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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
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
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This article provides a step by step deployment guide for using Amazon Bedrock models with ADK Agents. This project aims to configure an ADK agent to use an Amazon Bedrock model. LiteLLM is an open-source AI gateway and Python SDK that provides a unified OpenAI-compatible interface to over 100 LLMs (Anthropic, Gemini, Azure, Bedrock, Ollama). It simplifies API management by allowing users to call
What's new Based on early user feedback, Permi can now save your vulnerability scan results in three distinct formats to fit your workflow: --export results.txt – Human-readable plain text for quick reviews. --export results.json – Structured data designed for scripts and CI/CD automation. --export results.md – Clean Markdown, perfect for GitHub documentation or internal wikis. To try out the ne
Most "chat with your website" projects ship without any measurement. Mine did too. The live demo was up, answers looked plausible, and I moved on. Then I built a proper evaluation harness and found out exactly how wrong "looks plausible" is as a quality signal. This post covers the eval design, the bugs it caught, the prompt changes that fixed most of them, and the two metrics that still don't pas
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