Join us

ContentUpdates and recent posts about AIStor..
Story
@laura_garcia shared a post, 7 months ago
Software Developer, RELIANOID

Asia Hits 50% IPv6 Capability — A Global Milestone

- Asia has reached a major internet milestone: 50% of its systems are now IPv6 capable, positioning the region as a global leader in IPv6 user adoption. - Why this matters: - India (78.1%) and China (810M users) are driving this growth. - Historical IPv4 scarcity in Asia helped fuel early IPv6 inves..

Blog Asia reaches 50 percent IPv6 capability
Story
@laura_garcia shared a post, 7 months ago
Software Developer, RELIANOID

🚀 RELIANOID is heading to it-sa Expo&Congress 2025!

📍 Nuremberg, Germany | October 7–9, 2025 🔒 Europe’s largest IT security event with 900+ exhibitors, expert talks & global networking. We’ll be there to showcase how RELIANOID helps businesses stay ahead of evolving cyber threats. 👉 See you in Nuremberg! Send us a DM to make an appointment. #itSa2025..

itsa nuremberg
Link
@faun shared a link, 7 months ago
FAUN.dev()

Building a Resilient Data Platform with Write-Ahead Log at Netflix

Netflix faced challenges like data loss, system entropy, updates across partitions, and reliable retries. To address these, they built a generic Write-Ahead Log (WAL) system serving a variety of use cases like delayed queues, generic cross-region replication, and multi-partition mutations. WAL abstr.. read more  

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

Organize your Slack channels by “How Often”, not “What” - Aggressively Paraphrasing Me

One dev rewired their Slack setup by **engagement frequency**—not subject. Channels got sorted into tiers like “Read Now” and “Read Hourly,” cutting through noise and saving brainpower. It riffs off the **Eisenhower Matrix**, letting priorities shift with projects, not burn people out... read more  

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

Privacy for subdomains: the solution

A two-container setup using **acme.sh** gets Let's Encrypt certs running on a Synology NAS—thanks, Docker. No built-in Certbot support? No problem. Cloudflare DNS API token handles auth. Scheduled tasks handle renewal... read more  

Privacy for subdomains: the solution
Link
@faun shared a link, 7 months ago
FAUN.dev()

Users Only Care About 20% of Your Application

Modern apps burst with features most people never touch. Users stick to their favorite 20%. The rest? Frustration, bloat, ignored edge cases. Tools like **VS Code**, **Slack**, and **Notion** nail it by staying lean at the core and letting users stack what they need. Extensions, plug-ins, integrati.. read more  

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

Authentication Explained: When to Use Basic, Bearer, OAuth2, JWT & SSO

Modern apps don’t just check passwords—they rely on **API tokens**, **OAuth**, and **Single Sign-On (SSO)** to know who’s knocking before they open the door... read more  

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

Uncommon Uses of Common Python Standard Library Functions

A fresh guide gives old Python friends a second look—turns out, tools like **itertools.groupby**, **zip**, **bisect**, and **heapq** aren’t just standard; they’re slick solutions to real problems. Think run-length encoding, matrix transposes, or fast, sorted inserts without bringing in another depen.. read more  

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

Writing Load Balancer From Scratch In 250 Line of Code

A developer rolled out a fully working **Go load balancer** with a clean **Round Robin** setup—and hooks for dropping in smarter strategies like **Least Connection** or **IP Hash**. Backend servers live in a custom server pool. Swapping balancing logic? Just plug into the interface... read more  

Writing Load Balancer From Scratch In 250 Line of Code
Link
@faun shared a link, 7 months ago
FAUN.dev()

The productivity paradox of AI coding assistants

A July 2025 METR trial dropped a twist: seasoned devs using Cursor with Claude 3.5/3.7 moved **19% slower** - while thinking they were **20% faster**. Chalk it up to AI-induced confidence inflation. Faros AI tracked over **10,000 developers**. More AI didn’t mean more done. It meant more juggling, .. read more  

The productivity paradox of AI coding assistants
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.