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@laura_garcia shared a post, 1ย week, 2ย days ago
Software Developer, RELIANOID

๐—๐—ฎ๐—ฝ๐—ฎ๐—ป ๐—œ๐—ง & ๐——๐—ซ ๐—ช๐—ฒ๐—ฒ๐—ธ!

๐Ÿš€ ๐—›๐—ฒ๐—ฎ๐—ฑ๐—ถ๐—ป๐—ด ๐˜๐—ผ ๐—ง๐—ผ๐—ธ๐˜†๐—ผ ๐—ณ๐—ผ๐—ฟ ๐—๐—ฎ๐—ฝ๐—ฎ๐—ป ๐—œ๐—ง & ๐——๐—ซ ๐—ช๐—ฒ๐—ฒ๐—ธ! ๐—ฅ๐—˜๐—Ÿ๐—œ๐—”๐—ก๐—ข๐—œ๐—— will be at the 23rd Information Security Expo Spring 2026 from April 8โ€“10 at Tokyo Big Sight โ€“ ๐—๐—ฎ๐—ฝ๐—ฎ๐—ปโ€™๐˜€ ๐—น๐—ฎ๐—ฟ๐—ด๐—ฒ๐˜€๐˜ ๐˜€๐—ต๐—ผ๐˜„๐—ฐ๐—ฎ๐˜€๐—ฒ ๐—ณ๐—ผ๐—ฟ ๐—ฐ๐˜†๐—ฏ๐—ฒ๐—ฟ๐˜€๐—ฒ๐—ฐ๐˜‚๐—ฟ๐—ถ๐˜๐˜† ๐˜€๐—ผ๐—น๐˜‚๐˜๐—ถ๐—ผ๐—ป๐˜€. Come see how our advanced ADC and secure application delivery solutions help protect critical infr..

japan it dx week april 26 relianoid
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@laura_garcia shared a post, 1ย week, 3ย days ago
Software Developer, RELIANOID

Maritime Cybersecurity Is Still Too Weak โ€“ And the Risks Are Growing

๐Ÿšข Maritime Cybersecurity Is Still Too Weak โ€“ And the Risks Are Growing As ships become smarter, greener, and more connected, their cyber defenses remain worryingly outdated. ๐Ÿ“‰ Over 80% of shipowners have faced cyberattacks in the past 3 years ๐Ÿ’ธ Average cost per attack: $3.1 million ๐ŸŽฃ Phishing causes..

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@pramod_kumar_0820 shared a link, 1ย week, 4ย days ago
Software Engineer, Teknospire

Java 26 Released ๐Ÿš€: Whatโ€™s New, What Matters & Why Itโ€™s Faster Than Ever

Java 26 (March 2026) is out, and while itโ€™s not a headline-heavy release, it brings meaningful improvements where it counts โ€” performance, networking, and concurrency.

Some notable updates:

๐ŸŒ HTTP/3 support (QUIC-based, lower latency, better reliability)

๐Ÿงต Structured Concurrency (Preview) for safer multithreading

โšก JVM & GC optimizations improving startup and runtime performance

๐Ÿง  Continued evolution of pattern matching

๐Ÿงช Vector API (Incubator) for high-performance workloads

This release is less about flashy features and more about incremental improvements that impact real-world systems.

java_26_released_version
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@mmaksimovic shared a link, 1ย week, 4ย days ago

Monitoring Your App Without Running Your Own Prometheus Stack

When to use Prometheus and when to look for other solutions.

Monitoring Your App Without Running Your Own Prometheus Stack
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@tellsaqib shared a link, 1ย week, 4ย days ago

How Cloudways is manages its 90K servers fleet using Agentic SRE

Scaling Autonomous Site Reliability Engineering: Architecture, Orchestration, and Validation for a 90,000+ Server Fleet

How Cloudways is manages its 90K servers fleet using Agentic SRE
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@kala shared an update, 1ย week, 6ย days ago
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Anthropic Asked 81,000 People What They Want From AI. Here's What They Said.

#Studyย  #researc...ย  #Anthrop...ย  #Claudeย  #AIย 
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 , 1ย week, 6ย days ago.
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@varbear shared a link, 1ย week, 6ย days ago
FAUN.dev()

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 ย 

The Slow Collapse of MkDocs
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@varbear shared a link, 1ย week, 6ย days ago
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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 ย 

How we monitor internal coding agents for misalignment
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@varbear shared a link, 1ย week, 6ย days ago
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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 ย 

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 AIStorโ€™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 โ€” from unstructured storage for application data to structured table storage for analytics, as well as AI training and inference datasets โ€” within a consistent object-native architecture. It supports multi-tenant environments and can be deployed across on-premises, cloud, and hybrid infrastructure.