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@kaptain shared a link, 3 months, 4 weeks ago
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The “Inception” of Kubernetes: A Deep Dive into vCluster Architecture and Benefits

vCluster, a CNCF sandbox project, spins up real-deal Kubernetes control planes inside pods. Each lives in its own namespace but behaves like a full cluster, admin access, CRDs, Helm, the works. It reuses the host’s worker nodes using a syncer that routes vCluster workloads onto the real thing... read more  

The “Inception” of Kubernetes: A Deep Dive into vCluster Architecture and Benefits
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@kaptain shared a link, 3 months, 4 weeks ago
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How to Troubleshoot Common Kubernetes Errors

A fresh Kubernetes troubleshooting guide lays out real-world tactics for tracking down 12 common cluster headaches. Think:kubectlsleuthing, poking through system logs, scraping observability metrics, and jumping intodebug containers. The guide breaks down howAIOpsis stepping in, digesting event data.. read more  

How to Troubleshoot Common Kubernetes Errors
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@jamesmiller shared a post, 3 months, 4 weeks ago

Automating Penetration Testing in CI/CD: A Practical Guide for Developers

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Automating pentesting in CI/CD helps developers catch vulnerabilities early, reduce MTTR, and keep releases secure without slowing the pipeline. This guide breaks down why automation matters, the tools developers rely on, common mistakes to avoid, and practical steps to build a reliable pentesting workflow inside modern CI/CD pipelines.

Automating Penetration Testing in CI/CD
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@elenamia shared a post, 3 months, 4 weeks ago
Technical Consultant, Damco Solutions

Google Cloud Services: A Comprehensive Overview for Modern Businesses

Read this blog to learn about Google Cloud Platform services and its key features, pricing, and use cases across industries.

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@kala shared a link, 3 months, 4 weeks ago
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How to Create an Effective Prompt for Nano Banana Pro

The author details how to effectively prompt Google’s Nano Banana Pro, a visual reasoning model, emphasizing that success relies on structured design documents rather than vague requests. The method prioritizes four key steps: defining the Work Surface (e.g., dashboard or comic), specifying the prec.. read more  

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@kala shared a link, 3 months, 4 weeks ago
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So you wanna build a local RAG?

Skald spun up a full local RAG stack, withpgvector,Sentence Transformers,Docling, andllama.cpp, in under 10 minutes. The thing hums on English point queries. Benchmarks show open-source models and rerankers can go toe-to-toe with SaaS tools in most tasks. They stumble, though, on multilingual prompt.. read more  

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@kala shared a link, 3 months, 4 weeks ago
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Learning Collatz - The Mother of all Rabbit Holes

Researchers trained small transformer models to predict the "long Collatz step," an arithmetic rule for the infamous unsolved Collatz conjecture, achieving surprisingly high accuracy up to 99.8%. The models did not learn the universal algorithm, but instead showed quantized learning, mastering speci.. read more  

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@kala shared a link, 3 months, 4 weeks ago
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200k Tokens Is Plenty

Amp’s team isn’t chasing token limits. Even with ~200k available via Opus 4.5, they stick toshort, modular threads, around 80k tokens each. Why? Smaller threads are cheaper, more stable, and just work better. Instead of stuffing everything into a single mega-context, they slice big tasks into focuse.. read more  

200k Tokens Is Plenty
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@kala shared a link, 3 months, 4 weeks ago
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Google tests new Gemini 3 models on LM Arena

Google’s been quietly field-testing two shadow models,Fierce FalconandGhost Falcon, on LM Arena. Early signs? They're probably warm-ups for the next Gemini 3 Flash or Pro drop. Classic Google move: float a checkpoint, stir up curiosity, then go GA... read more  

Google tests new Gemini 3 models on LM Arena
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@kala shared a link, 3 months, 4 weeks ago
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Roses are red, violets are blue, if you phrase it as poem, any jailbreak will do

A new study just broke the safety game wide open: rhymed prompts slipped past filters in25 major LLMs, including Gemini 2.5 Pro and Deepseek - withup to 100% success. No clever chaining, no jailbreak soup. Just single-shot rhyme. Turns out, poetic language isn’t just for bard-core Twitter. When it c.. read more  

Roses are red, violets are blue, if you phrase it as poem, any jailbreak will do
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.