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Updates and recent posts about AIStor.. Course
@eon01 published a course, 1 month ago
Founder, FAUN.dev
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@shurup gave 🐾 to Helm Cheat Sheet: Everything You Need to Know to Start Using Helm , 1 month, 1 week ago.
Story Palark Team
@shurup shared a post, 1 month, 1 week ago
@palark
Kubernetes best practices for DevOps engineers
Have to manage Kubernetes in production but don’t feel confident about its many moving parts, complex architecture, and configurations? Here’s a selection of technical guides from experienced engineers for Kubernetes beginners looking to master this orchestration tool for running containerised apps efficiently and reliably.
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@pramod_kumar_0820 shared a link, 1 month, 1 week ago
Software Engineer, Teknospire
⚡ Why Your Spring Boot API Takes 3 Seconds to Respond (And How to Fix It)
A practical breakdown of the most common Spring Boot performance bottlenecks — and how we optimized our API from 3 seconds to 200 ms.
News FAUN.dev() Team Trending
@devopslinks shared an update, 1 month, 1 week ago
FAUN.dev()
Microsoft Project Silica: Your Data, Stored in a Pyrex Dish, for 10,000 Years
Microsoft's Project Silica encodes data in borosilicate glass using femtosecond lasers, offering long-term storage for up to 10,000 years. This method overcomes traditional storage limitations and is cost-effective, though write speed remains a challenge. The research phase is complete, but no product release has been announced.
News FAUN.dev() Team Trending
@varbear shared an update, 1 month, 1 week ago
FAUN.dev()
Operating Systems as Age Gatekeepers: The Law That Could Reshape the Internet
California's Digital Age Assurance Act mandates operating systems to share users' age data with app developers via a real-time API by 2027. The law faces criticism for depending on self-reported ages, potentially affecting its efficacy.
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@varbear shared a link, 1 month, 1 week ago
FAUN.dev()
I deleted my laptop from my dev workflow. My iPhone does the job now
A developer ditches the laptop and SSHs from an iPhone into an always-onMac Mini. The phone becomes a terminal and browser. The remote runs the dev server, theClaude Code/CodexCLI, hot reload, file watching, and pushes viaTailscale. Persistent sessions (tmux) keep AI agents and services alive across.. read more
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@varbear shared a link, 1 month, 1 week ago
FAUN.dev()
Build agents that run automatically
Agents trigger from schedules, Slack, Linear, GitHub, PagerDuty events, or customwebhooks. They spin upcloud sandboxes. They run configuredMCPsand models. They verify outputs. They use amemorytool. Cursor automates security audits on pushes. Scores PR risk and auto-approves low-risk changes. Runs Pa.. read more
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@varbear shared a link, 1 month, 1 week ago
FAUN.dev()
We Might All Be AI Engineers Now
The author supervises AI agents that orchestrate concurrent graph traversal, multi-layer hashing, AST parsing, and file system watchers. The agents run traversal, hashing, and watcher loops. The engineer architects system behavior, verifies outputs, and probes agents in parallel to debug... read more
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@varbear shared a link, 1 month, 1 week ago
FAUN.dev()
The Great Developer Divide: How AI Is Reshaping the Software Job Market Into Three Tiers
AI hiring has split dev work into three camps:Apex Tier,Hybrid Middle, and a shrinkingAutomatable Tail. Demand now favorsAI orchestration,prompt engineering, fastcode reading, and platform roles likeplatform engineer,fleet supervisor, andAI QA. System shift:Organizations must rework career ladders, .. read more
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.
