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@ilobe added a new tool Weights & Biases , 1 week, 5 days ago.
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VP of Product Marketing, http://checkmarx.com

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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@eon01 shared a post, 1 week, 6 days ago
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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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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@eon01 shared a post, 2 weeks, 2 days ago
Founder, FAUN.dev

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 weeks, 5 days 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 weeks, 5 days 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.

Zrzut ekranu 2025-12-23 155342
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@derynleigh started using tool Snyk , 2 weeks, 5 days ago.
LangChain is a modular framework designed to help developers build complex, production-grade applications that leverage large language models. It abstracts the underlying complexity of prompt management, context retrieval, and model orchestration into reusable components. At its core, LangChain introduces primitives like Chains, Agents, and Tools, allowing developers to sequence model calls, make decisions dynamically, and integrate real-world data or APIs into LLM workflows.

LangChain supports retrieval-augmented generation (RAG) pipelines through integrations with vector databases, enabling models to access and reason over large external knowledge bases efficiently. It also provides utilities for handling long-term context via memory management and supports multiple backends like OpenAI, Anthropic, and local models.

Technically, LangChain simplifies building LLM-driven architectures such as chatbots, document Q&A systems, and autonomous agents. Its ecosystem includes components for caching, tracing, evaluation, and deployment, allowing seamless movement from prototype to production. It serves as a foundational layer for developers who need tight control over how language models interact with data and external systems.