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@varbear shared a link, 1 month, 1 week ago
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The challenges of soft delete

"Soft delete" sounds gentle. It isn't. Slapping adeleted_atcolumn on every table pollutes queries, drags down migrations, and leaves tombstones all over production. This post digs into saner options:PostgreSQL triggers,event archiving in the app layer, andCDC via WAL. Each separates the dead stuff f.. read more  

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@kaptain shared a link, 1 month, 1 week ago
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Experimenting with Gateway API using kind

A new guide shows how to runGateway APIlocally withkindandcloud-provider-kind. It spins up a one-node Kubernetes cluster in Docker - complete with LoadBalancer Services and a Gateway API controller. Cloud vibes, zero cloud bill. Fire it up to deploy demo apps, test routing, or poke around with CRD e.. read more  

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@kaptain shared a link, 1 month, 1 week ago
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Ingress NGINX: Statement from the Steering and Security Response Committees

Kubernetes is cutting offIngress NGINXin March 2026. No more updates. No bug fixes. No security patches. Done. Roughly half of cloud-native setups still rely on it, but it's been understaffed for years. If you're one of them, it's time to move. There’s no plug-and-play replacement, but the ecosystem.. read more  

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@kaptain shared a link, 1 month, 1 week ago
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Cluster API v1.12: Introducing In-place Updates and Chained Upgrades

Cluster API v1.12.0 addsin-place updatesandchained upgrades, so machines can swap parts without going down, and clusters can jump versions without drama. KubeadmControlPlaneandMachineDeploymentsnow choose between full rollouts or surgical patching, depending on what changed. The goal: keep clusters .. read more  

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@kaptain shared a link, 1 month, 1 week ago
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Run a Private Personal AI with Clawdbot + DMR

Clawdbot just plugged intoDocker Model Runner (DMR). That means you can now run your own OpenAI-compatible assistant, locally, on your hardware. No cloud. No per-token fees. No data leaking into the void!.. read more  

Run a Private Personal AI with Clawdbot + DMR
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@kaptain shared a link, 1 month, 1 week ago
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New Conversion from cgroup v1 CPU Shares to v2 CPU Weight

A new quadratic formula now mapscgroup v1 CPU sharestocgroup v2 CPU weight. Why? Because the old linear approach messed with CPU fairness; especially at low share values. This fix nails prioritization where it counts. It lands at theOCI runtime layer, live inrunc v1.3.2andcrun v1.23, so containers f.. read more  

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@kala shared a link, 1 month, 1 week ago
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AWS Frontier Agents: Kiro, DevOps Agent, and Security Agent

“Frontier Agents” drop straight into incident workflows. They kick off investigations on their own, whether triggered by alarms or a human hand, pulling together logs, metrics, and deployment context fast. Findings show up where they’re needed: Slack threads, tickets, operator dashboards. No shell c.. read more  

AWS Frontier Agents: Kiro, DevOps Agent, and Security Agent
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@kala shared a link, 1 month, 1 week ago
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Keeping 20,000 GPUs healthy

Modal unpacked how it keeps a 20,000+ GPU fleet sane across AWS, GCP, Azure, and OCI. Think autoscaling, yes, but with some serious moves behind the curtain. They're running instance benchmarking, enforcing machine image consistency, running boot-time checks, and tracking GPU health both passively a.. read more  

Keeping 20,000 GPUs healthy
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@kala shared a link, 1 month, 1 week ago
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Securing Agents in Production (Agentic Runtime, #1)

Palantir's AIP Agentic Runtime isn't just another agent platform, it's a control plane with teeth. Think tight policy enforcement, ephemeral autoscaling with Kubernetes (Rubix), and memory stitched in from the jump viaOntology. Tool usage? Traced and locked down with provenance-based security. Every.. read more  

Securing Agents in Production (Agentic Runtime, #1)
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@kala shared a link, 1 month, 1 week ago
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Is that allowed? Authentication and authorization in Model Context Protocol

TheModel Context Protocol (MCP) 2025-11-25spec tightens up remote agent auth. It leans intoOAuth 2.1 Authorization Code grants, PKCE required, step-up auth backed. No token passthrough allowed. What’s new: experimental extensions forclient credentialsandclient ID metadata. These smooth out agent reg.. read more  

Is that allowed? Authentication and authorization in Model Context Protocol
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.