Join us

ContentUpdates and recent posts about AIStor..
Link
@faun shared a link, 11 months ago
FAUN.dev()

You are in a box

Tools often bloat as users cling to them, terrified of steep costs.Ripgrepand the classicGNUgrep syntax point the way to easier transitions—if we bothered to notice. Simpler shell workflows beckon us. But beware the tug-of-war: unstructured IPC flounders without a schema, andPowerShellcan't get its .. read more  

Link
@faun shared a link, 11 months ago
FAUN.dev()

Zig's New Async I/O

Zig's New I/O Interfacelets users plug in their own I/O designs, cranking up parallelism and taming the chaos of async function spread. Code bloat? Vanquished, thanks to clever virtual tables. And those compiling optimizations? They shave down runtime expense like a barber on a mission... read more  

Zig's New Async I/O
Link
@faun shared a link, 11 months ago
FAUN.dev()

Happy 20th birthday Django! Here’s my talk on Django Origins from Django’s 10th

Django'stale began like any good saga—chock-full of twists. Fast forward20 years, and it's not just surviving; it's flourishing, born from an urgent need to ship projects at breakneck speed... read more  

Happy 20th birthday Django! Here’s my talk on Django Origins from Django’s 10th
Link
@faun shared a link, 11 months ago
FAUN.dev()

Why Your First 100 Bugs Are the Best Thing That Ever Happened to You

Bugsaren't failures; they're mysterious mentors, whispering secrets of logic and nuance that tutorials gloss over. Debugging sharpens your mind into a puzzle-slayer, priming you to catch issues before your code even hits the keyboard. It's not arookie's curse; it's a coder's lifetime gym session... read more  

Why Your First 100 Bugs Are the Best Thing That Ever Happened to You
Link
@faun shared a link, 11 months ago
FAUN.dev()

I’m more proud of these 128 kilobytes than anything I’ve built since

Ditching webfonts for system fonts? A masterstroke. It dodged FOUT and saved bytes by leaning on native beauties likeRobotoorSan Francisco. When juggling a tight128KBbudget, out goes the clunky frameworks. In comesWhizz, a slick custom library that sharpens DOM and AJAX choreography. Images weren't .. read more  

I’m more proud of these 128 kilobytes than anything I’ve built since
Link
@faun shared a link, 11 months ago
FAUN.dev()

How to bring data centre-like connectivity to your home with IPTTTH

IPTTTHin India serves up enterprise-grade connectivity minus the ISP circus. It's like bypassing the bouncer to get inside the exclusive club. Costs? A slippery beast to tame. And those routing snafus? An obstacle course for the faint of heart. Yet, for networking aficionados, it's a ride you won't .. read more  

How to bring data centre-like connectivity to your home with IPTTTH
Link
@faun shared a link, 11 months ago
FAUN.dev()

How does a screen work?

Tiny electric crystalshave dethroned the clunkyelectron guns, quietly transforming digital displays into computing's under-the-radar MVPs. From firing electrons at glowing phosphors to bending light through crystal layers, screens are the overlooked magic powering everything we see. Understanding th.. read more  

Link
@faun shared a link, 11 months ago
FAUN.dev()

Algorithms for making interesting organic simulations

Jeff Jonescrafted an algorithm inspired by the ever-curiousPhysarumslime mold. His system uses agents sniffing out trails and veering according to their strength. But that's just the base model.36 Pointscranked up the intricacy by throwing in 20 parameters to keep things unpredictable. Instead of st.. read more  

Algorithms for making interesting organic simulations
Link
@faun shared a link, 11 months ago
FAUN.dev()

Grok 4 Heavy won’t reveal its system prompt

Grok 4 Heavytucks its system prompt under the rug, abandoning its earlier promise of transparency. This move risks its credibility, especially on the heels of that recent antisemitic prompt debacle... read more  

Grok 4 Heavy won’t reveal its system prompt
Link
@faun shared a link, 11 months ago
FAUN.dev()

AI slows down open source developers. Peter Naur can teach us why.

AI tools trip up seasoned devs who’ve got the code stored upstairs because they bungle model transfer. Meanwhile, devs mistakenly trust they'll zip through it.Newcomers blaze ahead, knowing zilch about the codebase. Veterans? They hit roadblocks trying to dig deep... read more  

AI slows down open source developers. Peter Naur can teach us why.
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.