Cloudways’ Post

You want to make it easy to correct errors but hard to change the mechanism that enables error correction. This is what states should aspire to.

The real value of problem-solving isn’t just fixing issues—it’s preventing them from coming back. Root causes matter more than quick fixes.

Feature flags are powerful. However, when poorly managed, they create “ghost errors.” ➡️ To avoid them: Remove old flags, Track their activation, Test all states. 👉 Simple, yet rarely implemented. 👉 Discover our complete article https://lnkd.in/dRXc-UPK

Let's learn some terminal tips ✍️ In the terminal, you can use command history to find and reuse your forgotten commands: ➡️ Press Ctrl + R to search your previous commands. Then press Ctrl + O, Enter, or Tab to run the selected one. ➡️ Use !! to run your last executed command instantly.

Here`s a few examples on how our daily devices and operating tools can turn into unpleasant and dangerous incidents. To be able to warn before stuff like this happen is a no brainer ...

One of things we need to care as early as possible. Add retry loops, backoff, and failure detection early. Because your system doesn’t fail when tested — it fails when ignored

RAG vs CAG RAG changed how we build knowledge-grounded systems, but it still has a weakness. Every time a query comes in, the model often re-fetches the same context from the vector DB, which can be expensive, redundant, and slow. Cache-Augmented Generation (CAG) fixes this. It lets the model “remember” stable information by caching it directly in the model’s key-value memory. And you can take this one step ahead by fusing RAG and CAG as depicted below:

Not a week goes by when I help someone struggling for hours with a technical issue that seems impossible to troubleshoot. I always start with basic typo checks, silly mistakes , white space and special characters. Hard to believe but more than 40% issues are solved just in the first level check. Resolution time of an issue is exponentially proportion to the silliness of its root cause. 

High load with high errors isn’t performance — it’s noise. In performance testing, error rate is everything. Even a tiny 0.01% failure can distort your throughput and response times. If the script isn’t stable, the results lie. My learnings to keep error % low (JMeter / LR / k6): - Handle correlations properly - Refresh tokens & sessions - Use enough test data - Add realistic pacing - Validate responses smartly - Add light retry logic What’s your trick to keep errors near zero? 👇

📊 Inconsistent results are usually a symptom — not a surprise. Every outcome points to a system that either enables or limits performance. 🧭 What system needs to be redesigned before 2026?