Updates and recent posts about AIStor..

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@faun shared a link, 1 year ago

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AWS Lambda announces native support for Avro and Protobuf formatted Kafka events

AWS Lambdanow natively supportsAvroandProtobufformatted Kafka events, dancing through schema chaos with Glue and Confluent registries. Toss custom deserialization in the trash; plug inPowertoolsand let open-source Kafka consumer interfaces do the grunt work... read more

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@faun shared a link, 1 year ago

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Terraform AWS provider 6.0 now generally available

Terraform AWS Provider 6.0bursts onto the scene with multi-region support. Now, devs can tweak 32 config files in one shot, slimming down memory bloat. 🌍💻.. read more

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@faun shared a link, 1 year ago

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On Azure’s new SRE Agent

Microsoft's shinySRE Agentwades into network snafus with swagger but makes some bold, perplexing claims—like leaning on faulty data insights for fixes. Slick demos dazzle, yet its "approve and act" zeal might lure newbies into rash decisions. Handle with care!.. read more

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@faun shared a link, 1 year ago

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Load Testing with Impulse at Airbnb

Impulselets Airbnb teams wreak havoc in the best way possible. It makes load testing in Java/Kotlin a breeze. No need to call in the cavalry. It just mocks what it needs to and spins up a frenzy of pseudo-real traffic... read more

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@faun shared a link, 1 year ago

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alden: detachable terminal sessions without breaking scrollback

Tired of losing terminal sessions and scrollback with tools liketmux,screen, ormosh? A new tool calledaldenkeeps your SSH shell alive after disconnects without breaking your native terminal scrollback. Unlike other solutions, it avoids emulating a terminal—so you get seamless reconnection and keep y.. read more

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@faun shared a link, 1 year ago

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Why Environments Beat Clusters For Dev Experience

Developers chasepromotions, not the tedium of deployments. Environments should reign supreme—not just a lone Kubernetes cluster hogging the spotlight.Real-time insights? They zoom past those outdated, siloed CI pipelines... read more

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@faun shared a link, 1 year ago

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Amazon VPC raises default Route Table capacity

AWS VPClets your inner network architect cheer:500 routes per tablenow. That’s a cool 10x boost from before, turning network scaling from a headache into a child's play. 🚀.. read more

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@faun shared a link, 1 year ago

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Inside Google’s Agent2Agent (A2A) Protocol: Teaching AI Agents to Talk to Each Other

Agent2Agent (A2A)is the new gospel for AI agents, taking over as the universal translator across platforms. Imagine 50+ tech behemoths waving its banner. A2A, clutchingJSON-RPC 2.0 over HTTP(S), crafts a chat apocalypse for AI, wiping out the custom integration chaos, much like the venerableInternet.. read more

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@faun shared a link, 1 year ago

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Lenovo introduces new AI-optimized data center systems

Lenovo'sThinkSystem SR680a V4doesn't just perform—it explodes with AI power, thanks to Nvidia'sB200GPUs. We're talking4nmchips with a mind-boggling208 billion transistors. Boost? Try11x... read more

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@faun shared a link, 1 year ago

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How to Build an Asynchronous AI Agent Network Using Gemini for Research, Analysis, and Validation Tasks

The Gemini Agent Network Protocol introduces powerful AI collaboration with four distinct roles. Leveraging Google’s Gemini models, agents communicate dynamically for improved problem-solving... read more

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.