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@kala shared a link, 5 days, 9 hours ago
FAUN.dev()

My AI Adoption Journey

A dev walks through the shift from chatbot coding toagent-based AI workflows, think agents that read files, run code, and double-check their work. Things only clicked once they built outcustom tools and configsto help agents spot and fix their own screwups. That’s the real unlock... read more  

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@devopslinks shared a link, 5 days, 9 hours ago
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Demystifying : Why You Shouldn’t Fear Observability in Traditional Environments

OpenTelemetry is friendly with the past. It now pipesreal-time observability into legacy systems- no code rewrite, no drama. Pull structured metrics straight from raw logs, Windows PDH counters, or SQL Server stats. It doesn’t stop there. Got MQTT-based IoT gear? OTLP export or lightweight adapters .. read more  

Demystifying : Why You Shouldn’t Fear Observability in Traditional Environments
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@devopslinks shared a link, 5 days, 9 hours ago
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How GEICO lowered its $300M cloud spend and decoupled security from the network

GEICO's IT infrastructure transformation journey highlights the shift from legacy network-centric security model to a more modern, identity-first approach. By centralizing identity and secrets management using HashiCorp Vault, GEICO improved security, reliability, and compliance across their hybrid .. read more  

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@devopslinks shared a link, 5 days, 9 hours ago
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CloudBees CEO: Why Migration Is a Mirage Costing You Millions

A new CloudBees survey shows 57% of enterprises dropped over $1M on cloud migrations last year. Each effort blew past budget by an average of $315K. The kicker? Many teams still treatmodernization as migration- a shortcut that usually leads to drained budgets, burned-out devs, and delays in shipping.. read more  

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@devopslinks shared a link, 5 days, 9 hours ago
FAUN.dev()

The only Terraform pipeline you will ever need: GitHub Actions for Multi-Environment Deployments

A sharp new GitHub Actions pipeline can now sniff out which Terraform environments changed - anywhere in the repo, no matter how nested - and run them in parallel. Fast, clean, and automatic. It leans onmatrix jobs,Checkovfor static analysis,Workload Identity Federationfor secure cloud access (no ha.. read more  

The only Terraform pipeline you will ever need: GitHub Actions for Multi-Environment Deployments
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@devopslinks shared a link, 5 days, 9 hours ago
FAUN.dev()

Scaling PostgreSQL to power 800 million ChatGPT users

OpenAI pushedPostgreSQLto handle millions of QPS across 800M users. How? Nearly 50 read replicas, heavy read offloading, and serious trimming on write pressure. Writes? Sent elsewhere. Sharded systems likeCosmosDB, lazy writes, and app-level tweaks helped sidestep PostgreSQL’sMVCCwrite amplification.. read more  

Scaling PostgreSQL to power 800 million ChatGPT users
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@eon01 published a course, 5 days, 23 hours ago
Founder, FAUN.dev

Painless Docker - 2nd Edition

Docker Compose Docker Grype Syft Docker Swarm Go Python

A Comprehensive Guide to Mastering Docker and its Ecosystem

Painless Docker - 2nd Edition
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@laura_garcia shared a post, 6 days, 8 hours ago
Software Developer, RELIANOID

🚀 FinovateEurope 2026

📍 London, UK | 🗓️ 10–11 February 2026 Market-ready innovations. Executive-level networking. Inspiring insights. FinovateEurope brings together banking leaders, fintech innovators, investors, and technology providers to shape the future of financial services at a critical moment for the global fint..

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@kala shared an update, 1 week ago
FAUN.dev()

This Is the First AI That Helped Build Itself - Meet GPT-5.3-Codex

GPT-5.3-Codex

GPT-5.3-Codex, an advanced model, enhances coding performance and reasoning, operating 25% faster than its predecessor. It excels in industry benchmarks, supports the software lifecycle, and can autonomously build complex applications. The model is available on multiple platforms with plans for API access.

This Is the First AI That Helped Build Itself - Meet GPT-5.3-Codex
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.