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@kala shared an update, 1 week, 2 days ago
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Anthropic Asked 81,000 People What They Want From AI. Here's What They Said.

Claude Code Claude

Anthropic's global AI study surveyed 80,508 participants across 159 countries, revealing desires for more personal time and concerns about AI's unreliability and job displacement. Sentiments vary regionally, with lower-income countries seeing AI as an equalizer, while Western Europe and North America focus on governance issues. The study highlights a complex mix of hope and fear regarding AI's impact.

Anthropic Asked 81,000 People What They Want From AI. Here's What They Said.
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@kala added a new tool Claude , 1 week, 2 days ago.
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The Slow Collapse of MkDocs

On March 9, 2026 a former maintainer grabbed the PyPI package forMkDocs. The original author's rights got stripped. Ownership snapped back within six hours. Core development stalled for 18 months.Material for MkDocswent into maintenance. The ecosystem splintered intoProperDocs,MaterialX, andZensical.. read more  

The Slow Collapse of MkDocs
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How we monitor internal coding agents for misalignment

AI systems are acting with more autonomy in real-world settings, with OpenAI focusing on responsibly navigating this transition to AGI by building capable systems and developing monitoring methods to deploy and manage them safely. OpenAI has implemented a monitoring system for coding agents to learn.. read more  

How we monitor internal coding agents for misalignment
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How Slack Rebuilt Notifications

At Slack, notifications were redesigned to address the overwhelming noise issue by simplifying choices and improving controls. The legacy system had complex preferences that made it difficult for users to understand and control notifications. Through a collaborative effort, the team refactored prefe.. read more  

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Why I Vibe in Go, Not Rust or Python

In a world where the machine writes most of the code, Python lacks solid type enforcement, Rust is overly strict with complex lifetimes, while Go strikes the right balance by catching critical issues without hindering development velocity. The article argues in favor of Go over Python and Rust for A.. read more  

Why I Vibe in Go, Not Rust or Python
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@varbear shared a link, 1 week, 2 days ago
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What if Python was natively distributable?

The Python ecosystem's insistence on solving multiple problems when distributing functions has led to unnecessary complexity. The dominant frameworks have fused orchestration into the execution layer, imposing constraints on function shape, argument serialization, control flow, and error handling. W.. read more  

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AWS Load Balancer Controller Reaches GA with Kubernetes Gateway API Support

AWS ships GAGateway APIsupport in theAWS Load Balancer Controller. Teams can manageALBandNLBwith the SIG standard. The controller swaps annotation JSON for validated CRDs -TargetGroupConfiguration,LoadBalancerConfiguration,ListenerRuleConfiguration- and handles L4 (TCP/UDP/TLS) and L7 (HTTP/gRPC). M.. read more  

AWS Load Balancer Controller Reaches GA with Kubernetes Gateway API Support
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@kaptain shared a link, 1 week, 2 days ago
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A one-line Kubernetes fix that saved 600 hours a year

Atlantis, a tool for planning and applying Terraform changes, faced slow restarts of up to 30 minutes due to a safe default in Kubernetes that became a bottleneck as the persistent volume used by Atlantis grew to millions of files. After investigation, a one-line change to fsGroupChangePolicy reduce.. read more  

A one-line Kubernetes fix that saved 600 hours a year
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@kaptain shared a link, 1 week, 2 days ago
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Trivy Hack Spreads Infostealer via Docker, Triggers Worm and Kubernetes Wiper

Cybersecurity researchers found malicious artifacts distributed via Docker Hub after the Trivy supply chain attack. Malicious versions 0.69.4, 0.69.5, and 0.69.6 of Trivy were removed from the image library. Threat actor TeamPCP targeted Aqua Security's GitHub organization, compromising 44 repositor.. read more  

Trivy Hack Spreads Infostealer via Docker, Triggers Worm and Kubernetes Wiper
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.