Join us

ContentUpdates and recent posts about AIStor..
Link
@faun shared a link, 4 months, 3 weeks 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, 4 months, 3 weeks 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, 4 months, 3 weeks 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, 4 months, 3 weeks 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, 4 months, 3 weeks 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, 4 months, 3 weeks 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, 4 months, 3 weeks ago
FAUN.dev()

Becoming a Research Engineer at a Big LLM Lab - 18 Months of Strategic Career Development

To land a big career role like Mistral, mix efficient **tactical** moves (like LeetCode practice) with **strategic** ups, like building a powerful portfolio and a solid network. Balance is key; aim to impress and prepare well without overlooking the power of strategy in shaping a successful career... read more  

Link
@faun shared a link, 4 months, 3 weeks ago
FAUN.dev()

Building a Natural Language Interface for Apache Pinot with LLM Agents

MiQ plugged **Google’s Agent Development Kit** into their stack to spin up **LLM agents** that turn plain English into clean, validated SQL. These agents speak directly to **Apache Pinot**, firing off real-time queries without the usual parsing pain. Behind the scenes, it’s a slick handoff: NL2SQL .. read more  

Building a Natural Language Interface for Apache Pinot with LLM Agents
Link
@faun shared a link, 4 months, 3 weeks ago
FAUN.dev()

Implementing Vector Search from Scratch: A Step-by-Step Tutorial

Search is a fundamental problem in computing, and vector search aims to match meanings rather than exact words. By converting queries and documents into numerical vectors and calculating similarity, vector search retrieves contextually relevant results. In this tutorial, a vector search system is bu.. read more  

Link
@faun shared a link, 4 months, 3 weeks ago
FAUN.dev()

5 Free AI Courses from Hugging Face

Hugging Face just rolled out a sharp set of free AI courses. Real topics, real tools—think **AI agents, LLMs, diffusion models, deep RL**, and more. It’s hands-on from the jump, packed with frameworks like LangGraph, Diffusers, and Stable Baselines3. You don’t just read about models—you build ‘em i.. 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.