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 gestão de armazenamento na AWS sempre exigiu uma escolha difícil: a escalabilidade e o baixo custo do Amazon S3 (Object Storage) ou a facilidade de montagem e baixa latência do Amazon EFS (File Storage). Para aplicações legadas ou fluxos de trabalho que dependem de comandos de sistema de arquivos nativos, essa escolha muitas vezes significava reescrita de código ou custos elevados de infraestrut
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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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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
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