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@kaptain shared a link, 2 months, 1 week ago
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How to Add MCP Servers to ChatGPT

ChatGPT leveled up with fullModel Context Protocol (MCP)support. It can now run real developer tasks, scraping, writing to a database, even making GitHub commits, through secure, containerized tools in Docker. TheDocker MCP Toolkitconnects ChatGPT’s language smarts to production-safe tools like Stri.. read more  

How to Add MCP Servers to ChatGPT
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@kaptain shared a link, 2 months, 1 week ago
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Compose to Kubernetes to Cloud With Kanvas

Docker just droppedKanvas, a new visual toy for building multi-cloud Kubernetes setups, without drowning in YAML. It bolts onto Docker Desktop and runs onMeshery. Drag and drop services into a topology, then bring them to life across AWS, GCP, or Azure. Mix inpolicy-driven validationandreal-time mut.. read more  

Compose to Kubernetes to Cloud With Kanvas
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@kaptain shared a link, 2 months, 1 week ago
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Kubernetes 1.35 - New security features

Kubernetes 1.35 is done with legacy baggage. cgroups v1? Deprecated. Image pull credentials? Now re-verified by default—no more freeloading. kubectl SPDY API upgrades? Locked down. You’ll needcreatepermissions just to speak the protocol. Expect breakage if your workflows leaned on old assumptions. U.. read more  

Kubernetes 1.35 - New security features
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@jamesmiller shared a post, 2 months, 1 week 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, 2 months, 1 week 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, 2 months, 1 week 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, 2 months, 1 week 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, 2 months, 1 week 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, 2 months, 1 week 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, 2 months, 1 week 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
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.