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@laura_garcia shared a post, 20 hours ago

Software Developer, RELIANOID

How to Load Balance Navitaire

✈️ Airline platforms can't afford downtime. Discover how RELIANOID helps improve the availability, performance, and security of Navitaire environments with load balancing, high availability, SSL offloading, and advanced protection capabilities. Read the 3-minute guide. 👇 https://www.relianoid.com/re..

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@rafidbottler gave 🐾 to Android Architecture: Components, Patterns & Best Practices Guide , 23 hours, 28 minutes ago.

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@rafidbottler gave 🐾 to Artificial Intelligence vs Machine Learning: What's the Difference? , 23 hours, 29 minutes ago.

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@anjali5 shared a link, 1 day, 19 hours ago

How to Fix Developer Productivity at 50+ Engineers

You ship a feature. It works. A week later, someone asks why it's not in staging yet, and you realize it's behind an infrastructure request that's still in review. The ticket isn't urgent enough to escalate. It's also not small enough to ignore. So it waits.

That's what a developer productivity problem feels like at 50 engineers.

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@laura_garcia shared a post, 1 day, 19 hours ago

Software Developer, RELIANOID

AI Reliability Engineering: The New Era of SRE

🤖 As AI becomes part of critical business operations, reliability is no longer just an infrastructure concern. From latency and model drift to observability and trust, AI workloads introduce a new set of challenges for modern SRE teams. In our latest article, we look at how reliability engineering i..

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@pluto_native started using tool Terraform , 4 days, 13 hours ago.

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@pluto_native started using tool Kubernetes , 4 days, 13 hours ago.

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@pluto_native started using tool Google Cloud Platform , 4 days, 13 hours ago.

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@pluto_native started using tool Amazon Web Services , 4 days, 13 hours ago.

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@eon01 shared a link, 4 days, 18 hours ago

Founder, FAUN.dev

A curated list of free AI models, APIs, and tools you can use without paying a cent.

Running AI shouldn't require a credit card. This list curates genuinely free models — open-weight models you can self-host, free API tiers from major providers, and tools to run everything locally.

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.