineers, not to turn you into a developer, but to help you: ⚡ Automate repetitive tasks ⚡ Reduce outages caused by human error ⚡ Manage multi-vendor environments more efficiently The good news? You don’t need to be a programmer to get started. Our 5-day Network Automation with Python course is built for engineers who want practical, hands-on skills they can use immediately. If automation is on your roadmap this year comment or message me if you want the details.

Still configuring networks manually? That’s time-consuming, error-prone and increasingly outdated. Python is quickly becoming a must-have skill for network engineers, not to turn you into a developer, but to help you: ⚡ Automate repetitive tasks ⚡ Reduce outages caused by human error ⚡ Manage multi-vendor environments more efficiently The good news? You don’t need to be a programmer to get started. Our 5-day Network Automation with Python course is built for engineers who want practical, hands-on skills they can use immediately. If automation is on your roadmap this year comment or message me if you want the details.

Still configuring networks manually? That’s time-consuming, error-prone and increasingly outdated. Python is quickly becoming a must-have skill for network engineers, not to turn you into a developer, but to help you: ⚡ Automate repetitive tasks ⚡ Reduce outages caused by human error ⚡ Manage multi-vendor environments more efficiently The good news? You don’t need to be a programmer to get started. Our 5-day Network Automation with Python course is built for engineers who want practical, hands-on skills they can use immediately. If automation is on your roadmap this year comment or message me if you want the details.

Why your Python API slows down in production: Most Python APIs aren’t slow. They’re just waiting too much. Our system wasn’t slow in development. It broke only in production. We were handling thousands of customer interactions daily (Calls, SMS, Email - integrated with Cisco Contact Center) Everything looked fine during testing. Then real traffic hit. Suddenly: ❌ APIs started slowing down ❌ Response times increased ❌ Campaign execution got delayed At first, we assumed: "It must be complex logic." It’s not. The real problem is simple and very common in Python: 👉 Blocking I/O operations 👉 Sequential API calls 👉 Database calls inside loops Which meant: While one request was waiting… The system was doing nothing. That’s where things changed. We didn’t rewrite business logic. We changed how the system handles waiting. ✔ Introduced async for I/O operations ✔ Reduced unnecessary DB round-trips ✔ Improved API communication flow ✔ Enabled better concurrency Result: ✔ Faster API responses ✔ Higher throughput ✔ More stable systems under load 👉 Latency improved by ~25–30% under load This is where backend + DevOps thinking really matters. Not just writing code… But building systems that survive production. I’ve broken this down visually below 👇 Have you seen something like this in your system? What was the real root cause? 👇 Let’s discuss #Performance #Python #FastAPI #Async #Backend #SystemDesign #Performance

Just shipped diskdump - fast, deduplicated raw disk capture over plain SSH. This is not a traditional backup tool - there are plenty of mature solutions for that. The use case here is different: 👉 You have multiple (often similar) machines 👉 You want to quickly grab full raw disks (/dev/sda) 👉 Store them efficiently 👉 Reuse, rehydrate, or analyze them locally (e.g. for forensics) That's where diskdump fits. Instead of repeatedly copying entire disks, it uses content-addressable deduplication across machines and time. How it works: • Ships a zero-dependency Python script over SCP • Streams the disk and hashes 128KB blocks • Checks what already exists locally • Transfers only new data (compressed over SSH) • Stores everything in a shared block store + lightweight manifests Why it matters: • Dump 10 similar servers → you don't store 10× the data • Repeated captures of the same host → near-zero transfer after the first run • Single-pass streaming → no temp files, minimal footprint on remote systems Example: Dump multiple machines in parallel diskdump dump server01:/dev/sda server02:/dev/sda Restore / rehydrate diskdump restore 2026/04/24/server01-sda.manifest | dd of=/dev/sda Or mount/analyze locally for investigations, diffing, or debugging. No agents. No daemon. Just SSH + Python. Dependencies: • Remote: Python 3 (stdlib only) • Local: Python 3 + lz4 Source: https://lnkd.in/dGHvWMQc #python #devops #forensics #incidentresponse #sysadmin #opensource

Empty files waste storage. Cleaning them manually wastes time. I built a tool to solve that. ZeroByteCleaner — Automated File System Cleanup Tool A Python automation tool that runs in the background and keeps your directory clean — without any manual effort. How it works: → Point it to any folder → It recursively scans every file and subfolder → Detects all empty (0-byte) files → Deletes them automatically → Generates a timestamped log report → Repeats on schedule — every minute, hour, or day The real challenge was making it reliable: → What if the path doesn't exist? → What if it's a file, not a folder? → What if a file is locked by the system? Handling edge cases is what separates a working script from a production-ready tool. 🔧 Tech Used Language : Python 3.13 Libraries : os · sys · time · schedule Concepts : File Automation · Scheduling · Log Generation 📂 GitHub → https://lnkd.in/gK-rhJMw #Python #Automation #OpenSource #GitHub #ProblemSolving #PythonDeveloper #SoftwareDevelopment #PythonProjects

If you’re still managing users, roles, subscriptions, and cache updates manually, you’re spending time on work that should already be automated. Tomorrow, we’re showing Strategy administrators how to change that. Task automation with Python for Strategy administrators 📅 April 17 ⏰ 12:00 PM EST In this session, you’ll learn how to use Python and mstrio-py to turn repetitive admin tasks into secure, scalable workflows. → Automate user, group, and role management → Programmatically manage subscriptions and cache updates → Run scripts in Strategy Workstation or server-side in MCE → Build reusable, production-ready automation Less manual work. Fewer errors. More time for what actually matters. Join us tomorrow: https://ow.ly/aCfy50YF2BS #SemanticLayer #PythonAutomation #DataOps #AnalyticsEngineering #WorkflowAutomation

Workflow Experiment Tracking using snakemake #machinelearning #datascience #workflowexperimenttracking #snakemake Workflow management system to create reproducible and scalable data analyses Snakemake is a workflow management system that aims to reduce the complexity of creating workflows by providing a fast and comfortable execution environment, together with a clean and modern specification language in python style. Snakemake workflows are essentially Python scripts extended by declarative code to define rules. Rules describe how to create output files from input files. https://lnkd.in/gm-Xzv34