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@kala shared a link, 2 months, 1 week ago
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Introducing structured output for Custom Model Import in Amazon Bedrock

Amazon Bedrock’s Custom Model Import just got structured output support. Now LLMs can lock their responses to your JSON schema - no prompt hacks, no cleanup after... read more  

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@kala shared a link, 2 months, 1 week ago
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The Fatal Math Error Killing Every AI Architecture - Including The New Ones

LLMs are fading as JEPA (Joint Embedding Predictive Architecture) emerges with joint, embedding, predictive architecture. JEPA is a step towards true intelligence by avoiding the flat, finite spreadsheet trap of Euclidean space and opting for a toroidal model... read more  

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@kala shared a link, 2 months, 1 week ago
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LaTeX, LLMs and Boring Technology 

LLMs are tearing down LaTeX's old walls. Syntax hell, cryptic errors, clunky formatting - easier now. Whether baked into editors or running solo, these models smooth the pain. Why does it work so well? LaTeX has history. Mountains of examples. It's the perfect training set. That puts newer contender.. read more  

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@kala shared a link, 2 months, 1 week ago
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Building a Healthcare Robot from Simulation to Deployment with NVIDIA Isaac

NVIDIA just droppedIsaac for Healthcare v0.4, and it’s a big one. Headliner: the newSO-ARM starter workflow- a full-stack sim2real pipeline built for surgical robotics. It covers the whole loop: spin up synthetic and real-world data capture, train withGR00t N1.5, and deploy straight to 6-DOF hardwar.. read more  

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@devopslinks shared a link, 2 months, 1 week ago
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Visibility at Scale: How Detects Sensitive Data Exposure

Segment gutted its old permissions table—bloated, slow, tangled in logic - and replaced it with a lean, service-based setup. The new stack runs onPostgres,Redis, and a sharply tunedGo API, cutting query times from 1400ms to under 100ms. Clean, fast, and centralized... read more  

Visibility at Scale: How Detects Sensitive Data Exposure
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@devopslinks shared a link, 2 months, 1 week ago
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Terraform vs. Pulumi vs. Crossplane: Choosing the right IaC Tool for your platform

Terraform, Pulumi, and Crossplane take very different routes to Infrastructure as Code.Terraformsticks to a declarative HCL model with a massive provider ecosystem.Pulumiflips the script—developers write infrastructure in real languages, so logic is testable and dynamic.Crossplane? It runs inside Ku.. read more  

Terraform vs. Pulumi vs. Crossplane: Choosing the right IaC Tool for your platform
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@devopslinks shared a link, 2 months, 1 week ago
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How Google, Amazon, and CrowdStrike broke millions of systems

AWS. Google Cloud. Azure. CrowdStrike. All hit hard by dumb bugs with big blast radii - race conditions, nulls, misfired configs. Small cracks. Massive fallout. AWS's DNS automation knocked out its DynamoDB endpoint, dragging 113 services down with it. Google Cloud’s global APIs fell over from a str.. read more  

How Google, Amazon, and CrowdStrike broke millions of systems
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@devopslinks shared a link, 2 months, 1 week ago
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Notes on switching to Helix from vim

Helix keeps things lean - and that's the point. It ships withLSP support, multi-cursor editing, and smart search baked in. No dotfile gymnastics required. That alone has peeled some loyalists off Vim and Neovim. Still rough around the edges. No persistent undo. No auto-reload. Markdown support's a b.. read more  

Notes on switching to Helix from vim
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@devopslinks shared a link, 2 months, 1 week ago
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Creating VMs in separate ZFS filesystems

A dev split KVM/QEMU VMs out of a shared ZFS directory and into their own ZFS filesystems. Why? Snapshot rollbacks. Finer-grained storage control. Clean. The new setup rides a fresh ZFS pool tuned with a 64KBrecordsizefor QCOW2 images. That lines up virtual disk performance with the real IO under th.. read more  

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@laura_garcia shared a post, 2 months, 1 week ago
Software Developer, RELIANOID

EU's Cybersecurity standards for IoT devices

🔒 The EU enforces strict cybersecurity standards for IoT devices: securing networks, protecting privacy, and preventing fraud. At RELIANOID, we share this open-source commitment to resilience — helping organizations build safer, more reliable digital ecosystems. #CyberSecurity#IoT#OpenSource#Digital..

Blog IoT Security RELIANOID
vLLM is an advanced open-source framework for serving and running large language models efficiently at scale. Developed by researchers and engineers from UC Berkeley and adopted widely across the AI industry, vLLM focuses on optimizing inference performance through its innovative PagedAttention mechanism — a memory management system that enables near-zero waste in GPU memory utilization. It supports model parallelism, continuous batching, tensor parallelism, and dynamic batching across GPUs, making it ideal for real-world deployment of foundation models. vLLM integrates seamlessly with Hugging Face Transformers, OpenAI-compatible APIs, and popular orchestration tools like Ray Serve and Kubernetes. Its design allows developers and enterprises to host LLMs with reduced latency, lower hardware costs, and increased throughput, powering everything from chatbots to enterprise-scale AI services.