Updates and recent posts about AIStor..

Posts
Description

Link

@faun shared a link, 11 months, 2 weeks ago

FAUN.dev()

LLM Evaluation Metrics: The Ultimate LLM Evaluation Guide - Confident AI

Dump BLEU and ROUGE. Let LLM-as-a-judge tools like G-Eval propel you to pinpoint accuracy.The old scorers? They whiff on meaning, like a cat batting at a laser dot.DeepEval? It wrangles bleeding-edge metrics with five lines of neat code.Want a personal touch? G-Eval's got your back. DAG keeps benchm.. read more

Link

@faun shared a link, 11 months, 2 weeks ago

FAUN.dev()

Building tiny AI tools for developer productivity

Tiny AI scripts won't make you the next tech billionaire, but they're unbeatable for rescuing hours from the drudgery of repetitive tasks. Whether it's wrangling those dreadedGitHub rollupsor automating the minutiae, these little miracles grant engineers the luxury to actually think... read more

Link

@faun shared a link, 11 months, 2 weeks ago

FAUN.dev()

My Honest Advice for Aspiring Machine Learning Engineers

Becoming a machine learning engineer requires dedicatingat least 10 hours per weekto studying outside of everyday responsibilities. This can take a minimum of two years, even with an ideal background, due to the complexity of the required skills. Understanding core algorithms and mastering the funda.. read more

Link

@faun shared a link, 11 months, 2 weeks ago

FAUN.dev()

Grafana Tempo 2.8 release: memory improvements, new TraceQL features, and more

Grafana Tempo 2.8lands with a bang. Say hello toTraceQL query hints—they bump up results you care about and streamline span searches with parent span IDs. Meanwhile,compactor poolingrevamps slashes memory usage. Kiss those OOM errors goodbye. Important heads-up:serverless features are historyand the.. read more

Link

@faun shared a link, 11 months, 2 weeks ago

FAUN.dev()

Linux 6.16 Performance Regression Tracked Down In New Futex Code

Linux 6.16takes a36% performance nosediveon AMD EPYC 9005 all thanks toFUTEXPRIVATEHASH. The quick fix? Yank it. Engineers scramble for a smarter solution... read more

Link

@faun shared a link, 11 months, 2 weeks ago

FAUN.dev()

Critical Linux “sudo” flaw allows any user to take over the system

Millions of Linux systems are vulnerable to a sudo flaw allowing unauthorized users to run commands as root. The bug affects Ubuntu and Fedora servers, escalates privileges to root, and requires installation of the latest sudo packages for mitigation. The flaw lies in the seldom-used sudo chroot fea.. read more

Link

@faun shared a link, 11 months, 2 weeks ago

FAUN.dev()

Caching is an Abstraction, not an Optimization

Cachingdoes more than rev up performance; it cuts through the chaos of software design, making it tidier and more modular. Sure,LRUandLFUsound like they should open for a prog rock band, but their trusty old formulas stand strong against those wild swings in data access... read more

Link

@faun shared a link, 11 months, 2 weeks ago

FAUN.dev()

Serving 200 million requests per day with a cgi-bin

UsingGoandRustwith CGI-style requests taps into multi-core CPU might, poking fun at long-held CGI inefficiency myths... read more

Link

@faun shared a link, 11 months, 2 weeks ago

FAUN.dev()

Atlassian moved 4 million Postgres databases to AWS Aurora

Atlassianpulled off a major coup, relocating 4 million Jira Postgres databases toAWS Aurora. They slashed expenses by taming CPU beasts and carved out a rock-solid 99.99% uptime. A delightful efficiency cocktail. SamsungandTSMCare brooming through some project cobwebs. Samsung's rethinking its Texas.. read more

Link

@faun shared a link, 11 months, 2 weeks ago

FAUN.dev()

Hewlett Packard Enterprise completes $14B acquisition of Juniper after settlement of DOJ suit

Hewlett Packard Enterprise closed its acquisition of Juniper Networks following the settlement of a lawsuit by the U.S. Department of Justice. This acquisition will allow HPE to expand its networking business and compete in the AI networking market. HPE officials stated that the merger positions the.. 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.