Join us

ContentUpdates and recent posts about AIStor..
Story
@laura_garcia shared a post, 3 months, 2 weeks ago
Software Developer, RELIANOID

🔐 Preparing for the Post-Quantum Era

Quantum computing is no longer a distant threat — it’s a coming reality that could render today’s encryption obsolete. Post-Quantum Cryptography (PQC) is the next frontier in securing digital infrastructure, and organizations need crypto agility to adapt fast. At RELIANOID, we’re already building fo..

Link
@varbear shared a link, 3 months, 2 weeks ago
FAUN.dev()

Dead framework theory

LLM dev tools keep steering straight into React. System prompts hardwire it. Generated code defaults to it. The result? A feedback loop. Tools crank out React, models get trained on more React, and newcomers inherit the bias. What’s changing:React isn’t just a framework anymore. It’s sliding into in.. read more  

Dead framework theory
Link
@varbear shared a link, 3 months, 2 weeks ago
FAUN.dev()

From web developer to database developer in 10 years

EnterpriseDB is leveling upPostgres Distributed, the spiritual successor topglogical. Now withreplicated DDLandtunable consistencyacross clusters. It's mostly C and Rust under the hood - tight hooks into Postgres internals, with APIs that nod at abstraction but stay close to the core... read more  

Link
@varbear shared a link, 3 months, 2 weeks ago
FAUN.dev()

The Green Tea Garbage Collector

Go 1.25 drops an experimental GC calledGreen Tea. It flips the script on object traversal - scanning memory pages instead of hopping from object to object. The payoff? Up to40% less GC CPU overheadon real workloads. Bonus: it taps intoAVX-512on newer x86 chips forvectorized scanning. Turns out strea.. read more  

The Green Tea Garbage Collector
Link
@varbear shared a link, 3 months, 2 weeks ago
FAUN.dev()

The state of the Rust dependency ecosystem

A deep dive into 200,650 Rust crates shows a brewing maintenance problem:45% are inactive, andover half of new crates never see a second update- a wild jump from just 1.4% in 2015. Zoom in on the top 1,000 crates, and it gets messier.249 dependencies have been abandoned, and158 are stuck on older ma.. read more  

Link
@varbear shared a link, 3 months, 2 weeks ago
FAUN.dev()

AI's 70% Problem

Google’s Addy Osmani dropped a stat: AI now writesover 30% of the codeat Google. Impressive. But the hard part - the last 30% - still needs a human brain. That’s where the bugs live:security, edge cases, production wiring. No shortcut. And while AI adoption keeps climbing in greenfield work,trust is.. read more  

AI's 70% Problem
Link
@kaptain shared a link, 3 months, 2 weeks ago
FAUN.dev()

LinkPro: eBPF rootkit analysis

A new stealth rootkit calledLinkProjust surfaced, taking aim at AWS-hosted Linux boxes. It blends two customeBPF programsfor deep concealment and remote activation via magic packets. The path in?CVE-2024-23897—an RCE on a public Jenkins server. From there, attackers slipped into Amazon EKS clusters,.. read more  

LinkPro: eBPF rootkit analysis
Link
@kaptain shared a link, 3 months, 2 weeks ago
FAUN.dev()

Manage Secrets of your Kubernetes Platform at Scale with GitOps

Learn how to manage secrets with the External Secrets Operator and plug it into Argo CD to power your Internal Developer Platform without manual management, enabling self-service secrets management and secure connections between workload clusters and the control plane. With a chain of trust between .. read more  

Link
@kaptain shared a link, 3 months, 2 weeks ago
FAUN.dev()

Kubernetes with Buildkite: faster, simpler, and ready for scale

Buildkite just added a major revamp of its Kubernetes Agent Stack. Highlights:REST-based config,leaner K8s objects, andhardened security defaults. It handlestens of thousands of concurrent jobswithout breaking a sweat. Shared environment vars cut down pod config noise. Error messages come with full .. read more  

Kubernetes with Buildkite: faster, simpler, and ready for scale
Link
@kaptain shared a link, 3 months, 2 weeks ago
FAUN.dev()

How Airbnb Runs Distributed Databases on Kubernetes at Scale

Airbnb runs distributed databases across multiple Kubernetes clusters - each tied to its own AWS Availability Zone. That setup isolates failures down to individual pods and keeps the whole system highly available. They built a custom Kubernetes operator and leaned on EBS volumes with PVCs to smooth .. read more  

How Airbnb Runs Distributed Databases on Kubernetes at Scale
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.