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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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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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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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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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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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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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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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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
GPT-5.4 is OpenAI’s latest frontier AI model designed to perform complex professional and technical work more reliably. It combines advances in reasoning, coding, tool use, and long-context understanding into a single system capable of handling multi-step workflows across software environments. The model builds on earlier GPT-5 releases while integrating the strong coding capabilities previously introduced with GPT-5.3-Codex.

One of the defining features of GPT-5.4 is its ability to operate as part of agent-style workflows. The model can interact with tools, APIs, and external systems to complete tasks that extend beyond simple text generation. It also introduces native computer-use capabilities, allowing AI agents to operate applications using keyboard and mouse commands, screenshots, and browser automation frameworks such as Playwright.

GPT-5.4 supports context windows of up to one million tokens, enabling it to process and reason over very large documents, long conversations, or complex project contexts. This makes it suitable for tasks such as analyzing codebases, generating technical documentation, working with large spreadsheets, or coordinating long-running workflows. The model also introduces a feature called tool search, which allows it to dynamically retrieve tool definitions only when needed. This reduces token usage and makes it more efficient to work with large ecosystems of tools, including environments with dozens of APIs or MCP servers.

In addition to improved reasoning and automation capabilities, GPT-5.4 focuses on real-world productivity tasks. It performs better at generating and editing spreadsheets, presentations, and documents, and it is designed to maintain stronger context across longer reasoning processes. The model also improves factual accuracy and reduces hallucinations compared with previous versions.

GPT-5.4 is available across OpenAI’s ecosystem, including ChatGPT, the OpenAI API, and Codex. A higher-performance variant, GPT-5.4 Pro, is also available for users and developers who require maximum performance for complex tasks such as advanced research, large-scale automation, and demanding engineering workflows. Together, these capabilities position GPT-5.4 as a model aimed not just at conversation, but at executing real work across software systems.