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LLM Optimization: LoRA and QLoRA

Learn how LoRA and QLoRA make it possible to fine-tune huge language models on modest hardware. Discover the adapter approach for scaling LLMs to new tasks—and why quantization is the next step in efficient model training... read more  

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Rust is Officially in the Linux Kernel

Rust struts into Linux 6.15with the flashy entrance of theNOVA DRM driver.No subtlety here; it's gunning for Nvidia's crown and promising a jetpack for those next-gen RTX beasts. Meanwhile,Fornjot CADwhispers sweet nothings to tinkerers: model anything your Rusty heart desires, straight from code to.. read more  

Rust is Officially in the Linux Kernel
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How To Structure Terraform Deployments At Scale

Scalrcracks the admin whip with a sharp, flexible hierarchy inTerraform. It lets dev teams roam free in their own isolated territories. Usinginheritance and reusable moduleslike a seasoned parent, it kills bottlenecks and spikes code efficiency... read more  

How To Structure Terraform Deployments At Scale
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GitHub MCP Exploited: Accessing private repositories via MCP

Invariantplayed detective and unearthed a gaping hole in GitHub MCP. This flaw lets sneaky attackers hijack agents using malicious GitHub issues, spilling private repo secrets all over the public domain. Fortify your agent systems: clamp down on access and deployInvariant Guardrailsalong withMCP-sca.. read more  

GitHub MCP Exploited: Accessing private repositories via MCP
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Building a Distributed Cache for S3

ClickHouse Cloud's distributed cache obliterates latency woes:Compute nodes band together, sharing cached data, and sidestepping S3 molasses. They rev up speed by4x, even during chilly starts. Cold starts and storage re-reads? Forget 'em. Welcome to the fast lane... read more  

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Cloud Service Mesh in 2025 — global control, zero pain upgrades

Google Cloud Service Meshpulls togetherAnthos Service MeshandTraffic Directorto handle routing, security, and observability without tweaking your app code. By 2025, Managed CSM will chop down operational headaches. It lets clusters stretch across clouds without fuss. Security's beefed up—think bulle.. read more  

Cloud Service Mesh in 2025 — global control, zero pain upgrades
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Chat with your AWS Bill

Chat up yourAWS billusing Amazon Q CLI. Get savvy cost optimization tips and let MCP untangle tricky questions—like how much your EBS storage is bleeding you dry... read more  

Chat with your AWS Bill
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Software Delivery Governance and Compliance, but make it automated

Regulated industries wrestle with compliance hassles. They crave efficiency, not endless, mind-numbing audit checklists.Koslisteps in with a bold solution: ahorizontal tech approach. Why? At their core, software risks wear the same uniform across sectors—consider code peer-reviews and release contro.. read more  

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How I use LLMs as a staff engineer

Copilotexcels at boilerplate code—think of it as a whiz-kid intern. But when tackling complex logic, it stumbles. EnterLLMs: masters of non-production code, boosting your workflow like black coffee... read more  

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Why is your Google Cloud SQL bill so high?

Point-in-time recoveryin Cloud SQL canballoonover 2 TiB of WAL logs from just 13 GiB of data. That hike in storage blows up costs quickly. Fine-tune your settings to trim the fat and save some cash... read more  

Why is your Google Cloud SQL bill so high?
Kueue is a Kubernetes-native job queueing and workload management system designed for large-scale, mixed compute environments such as AI/ML training, batch workloads, and HPC workflows. Instead of scheduling individual Pods, Kueue operates at the job level, deciding when a job should run based on resource quotas, fair-sharing policies, cluster availability, and workload priorities.

Kueue integrates tightly with Kubernetes, working alongside the default scheduler rather than replacing it. It provides features such as all-or-nothing (gang) admission, workload preemption, quota-based sharing across teams or tenants, and support for advanced frameworks like JobSet and Ray. Its goal is to help Kubernetes clusters run efficiently under heavy load while ensuring that critical, latency-sensitive, or large training jobs receive the resources they need without starving lower-priority workloads.