Articles

As AI systems move from experiments to 24/7 production, one question comes up in almost every project: “Which accelerator should we use — GPU, LPU, or TPU?” There is no single best chip. The right choice depends on what kind of AI work you run, how fast decisions must be made, and how the system […]

Introduction: Why LPUs Matter Now In one real-world deployment, an enterprise chatbot running on GPUs showed average response times of ~200 ms during testing—but spiked to over 2–3 seconds during peak hours due to contention and scheduling jitter. At the same time, infrastructure costs scaled almost linearly with traffic, forcing the team to choose between user […]

A Practical Mapping Between Security Language and Software Engineering Concepts Why cybersecurity sounds harder than it actually is Many software developers feel that cybersecurity is a different world: Too many acronyms (SIEM, SOAR, IOC, IDS…) Different vocabulary for things that feel familiar Security people sound like they’re talking about something mysterious The truth is simpler: […]

Building a Modern Cybersecurity Monitoring & Response System A Practical Architecture Using Wazuh, SOAR, and Threat Intelligence Why most security projects fail before they start Many organizations want “better security”, but what they usually get is: Too many alerts, no action Expensive tools nobody understands Security dashboards that look good but don’t protect anything A […]

Why Old Ideas Matter More Than Ever AI can write code faster than any human. It can generate entire modules, refactor files, and suggest fixes in seconds. Yet many teams today are discovering a paradox: The more AI we use, the more classic programming concepts matter.

The POS Designed for Businesses Where Reality Matters Imagine a recycle yard on a busy morning: a truck unloads scrap metal, the operator places it on the scale, weight stabilizes, a receipt prints instantly, and payment is calculated on the spot. There is no spreadsheet, no manual typing, and no argument later about what really […]

“The real problem is that programmers have spent far too much time worrying about efficiency in the wrong places.” — Brian Kernighan Modern programming talks a lot about frameworks, architectures, and tools. Classic programming talks about clarity, simplicity, and thinking.

In many projects, the first conversation starts with a solution: “We need a system.” “We want a dashboard.” “Can you integrate this software with our machines?” At Simplico, we deliberately slow this moment down. Not because we don’t like building software — we do — but because writing code too early is one of the […]

*Applying insights from **Good Strategy / Bad Strategy** to real-world software businesses*

The robot’s name was Unit-7. It moved carefully, spoke slowly, and paused too long between sentences — as if listening to something no one else could hear.

Most automation projects fail not because AI is weak — but because workflow reliability is underestimated. Emails arrive late. Humans respond days later. Systems crash. SAP has no API. If your automation cannot pause, retry, resume, and audit, it will eventually break.

Robotic Process Automation (RPA) promised fast efficiency gains by letting software “robots” mimic human actions. Artificial Intelligence (AI) promised smarter decisions by learning from data. Individually, both technologies delivered value — and also disappointment.

A Systems Approach Using Agent-Based, Network, and System Dynamics Models Modern border conflicts rarely resemble conventional wars. They are persistent, indirect, and system-driven, involving multiple actors, informal resource flows, and adaptive behaviors rather than open military confrontation.

Introduction Universities are investing heavily in digital transformation — learning platforms, AI tools, research analytics, and cloud infrastructure. Yet one critical system is quietly failing its users: the university library experience.

Scrap trading keeps factories efficient and recyclers running — but if you’ve ever dealt with it directly, you know the process is often slower and more manual than it needs to be. Prices are unclear. Coordination takes time. Too much depends on phone calls and personal contacts.

Manufacturers increasingly look to build or customize their own Manufacturing Execution Systems (MES) to gain better control over production, quality, and real-time visibility. While commercial MES platforms exist, many factories choose Python-based development for its flexibility, scalability, and strong ecosystem.

In manufacturing digitalization, confusion often arises around three core systems: ERP (Enterprise Resource Planning) MES (Manufacturing Execution System) SCADA (Supervisory Control and Data Acquisition)

Learning software development is often described as “just practice more” or “keep coding.” But anyone who has tried knows the truth: The pain is not lack of effort — it’s lack of structure. This article breaks down the real pain points people face when learning software development and, more importantly, how to solve them in […]

As AI models rapidly improve, many enterprise leaders are asking the wrong question:

After the popularity of “GPT-5.2 vs 5.1 Explained with Simple Analogies”, many readers asked a natural follow-up question: