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Securing the Museum of Software in an AI Coding Tsunami

In Securing the Museum of Software in an AI Coding Tsunami, Eran Kinsbruner argues that software now consists of legacy, modern, and rapidly AI-generated code, creating unprecedented complexity and risk. Traditional AppSec can’t keep up with machine-speed development. He calls for a unified, developer-first, agentic AppSec platform that embeds security into coding workflows to prevent, fix, and secure all code eras before vulnerabilities reach repositories.

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Founder, FAUN.dev

100 GitHub Projects That Defined 2025: A Community-Driven Ranking

This article ranks the 100 developer tools developers acted on most in 2025, based on real interaction data from across FAUN·dev() ecosystem.

100 GitHub Projects That Defined 2025
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@laura_garcia shared a post, 2 months ago
Software Developer, RELIANOID

🔐🌱 Cybersecurity and industrial sustainability: a moment to reflect as the year comes to an end

We shared this article a few months ago, but year-end is the perfect time to revisit it and reflect on where the industry is heading in the year ahead. Cybersecurity and sustainability can no longer be treated as separate disciplines. They share a common goal: ensuring ethical, resilient, and respon..

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Enshittification is not a bug

Docker Helm Kubernetes

Bitnami charts are still high quality, but their public image distribution is going away. Instead of rewriting everything, many teams can keep the charts and switch the underlying images (for example, to Docker Hardened Images) to minimize disruption and maintain security.

Bitnami vs Docker Hardened Images
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@laura_garcia shared a post, 2 months, 1 week ago
Software Developer, RELIANOID

🚀 Deploy RELIANOID CE v7 on AWS with Terraform

Quickly deploy RELIANOID Community Edition v7 on AWS using the official Terraform module. ✔️ VPC, Subnet & Security Group ✔️ EC2 with RELIANOID AMI ✔️ SSH & Web GUI ready ✔️ Easy cleanup with terraform destroy ⚠️ AMI is region-specific (default: us-east-1) 🔐 Always secure your SSH private key #Terra..

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@viktoriiagolovtseva shared a post, 2 months, 1 week ago

How To Create a Jira Test Case Template To Boost Efficiency

Many agile teams prefer Jira for managing test cases. Even though it’s not a dedicated tool, it provides a straightforward way to organize the testing process, track progress, and share results with stakeholders. Additionally, it enhances collaboration between QA and development teams.

Using test case templates in Jira allows you to manage this process even more efficiently. These templates save time, promote standardization, and provide a structured foundation for test execution. In this short tutorial, I will show you how to create a Jira test case template and use it with automation to simplify your testing process.

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@laura_garcia shared a post, 2 months, 1 week ago
Software Developer, RELIANOID

🔐 RELIANOID & NIST Cybersecurity Framework Alignment

At RELIANOID, security is built into both our Load Balancer and our internal operations. We align our product and organizational practices with the NIST Cybersecurity Framework (CSF) across its five core functions: Identify, Protect, Detect, Respond, and Recover. ✔️ Consistent security controls acro..

NIST Cybersecurity Framework RELIANOID compliance
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FAUN.dev()

Goodbye Microservices

Twilio Segment collapsed 140+ destination-specific microservices into asingle monolith, one repo, one set of dependencies, one test harness. They leveled out version sprawl and builtTraffic Recorder, a homegrown yakbak-based HTTP playback tool. That killed off hours-long test runs, dropping them to.. read more  

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Why I Didn’t Sign the Resonant Computing Manifesto: The Foundations Need Work

A sharp critique of theResonant Computing Manifestopushes it past vague ideals. It calls for real governance scaffolding, not just poetic prose. Without that? The manifesto risks becoming just another glossy PDF for entrenched players to wave around while changing nothing. Under the hood:What’s real.. read more  

Why I Didn’t Sign the Resonant Computing Manifesto: The Foundations Need Work
BigQuery is a cloud-native, serverless analytics platform designed to store, query, and analyze massive volumes of structured and semi-structured data using standard SQL. It separates storage from compute, automatically scales resources, and eliminates the need for infrastructure management, indexing, or capacity planning.

BigQuery is optimized for analytical workloads such as business intelligence, log analysis, data science, and machine learning. It supports real-time data ingestion via streaming, batch loading from cloud storage, and federated queries across external data sources like Cloud Storage, Bigtable, and Google Drive.

Query execution is distributed and highly parallel, enabling interactive performance even on petabyte-scale datasets. The platform integrates deeply with the Google Cloud ecosystem, including Looker for BI, Vertex AI for ML workflows, Dataflow for streaming pipelines, and BigQuery ML, which allows users to train and run machine learning models directly using SQL.

Built-in security features include fine-grained IAM controls, column- and row-level security, encryption by default, and audit logging. BigQuery follows a consumption-based pricing model, charging for storage and queries (on-demand or reserved capacity).