Top 7 Observability Platforms That Auto-Discover Services
Auto-discovery tools now detect services as they appear and build dashboards instantly. Here are seven platforms that do it well.
Updates and recent posts about AIStor.. Link
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@cadet15789 shared a link, 3Â months ago
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Cut Your Docker Build Time in Half: 6 Essential Optimization Techniques
Pro tips to write dockerfiles. Cut your build timing of your images by half.
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@laura_garcia shared a post, 3Â months ago
Software Developer, RELIANOID
The JLR Cyber Incident: A Warning Shot for the Automotive Industry
The major cyberattack that halted Jaguar Land Roverâs production for almost six weeks has exposed a hard truth: modern automotive manufacturing is deeply vulnerable to digital disruption. From frozen assembly lines to supplier chaos and regional economic fallout, the incident showed how quickly a si..
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@anjali shared a link, 3Â months ago
Customer Marketing Manager, Last9
What is AWS Fargate for Amazon ECS?
Understand how AWS Fargate runs your ECS containers without serversâjust define CPU, memory, and networking, and AWS handles the compute.
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@shurup shared a post, 3Â months ago
@palark
Helm v4 new features and changes
Helm v4 has been released a week ago. Its highlights are: - Server-Side Apply instead of 3-Way Merge - WASM plugins - Using kstatus for resource tracking - Content-based chart caching This articleprovides a detailed overview of why these changes were made in Helm v4 and what they bring for Helm user..
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@laura_garcia shared a post, 3Â months ago
Software Developer, RELIANOID
âď¸ Ensuring Efficiency and Security in Airport Operations
Today we highlight our main diagram âAirport Software Systemsâ, showcasing how integrated airport management platforms âfrom AODB to landside & airside operations, billing, and information systemsâ work together to ensure efficient and secure airport operations. We also explain how load balancing en..
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@anjali shared a link, 3Â months ago
Customer Marketing Manager, Last9
OTel Updates: Complex Attributes Now Supported Across All Signals
OTLP 1.9.0 adds support for maps, arrays, and byte arrays across all OTel signals. Here's when to use complex attributes and when to stick with flat.
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@laura_garcia shared a post, 3Â months ago
Software Developer, RELIANOID
SOC 2 Compliance
đ˘ At RELIANOID, we follow SOC 2 Trust Service Criteria to ensure Security, Availability, Confidentiality, Processing Integrity, and Privacy across our load balancing solutions â whether on-prem, cloud, or hybrid. Our controls align with the needs of highly regulated environments such as finance, hea..
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@varbear shared a link, 3Â months, 1Â week ago
FAUN.dev()
Chinese Hackers Use Anthropic's AI to Launch Automated Cyber Espionage Campaign
Chinese state-backed threat actorsorchestrated automated cyber attacks using AI technology developed byAnthropicin a highly refinedespionage campaignin mid-September 2025. The attackers leveraged AI to execute 80-90% of tactical operations independently at physically impossible request rates, markin.. read more Â
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@varbear shared a link, 3Â months, 1Â week ago
FAUN.dev()
10 MCP Servers to Optimize Developer Workflows
TheModel Context Protocol (MCP)wires AI agents into real-world dev workflows, think pushing to GitHub, deploying APIs, tweaking Docker, all straight from the code editor. MCP servers like GitHub MCP, Apidog MCP, and Supabase MCP plug into popular tools and infra. They let LLMs update code, ship APIs.. read more Â
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 AIStorâs 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 â from unstructured storage for application data to structured table storage for analytics, as well as AI training and inference datasets â within a consistent object-native architecture. It supports multi-tenant environments and can be deployed across on-premises, cloud, and hybrid infrastructure.
