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@devopslinks shared a link, 11 hours ago
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Why does SSH send 100 packets per keystroke? ·

The default macOS SSH client now floods connections withSSH2_MSG_PING “chaff” packets- a 2023 privacy tweak meant to hide keystroke timing. Nice in theory. In practice? It tanks performance for real-time terminal apps like games built on Bubbletea over SSH. Turning it off - either through client fla.. read more  

Why does SSH send 100 packets per keystroke? ·
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@devopslinks shared a link, 11 hours ago
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From Paging to Postmortem: Google Cloud SREs on Using Gemini CLI for Outage Response

Google Cloud SREs just leveled up their incident response game with theGemini CLI- an LLM-fueled terminal sidekick built onGemini 3. It jumps in fast: drafts mitigation playbooks, digs into root causes, and cranks out postmortem reports. All withhuman-in-the-loopguardrails to keep things sane... read more  

From Paging to Postmortem: Google Cloud SREs on Using Gemini CLI for Outage Response
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@kala shared an update, 14 hours ago
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GitHub Launches Copilot SDK to Embed Agentic AI into Any Application

GitHub Copilot GitHub Copilot SDK

GitHub has released the Copilot SDK in technical preview, allowing developers to embed Copilot’s agentic execution loop into their own applications. The SDK supports multiple AI models, real-time streaming, and languages like Python, TypeScript, Go, and .NET, but currently requires a Copilot subscription and is intended for development and testing rather than production use.

GitHub Launches Copilot SDK to Embed Agentic AI into Any Application
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@varbear shared an update, 14 hours ago
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VillageSQL Launches: A Drop-In MySQL Fork Bringing Extensions and AI to the Core

MySQL VillageSQL

VillageSQL is a drop-in, open-source fork of MySQL that introduces a true extension framework, enabling permissionless innovation for AI-era workloads. It allows developers to add custom data types and functions - with vector indexing and search on the roadmap - bringing MySQL closer to PostgreSQL-style extensibility without waiting for core upstream changes.

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@kala added a new tool GitHub Copilot SDK , 14 hours, 54 minutes ago.
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@varbear added a new tool VillageSQL , 15 hours, 1 minute ago.
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@devopslinks shared an update, 16 hours ago
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MinIO Ends Community Development, Positions AIStor as the Future

MinIO AIStor

MinIO has marked its open-source GitHub repository as "THIS REPOSITORY IS NO LONGER MAINTAINED," effectively ending active community development. The company is shifting focus to AIStor, its subscription-based enterprise object storage platform. The code remains available under AGPLv3, but future innovation and support are centered on the commercial product.

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@devopslinks added a new tool AIStor , 17 hours, 33 minutes ago.
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@gbdhodh-glitch started using tool Python , 1 day, 3 hours ago.
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@abdelbxl started using tool Windows Server , 1 day, 15 hours ago.
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.