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@viktoriiagolovtseva shared a post, 1 month, 4 weeks ago

A Git and Jira Integration Guide: How to Connect GitHub, GitLab, and Bitbucket to Jira Cloud

If you ask a developer, product manager, and QA “why integrate your Git repository with Jira,” they will all give different answers. Some like it for reducing context switching and providing automation options, while others value the transparency and improved traceability. But no matter who you ask, everyone is unanimous: this integration is immensely useful for the teams.

In this article, we focus on the most popular Git applications: GitHub, GitLab, and Bitbucket. We explain how to connect them to Jira Cloud and provide you with practical tips on making the most out of this integration. You will learn how to use smart commits, leverage automation, and gain extra value from third-party apps.

Story

@ashwinisdave shared a post, 1 month, 4 weeks ago

Developer advocate, Middleware

From Metrics to Meaning: Building Context-Aware Dashboards That Actually Help Debug Production Issues

#DevOps #AI Dash... #observa... #log mon...

Most dashboards show what's happening but not why it matters. Learn how to build context-aware dashboards that actually help engineers debug production issues faster.

Story Keploy Team

@sancharini shared a post, 1 month, 4 weeks ago

Why Understanding Software Testing Basics Is Essential for Every Developer?

Understand why software testing basics is essential for every developer. Learn key testing types, levels, techniques, and best practices to write reliable, maintainable, and high-quality code.

Course

@eon01 published a course, 1 month, 4 weeks ago

Founder, FAUN.dev

Practical MCP with FastMCP & LangChain

#LangCha... #Model C... #FastMCP #AI agen...

Engineering the Agentic Experience

Activity

@kala added a new tool FastMCP , 1 month, 4 weeks ago.

News FAUN.dev() Team

@kala shared an update, 1 month, 4 weeks ago

FAUN.dev()

FastMCP 3.0 Released: Community-Driven Enhancements Unveiled

#FastMCP #FastMCP... #Prefect... #AI #MCP

FastMCP 3.0 is now generally available. It keeps the @mcp.tool() API but rebuilds the internals around components + providers + transforms, adds a CLI, and ships production features like component versioning, per-component auth + OAuth additions, OpenTelemetry tracing, background tasks, pagination, tool timeouts, and hot reload. The project moved from jlowin/fastmcp to PrefectHQ/fastmcp on GitHub, and upgrading is supported via dedicated guides for FastMCP 2 and MCP SDK users.

FastMCP 3.0 Released: Community-Driven Enhancements Unveiled

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@laura_garcia shared a post, 2 months ago

Software Developer, RELIANOID

🚀 Ready to level up your #AppSec skills?

Join us at London OWASP Training Days 2026 – February 25–28 in London! Hands-on, instructor-led sessions covering: 🔹 API Security 🔹 Secure Development & Testing 🔹 Threat Modeling & Risk Analysis 🔹 AI & Security 🔹 Mobile & IoT Security 🔹 Offensive Security & Pentesting Learn from the global OWASP com..

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@laura_garcia shared a post, 2 months ago

Software Developer, RELIANOID

Finance: Resilience. Trust. Continuity

Downtime isn’t just costly—it’s a trust killer. 💸 In financial services, outages can cost millions, invite regulatory penalties, and damage customer confidence. Our latest blog dives into the true cost of downtime and why resilience, security, and compliance must be non-negotiable. #FinancialServic..

Blog_Why Financial Services Institutions Must Protect Themselves From Downtime_RELIANOID

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@dwisiswant0 shared a post, 2 months ago

The most practical, fast, tiny command sandboxing for AI agents

#sandbox #command... #AI agen... #AI agen... #AI agen...

Need to run one sketchy command without a full container? Here is the most practical, lightweight way to lock down one risky command in your AI pipeline. No daemon, no root, no image build.

Story

@laura_garcia shared a post, 2 months ago

Software Developer, RELIANOID

🚀 Deploy RELIANOID Community Edition v7 on Microsoft Azure using Terraform.

⚡ Infrastructure ready in minutes ⚡ Official Terraform module ⚡ Fully automated Azure deployment Simple. Fast. Reproducible. #Terraform#Azure#DevOps#IaC#LoadBalancer#CloudInfrastructure#RELIANOID https://www.relianoid.com/resources/knowledge-base/community-edition-v7-administration-guide/deploy-reli..

terraform_relianoid_community_azure_img2

AIStor is an enterprise-grade, high-performance object storage platform built for modern data workloads such as AI, machine learning, analytics, and large-scale data lakes. It is designed to handle massive datasets with predictable performance, operational simplicity, and hyperscale efficiency, while remaining fully compatible with the Amazon S3 API. AIStor is offered under a commercial license as a subscription-based product.

At its core, AIStor is a software-defined, distributed object store that runs on commodity hardware or in containerized environments like Kubernetes. Rather than being limited to traditional file or block interfaces, it exposes object storage semantics that scale from petabytes to exabytes within a single namespace, enabling consistent, flat addressing of vast datasets. It is engineered to sustain very high throughput and concurrency, with examples of multi-TiB/s read performance on optimized clusters.

AIStor is optimized specifically for AI and data-intensive workloads, where throughput, low latency, and horizontal scalability are critical. It integrates broadly with modern AI and analytics tools, including frameworks such as TensorFlow, PyTorch, Spark, and Iceberg-style table engines, making it suitable as the foundational storage layer for pipelines that demand both performance and consistency.

Security and enterprise readiness are central to AIStor’s design. It includes capabilities like encryption, replication, erasure coding, identity and access controls, immutability, lifecycle management, and operational observability, which are important for mission-critical deployments that must meet compliance and data protection requirements.

AIStor is positioned as a platform that unifies diverse data workloads — from unstructured storage for application data to structured table storage for analytics, as well as AI training and inference datasets — within a consistent object-native architecture. It supports multi-tenant environments and can be deployed across on-premises, cloud, and hybrid infrastructure.