Table of Contents Introduction Environment Requirements Core Features Core Design and Code Analysis Actual Execution Demo Architecture Overview How You Can Expand Future Plans & Conclusion What is this It is a basic debugger, running on Linux and implemented in C++, aiming to create a debugger that is easy to read and expand. In addition, Lavender's main function is to help users analyze the logic
The Reality Check ClickHouse just dropped a study that every executive should read: LLMs are great at some things, but basing your infrastructure on them? Too much, too soon. They tested five leading models (Claude Sonnet 4, GPT-o3, GPT-4.1, Gemini 2.5 Pro, and the newly released GPT-5) against real observability scenarios. The verdict? We're nowhere near the autonomous operations future Silicon
The 800 Million Weekly ChatGPT Users Who Are Just Getting Started Here's something that should excite everyone: ChatGPT just hit 800 million weekly active users. That's one in ten humans on Earth. Adoption faster than the world wide web. 18 billion messages every single week. And the really wild part: we haven't even scratched the surface of what's possible. OpenAI's latest research shows that ~
The Autonomous Paradox In 2026, we’ve moved past simple chatbots. We are building Production-Grade RAG pipelines and autonomous agents that can plan, execute, and iterate. But as an architect, I’ve noticed a glaring hole in our "Agentic" future: Identity Sprawl. We are giving agents non-human identities (NHI) with "Full Admin" permissions just to ensure the RAG works smoothly. We are effectively
Introduction It's Black Friday. In the space of a single second, your e-commerce platform processes 4,000 orders, updates inventory counts, triggers fulfillment workflows, and debits customer accounts. Every one of those operations lands in your OLTP database, fast, atomic, precise. None of it, in that same second, tells you that customers are abandoning their carts at three times the normal rat
You've likely heard that "Data is the new oil". But raw oil is useless without a refinery. In the world of Big Data, Apache Spark is that refinery. Whether it's millisecond-level fraud detection or processing terabytes of logs, Spark's ability to handle massive scale with in-memory speed is why it remains a core skill for every ML & Data Engineer. Here are 5 real-world problems and exactly how Spa
What if your Kubernetes cluster simply refused to run unsigned images? I spent some time experimenting with enforcing image provenance in a small Kubernetes setup using MicroK8s. The idea was simple: Only container images with valid cryptographic signatures are allowed to run in the cluster. For this I used: GitLab CI/CD (build + signing pipeline) Cosign / Sigstore (image signing) Kyverno (admissi
Data is no longer treated as a byproduct of business operations and has become one of the most valuable organizational assets. Every interaction on a banking application, e-commerce platform, hospital system, logistics network or social media service generates data continuously. As organizations increasingly adopt digital workflows, cloud platforms, machine learning systems and real-time applicati