A defaced website is a curious problem. It's loud — anyone visiting the page can see something is wrong. But it's also quiet from a server's perspective: HTTP returns 200, your uptime monitor is happy, your TLS cert hasn't moved, and the CMS logs show a "successful" content update from a legitimate-looking session. The signal is on the rendered page, not in the metrics. I run a site at hi3ris.blue
A follow-up: how the architecture works In my previous article, I explained why I built NGB Platform and what problem it is trying to solve: I Built an Open-Source Platform Foundation for Accounting-Centric Business Apps That article was mostly about the why. Why generic web frameworks are not enough for serious business applications. Why large ERP products solve many of the right problems, but
You just ran a dependency scan and the report shows 133 vulnerabilities. 34 are Critical. 68 are High. The dashboard is red, the backlog is exploding, and every item looks urgent. The engineering team asks the obvious question: where do we start? This is where vulnerability remediation prioritization matters. Without a clear framework, teams either panic and chase the loudest CVE, or they ignore t
We've been there. JSON Schema gets hard to write as soon as your payload is non-trivial. Conditional logic, cross-field rules, business invariants, and at some point we stop writing contracts at all. We go code-first, generate the schema from annotations, and end up with 200 lines very few understand, and error messages referencing paths like #/properties/items/allOf/0/then/Then that map to nothin
Metric Value Django Average Response Time 287ms Node.js Average Response Time 193ms Django Memory Usage (1000 users) 1.8GB We tested Django 4.2 and Node.js 18.16 under identical conditions to measure their performance for reporting dashboard workloads. The test environment consisted of AWS EC2 m5.2xlarge instances (8 vCPUs, 32GB RAM) running Ubuntu 22.04. Both frameworks connected to th
If you've ever built ETL pipelines pulling data from MongoDB into Delta Lake using Spark, you've probably hit this wall. The pipeline works fine — until it doesn't. A single document with an unexpected shape is enough to break the entire write, leave the table in an inconsistent state, and send your on-call engineer digging through Spark logs at 11pm. I built and maintained more than 10 of these j
War Story: Ditching MongoDB 6 for PostgreSQL 17 Saved Our 50M User App from Data Inconsistencies Our social commerce platform hit 50 million monthly active users in Q3 2024, powering 1.2 million daily transactions across 30 countries. For three years, we’d run on MongoDB 6, lured by its flexible document model and horizontal scaling. But by mid-2024, data inconsistencies were threatening our cor
I've been on both sides of the data engineering hiring table for years. I've written interview loops, failed interview loops, and watched candidates ace screens that told me absolutely nothing about whether they could debug a silent data loss bug at 2am. The signal was always thin. Now it's basically noise. Here's the situation in 2026: 64% of companies ban AI in interviews. Candidates use it anyw