GPT-5.2 Quietly Beats Human Experts at Knowledge Work
OpenAI releases GPT-5.2, enhancing professional tasks with improved speed and cost-effectiveness, now available for paid users in ChatGPT and via API.
Updates and recent posts about AIStor..
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@laura_garcia shared a post, 2 months, 1 week ago
Software Developer, RELIANOID
RELIANOID at CII Delhi International Technology Summit 2025
16–17 December 2025 - New Delhi, India Our team continues a packed December schedule, and we’re excited to add another key event: the CII Delhi International Technology Summit 2025. Focused on “Accelerating the Techade”, this summit brings together industry, government, and research leaders to shape..
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Customer Marketing Manager, Last9
OTel Updates: OpenTelemetry Proposes Changes to Stability, Releases, and Semantic Conventions
OpenTelemetry proposes stability changes: stable-by-default distributions, decoupled instrumentation, and epoch releases for production deployments.
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@laura_garcia shared a post, 2 months, 1 week ago
Software Developer, RELIANOID
deploy the RELIANOID Load Balancer Community Edition v7 on Azure using Terraform
🚀 New Technical Guide Available! You can now deploy the RELIANOID Load Balancer Community Edition v7 on Azure using Terraform in just a few minutes: ✔️ Install prerequisites (Terraform, Azure CLI, SSH keys) ✔️ Use the official Terraform module from the Registry ✔️ Automatically provision all Azure r..
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@tairascott gave 🐾 to Helm 4 or Nelm? What's the difference , 2 months, 1 week ago.
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@tairascott gave 🐾 to Hidden Correlations Traditional Monitoring Misses , 2 months, 1 week ago.
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@anjali shared a link, 2 months, 1 week ago
Customer Marketing Manager, Last9
How to Track Down the Real Cause of Sudden Latency Spikes
Sudden latency spikes rarely have a single cause. This blog shows how to uncover the real source using traces, histograms, and modern debugging signals.
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@anjali shared a link, 2 months, 1 week ago
Customer Marketing Manager, Last9
Hidden Correlations Traditional Monitoring Misses
Last9 is built to work with high-cardinality telemetry, and we’ve been covering it in detail through our series. This piece looks at a familiar pain: issues that only show up for a specific tenant or deployment. Why does that context disappear in most monitoring setups?
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.
