Join us

ContentUpdates and recent posts about Elastic APM..
Story
@laura_garcia shared a post, 2 months, 1 week ago
Software Developer, RELIANOID

🔐 CISOs are ramping up crisis simulations in 2025!

A recent study shows 74% of CISOs plan to increase their budgets for cyber crisis exercises. Why? The rise in sophisticated attacks and high-profile breaches like those affecting 23andMe, NHS, and Cencora highlight the urgent need for proactive defense strategies. At RELIANOID, we help organizations..

Story FAUN.dev Team
@eon01 shared a post, 2 months, 1 week ago
Founder, FAUN.dev

🚀 Meet This Week’s Human: A New Way to Celebrate Builders

Every week, thousands of developers read FAUN to stay sharp, discover tools, and learn what’s trending in Software Engineering.

Now, we’re adding a human touch.

ThisWeeksHuman
Story
@laura_garcia shared a post, 2 months, 1 week ago
Software Developer, RELIANOID

✈️ Understanding Airport Software Systems

From check-in to takeoff, modern airports rely on a complex network of integrated IT systems to ensure efficiency, safety, and smooth operations. We’ve visualized this in a new diagram, highlighting key components like: ✅ AODB (Airport Operational Database) ✅ Passenger & baggage handling systems ✅ A..

Airport Software Systems
Link
@faun shared a link, 2 months, 1 week ago

Why Go is a good fit for agents

Gorules the realm of long-lived, concurrent agent tasks. Its lightning-fast goroutines and petite memory use make Node.js and Python look like clunky dinosaurs trudging through thick mud. And don't get started on itscancellation mechanism—seamless cancelation, zero drama...

Why Go is a good fit for agents
Link
@faun shared a link, 2 months, 1 week ago

Vibe coding web frontend tests — from mocked to actual tests

Cursorwrestled with flaky tests, tangled in its over-reliance onXPath. A shift todata-testidfinally tamed the chaos. Though it tackled some UI tests, expired API tokens and timestamped transactions revealed its Achilles' heel...

Vibe coding web frontend tests — from mocked to actual tests
Link
@faun shared a link, 2 months, 1 week ago

AI Runbooks for Google SecOps: Security Operations with Model Context Protocol

Google's MCP servers arm SecOps teams with direct control of security tools using LLMs.Now, analysts can skip the fluff and get straight to work—no middleman needed. The system ties runbooks to live data, offeringautomated, role-specific security measures. The result? A fusion of top-tier protocols ..

AI Runbooks for Google SecOps: Security Operations with Model Context Protocol
Link
@faun shared a link, 2 months, 1 week ago

Poison everywhere: No output from your MCP server is safe

Anthropic's MCPmakes LLMs groove with real-world tools but leaves the backdoor wide open for mischief. Full-Schema Poisoning (FSP) waltzes across schema fields like it owns the place.ATPAsneaks in by twisting tool outputs, throwing off detection like a pro magicians’ misdirection. Keep your eye on t..

Poison everywhere: No output from your MCP server is safe
Link
@faun shared a link, 2 months, 1 week ago

Meta reportedly in talks to invest billions of dollars in Scale AI

Metawants a piece of the$10 billion pieat Scale AI, diving headfirst into the largest private AI funding circus yet.Scale AI'srevenue? Projected to rocket from last year’s $870M to$2 billionthis year, thanks to some beefy partnerships and serious AI model boot camps...

Meta reportedly in talks to invest billions of dollars in Scale AI
Link
@faun shared a link, 2 months, 1 week ago

Automate Models Training: An MLOps Pipeline with Tekton and Buildpacks

Tekton plusBuildpacks: your secret weapon for training GPT-2 without Dockerfile headaches. They wrap your code in containers, ensuring both security and performance.Tekton Pipelineslean on Kubernetes tasks to deliver isolation and reproducibility. Together, they transform CI/CD for ML into something..

Automate Models Training: An MLOps Pipeline with Tekton and Buildpacks
Link
@faun shared a link, 2 months, 1 week ago

Meta Introduces LlamaRL: A Scalable PyTorch-Based Reinforcement Learning RL Framework for Efficient LLM Training at Scale

Reinforcement Learningfine-tunes large language models for better performance by adapting outputs based on structured feedback. Scaling RL for LLMs faces resource challenges due to massive computation, model sizes, and engineering problems like GPU idle time. Meta's LlamaRL is a PyTorch-based asynch..

Meta Introduces LlamaRL: A Scalable PyTorch-Based Reinforcement Learning RL Framework for Efficient LLM Training at Scale

This tool doesn't have a detailed description yet. If you are the administrator of this tool, please claim this page and edit it.