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@kaptain shared a link, 1 day, 6 hours ago
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Kubernetes QoS vs. Linux Cgroups: The Mixed-Resource Pod Risk

Designing Kubernetes manifests with mixed configurations can lead to unpredictability in how resources are managed between containers. This is due to the different ways Kubernetes and Linux handle requests, limits, and OOM situations. To avoid operational risks and ensure stability, it is crucial to.. read more  

Kubernetes QoS vs. Linux Cgroups: The Mixed-Resource Pod Risk
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When failover isn’t safe: Building high-availability PostgreSQL on Kubernetes

Datadog made PostgreSQL failover safer by treating replica lag as the promotion gate. A zonal-failure gameday showed that detection and automation could not protect the database if the standby sat behind the primary. The team added lag-aware checks, clearer operator signals, and failure drills so en.. read more  

When failover isn’t safe: Building high-availability PostgreSQL on Kubernetes
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@kaptain shared a link, 1 day, 6 hours ago
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How Netflix Simplified Batch Compute with Kueue

Netflix migratedmillions of batch jobsfrom their custom queuing system toKueue, a cloud-native job queueing system, as part of transitioning to a more Kubernetes-native infrastructure. Kueue offers features such as preemption, fair sharing, and hierarchical tenants that were missing in their homegro.. read more  

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@kaptain shared a link, 1 day, 6 hours ago
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The feedback loops behind Kubernetes

Kubernetes operatoris a closed feedback loop that ensures desired state for running workloads, similar to a thermostat's control. Operators automate manual tasks in managing databases like Postgres, improving efficiency by comparing and converging states. The same loop structure in a Bash script can.. read more  

The feedback loops behind Kubernetes
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@kaptain shared a link, 1 day, 6 hours ago
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What job interviews taught me about Kubernetes

The recent shift towards Kubernetes adoption can be attributed to the benefits of uniform deployment, standardized knowledge, and traceability it offers. With managed K8s services maturing and Helm simplifying deployment, more companies are choosing Kubernetes regardless of their technical needs. Th.. read more  

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@kala shared a link, 1 day, 7 hours ago
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Build real agentic apps using CUGA: two dozen working examples on a lightweight harness

CUGA*, the Agent Harness for the Enterprise from IBM, streamlines agent building by handling planning, execution loop, tool calls, and state plumbing. Using it, you focus on defining tools and prompts while the rest is taken care of, leading to efficient agent development without needing to learn a .. read more  

Build real agentic apps using CUGA: two dozen working examples on a lightweight harness
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@kala shared a link, 1 day, 7 hours ago
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How LLMs Actually Work

This post covers the core mechanisms inside modern transformer-based LLMs, including tokens, embeddings, positional encoding, attention, multi-head attention, and more. Tokenization converts text into integer IDs, embeddings give tokens meaning through vectors, and positional encoding helps the mode.. read more  

How LLMs Actually Work
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@kala shared a link, 1 day, 7 hours ago
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Don't let the LLM speak, just probe it

When an LLM reads "here's some text, here's a criterion - does it satisfy it?", the answer often already exists in its hidden state before it generates a single token. So skip generation entirely: grab the hidden state at the last prompt token (~70% of the way up the model's layers), feed it to a ti.. read more  

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@kala shared a link, 1 day, 9 hours ago
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7,000 Langflow servers are under attack. LangGraph and LangChain have the same holes

Three popular AI agent frameworks had major vulnerabilities, from SQL injection to path traversal, allowing attackers to gain full remote code execution and access sensitive data. Exploits were publicly disclosed, and patches have been released for each framework... read more  

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Introducing Claude Tag

Anthropic's Claude Tag beta gives Slack teams a shared agent they can tag in a channel, assign tasks to, and connect to approved tools. Teams gain three practical benefits: - Claude can keep channel context, so teammates avoid re-explaining project history. - Admins can scope memory and tool access .. read more  

Introducing Claude Tag
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.