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@devopslinks shared a link, 3 months, 2 weeks ago
FAUN.dev()

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, 3 months, 2 weeks ago
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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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@devopslinks shared a link, 3 months, 2 weeks 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, 3 months, 2 weeks 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, 3 months, 2 weeks ago
FAUN.dev()

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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@eon01 published a course, 3 months, 2 weeks 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, 3 months, 2 weeks 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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News FAUN.dev() Team
@kala shared an update, 3 months, 2 weeks ago
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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
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@kala added a new tool GPT-5.3-Codex , 3 months, 2 weeks ago.
INTELLECT-3 is a frontier-class 100B+ Mixture-of-Experts language model developed by Prime Intellect and trained end-to-end using their large-scale asynchronous RL framework, PRIME-RL. Built on the GLM-4.5-Air base model, INTELLECT-3 combines supervised fine-tuning with long-horizon reinforcement learning across hundreds of verifier-backed environments spanning math, code, science, logic, and agentic tasks.

The model was trained on a high-performance cluster of 512 NVIDIA H200 GPUs across 64 nodes, supported by Prime Intellect’s Sandboxes execution engine, deterministic compute orchestration, and Lustre-backed distributed storage. The result is a model that surpasses many larger systems in reasoning benchmarks while remaining fully open-source.

Prime Intellect released not only the model weights but also the full training recipe: PRIME-RL, Verifiers, the Environments Hub, datasets, and evaluation suites. INTELLECT-3 is positioned as a foundation for organizations seeking to post-train or customize their own frontier-grade models without relying on proprietary AI labs.