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@kaptain shared a link, 5 days, 12 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, 5 days, 12 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, 5 days, 12 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, 5 days, 13 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, 5 days, 13 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, 5 days, 13 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, 5 days, 15 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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@kala shared a link, 5 days, 15 hours ago
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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
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@kala shared a link, 5 days, 15 hours ago
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OpenClaw’s Skill Marketplace and the Emerging AI Supply Chain Threat

Unit 42 researchers found five malicious ClawHub skills that attackers had designed to pass the marketplace's post-incident automated checks... read more  

OpenClaw’s Skill Marketplace and the Emerging AI Supply Chain Threat
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@devopslinks shared a link, 5 days, 15 hours ago
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IaC Isn't Dying. AI Makes it More Important

Teams that use AI to generate infrastructure code need IaC as the system of record that platform teams govern. Engineers can produce changes faster, so platform teams must absorb more work through review, policy, testing, integration, and rollout... read more  

IaC Isn't Dying. AI Makes it More Important
Unsloth is an open-source toolkit for training and fine-tuning large language models faster and with less memory than a standard Hugging Face stack. Its core library replaces PyTorch's default autograd with custom backpropagation kernels written in OpenAI's Triton language, which is where most of its speed and memory savings come from. It supports LoRA, QLoRA, full fine-tuning, reinforcement learning, pretraining, and 4-bit, 16-bit, and FP8 training, across more than 500 text, vision, audio, and embedding models.

The practical draw is hardware reach. QLoRA workflows in Unsloth let you fine-tune an 8B model on a single 12 GB consumer GPU, and the project headlines roughly 2x faster training with about 70 percent less VRAM versus baseline implementations, though the exact figures vary by model, GPU, and config. A 2026 update added faster mixture-of-experts training, with models like Qwen3-30B-A3B fine-tunable on about 17.5 GB of VRAM. It runs on NVIDIA (including Blackwell and DGX Spark), AMD, and Intel GPUs, with free Colab and Kaggle notebooks for trying it without local hardware.

It fits cleanly into the local-AI workflow. Unsloth integrates with Hugging Face transformers and TRL, and uses llama.cpp to save and run models, exporting to GGUF for Ollama or LM Studio as well as safetensors. As of 2026 it also ships Unsloth Studio, a local no-code GUI that covers the full lifecycle from dataset creation to training to running and comparing GGUF and safetensors models, with tool-calling, web search, and an OpenAI-compatible API, all running offline on Mac and Windows, with the core library under the Apache 2.0 license.