Join us

ContentUpdates and recent posts about AIStor..
 Activity
@devopslinks added a new tool GitGuardian , 7 months ago.
 Activity
@devopslinks added a new tool detect-secrets , 7 months ago.
 Activity
@devopslinks added a new tool Gitleaks , 7 months ago.
Course
@eon01 published a course, 7 months ago
Founder, FAUN.dev

DevSecOps in Practice

TruffleHog Flask NeuVector detect-secrets pre-commit OWASP Dependency-Check Docker checkov Bandit Hadolint Grype KubeLinter Syft GitLab CI/CD Trivy Kubernetes

A Hands-On Guide to Operationalizing DevSecOps at Scale

DevSecOps in Practice
Story
@tairascott shared a post, 7 months ago
AI Expert and Consultant, Trigma

How Do Large Language Models (LLMs) Work? An In-Depth Look

Discover how Large Language Models work through a clear and human centered explanation. Learn about training, reasoning, and real world applications including Agentic AI development and LLM powered solutions from Trigma.

How do Large Language Models (LLMs) Work Banner
Story
@laura_garcia shared a post, 7 months ago
Software Developer, RELIANOID

🔐 RELIANOID at Gartner IAM Summit 2025 | Dec 8–10, Grapevine, TX

We’re heading to the Gartner Identity & Access Management Summit to showcase how RELIANOID’s intelligent proxy and ADC platforms empower modern IAM: enhancing Zero Trust enforcement, adaptive access, and hybrid/multi-cloud security. Join us to explore AI-driven automation, ITDR, and identity governa..

Gartner Identity and Access Management Summit 2025 relianoid
Link
@varbear shared a link, 7 months ago
FAUN.dev()

Confessions of a Software Developer: No More Self-Censorship

A mid-career dev hits pause after ten years in the game -realizing core skills likepolymorphism, SQL, and automated testingnever quite clicked. Leadership roles, shipping products, mentoring junior devs - none of it filled those gaps. They'd been writingC#/.NETfor a while too. Not out of love, just .. read more  

Confessions of a Software Developer: No More Self-Censorship
Link
@varbear shared a link, 7 months ago
FAUN.dev()

Building a Blockchain in Go: From 'Hello, Block' to 10,000 TPS

A new Go tutorial shows how to build a lean, fast blockchain - clocking ~10,000 TPS - without the usual bloat. It covers the full stack:P2P networking,custom consensus, and properstate management. No unbounded mempools. No missing snapshots. Just a chain that actually runs, benchmarked on real machi.. read more  

Link
@varbear shared a link, 7 months ago
FAUN.dev()

Inside the GitHub Infrastructure Powering North Korea’s Contagious Interview npm Attacks

The Socket Threat Research Team has been following North Korea’s Contagious Interview operation as it targets blockchain and Web3 developers through fake job interviews. The campaign has added at least197 malicious npm packagesand over31,000 downloadssince last report, showcasing the adaptability of.. read more  

Link
@varbear shared a link, 7 months ago
FAUN.dev()

Before You Push: Implementing Quality Gates in Your Software Project

This post discusses best practices for automated testing in software engineering, including unit tests and integration tests for databases, APIs, and emulators. It also covers end-to-end tests using tools like Cypress, Appium, Postman, and more. Additionally, it highlights the importance of environm.. 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.