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Software Developer, RELIANOID

💡 Third-Party Vendors: The Hidden Cybersecurity Risk

In today’s hyper-connected world, digital supply chains are only as secure as their weakest link. One single vendor can open the door to ransomware, outages, or worse. At RELIANOID, we take this risk seriously. 🔒 That’s why we apply: ✅ Continuous vendor risk assessments ✅ Real-time monitoring of thi..

cropped-Blog-THIRD-PARTY-VENDOR-RISKS-RELIANOID
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Unconventional PostgreSQL Optimizations

PostgreSQL 18 now supportsvirtual generated columns, indexable expressions without burning storage. Perfect for standardizing queries in analytics-heavy pipelines. Pair that withplanner constraint exclusion(constraint_exclusion=on), and Postgres can dodge irrelevant table scans based on constraints... read more  

Unconventional PostgreSQL Optimizations
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Software engineering when machine writes the code

In 1968, computer scientists identified the "software crisis" - the existing methods of programming were struggling to handle the power of computers. Today, AI coding assistants are accelerating productivity, but concerns arise about understanding the code they generate, the implications for debuggi.. read more  

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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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How I Taught GitHub Copilot Code Review to Think Like a Maintainer

Vibe coding has made contributing to open source easier, but the high number of contributions to the AI agent framework goose has posed a challenge. An AI Code Review agent like Copilot can help review PRs, but tuning its feedback is crucial for reducing noise and increasing value. By providing clea.. read more  

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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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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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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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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
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.