ContentPosts from @ajay..
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@kala shared a link, 1 day, 18 hours ago
FAUN.dev()

Monitoring LLM behavior: Drift, retries, and refusal patterns

Traditional software is predictable due to determinism, while generative AI is unpredictable. Engineers need a new infrastructure layer, the AI Evaluation Stack, to ship enterprise-ready AI products. The stack includes deterministic assertions and model-based assertions to ensure structural integrit.. read more  

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@kala shared a link, 1 day, 18 hours ago
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Introducing the Agent Readiness score. Check to see if your site is agent-ready

Cloudflare launchedIsItAgentReady. It scans200kdomains, scoresagent readiness, publishes weekly adoption charts, and exposes results via anAPI. It checksrobots.txt,llms.txt, content negotiation viaAccept: text/markdown,API Catalog,.well-known/mcp.json, OAuth discovery, andx402payments. Cloudflare ov.. read more  

Introducing the Agent Readiness score. Check to see if your site is agent-ready
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@kala shared a link, 1 day, 18 hours ago
FAUN.dev()

The AI engineering stack we built internally - on the platform we ship

Cloudflare wired AI into the engineering stack. LLM traffic funnels through aproxy WorkerandAI Gateway. It shippedWorkers AIand theAgents SDK. Daily users hit 3,683 (93% R&D). MR throughput climbed to ~10,952/week.Workers AIhandled 51B input tokens and cut a security agent's inference spend by 77%... read more  

The AI engineering stack we built internally - on the platform we ship
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@kala shared a link, 1 day, 18 hours ago
FAUN.dev()

Multi-Agent System Reliability

LLMs are unreliable out of the box, but multi-agent systems can improve by dividing work among specialized agents. Building robust systems involves leveraging human system patterns like hierarchy, consensus, adversarial debate, and knock-out in a multi-agent architecture to ensure correctness and re.. read more  

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@devopslinks shared a link, 1 day, 20 hours ago
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How incidents can teach us about what’s already working well

A famous optical illusion developed by Edward H. Adelson shows that two squares, despite appearing different in shade, are actually the same gray. This illusion demonstrates how the brain processes light, shadow, and objects when interpreting visual signals from the optic nerve. Studying such illusi.. read more  

How incidents can teach us about what’s already working well
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@devopslinks shared a link, 1 day, 20 hours ago
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The most severe Linux threat to surface in years catches the world flat-footed

Publicly released exploit code for a critical privilege escalation vulnerability in Linux, known as CopyFail (CVE-2026-31431), allows attackers to gain root access across all vulnerable distributions with a single piece of code. The researchers from Theori disclosed the vulnerability 5 weeks after n.. read more  

The most severe Linux threat to surface in years catches the world flat-footed
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@devopslinks shared a link, 1 day, 20 hours ago
FAUN.dev()

The Software Development Lifecycle Is Dead

AI agents collapse the classicSDLC-requirements,design,implementation,testing,review,deployment- into an intent-driven loop. They generate code, tests, and pipelines together. They commit tomain. Automated verification runs. Deployment and release split withfeature flags... read more  

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@devopslinks shared a link, 1 day, 20 hours ago
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The Human Infrastructure: How Netflix Built the Operations Layer Behind Live at Scale

Netflix has massively scaled its live content, now streaming over nine shows per day with up to 17.9M peak viewers per game, thanks to a complex Broadcast Operations Center, strict transmission quality standards, and a tiered human operations model, including specialized engineering teams and dedica.. read more  

The Human Infrastructure: How Netflix Built the Operations Layer Behind Live at Scale
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@devopslinks shared a link, 1 day, 20 hours ago
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The Silent Failure of Reliability Metrics at Scale: Lessons Learned from a Decade of Broken Metrics

At scale, observability breaks whenSLIsand metrics mix different behaviors and lose clear meaning. Complexity grows: more event types, extra labels, and risingcardinality. That bloats queries, slows evaluation pipelines, and distortsPrometheus,PromQL, andElasticmetrics. Why this matters:Teams must t.. read more  

The Silent Failure of Reliability Metrics at Scale: Lessons Learned from a Decade of Broken Metrics
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@devopslinks shared an update, 1 day, 22 hours ago
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Ubuntu's Next Chapter: Local AI, Confined Agents, and a Bet Against the Cloud-First OS

Ubuntu Ollama Snap

Ubuntu is getting local AI as a native capability over the next year, with inference snaps that install models like any other package, AI-powered accessibility features, and confined agentic workflows for both desktops and server fleets. Canonical is betting on open weight models, local-by-default inference, and snap confinement, a deliberate counter to the cloud-first AI direction Microsoft, Apple, and Google are taking with their operating systems.

Ubuntu's Next Chapter: Local AI, Confined Agents, and a Bet Against the Cloud-First OS