AI SRE needs more than AI: it needs operational context
An AI SRE can diagnose a failure in seconds, yet incidents still drag without ownership and context. Why operational context decides whether AI SRE delivers.
Perspectives on AI, engineering, and the humans keeping systems running.
An AI SRE can diagnose a failure in seconds, yet incidents still drag without ownership and context. Why operational context decides whether AI SRE delivers.
Treat AI in platform engineering like an ambitious intern: scoped credentials, clear limits and policy-driven guardrails, drawing on a PlatformCon NYC panel.
Designing AI products adds a fourth step, interpretation, to input, system and output. What that means for product design and how Rootly designs for AI with AI.
How AI agents work in production operations, what they can and cannot do today, and how building trust with them will change the way SRE teams operate.
Rootly AI Labs analyzes the performance of Meta’s Llama 4 models and finds they underperform compared to competitors like Claude 3.5 Sonnet and Qwen2.5
A practical framework for evaluating AI tools based on four core pillars: Accuracy, Transparency, Adaptability, and Agentic capabilities.
TDX was buzzing with energy as Salesforce unveiled Agentforce 2.0, bringing agentic AI directly into Slack.
Rootly and the HackOS team distilled DeepSeek R1 from 671B to 70B parameters and benchmarked it against GPT-4o and Llama 3 on classifying Apache error logs.
Discover how Google is optimizing for accuracy in its AI strategy, while Meta strives to expand its response capabilities through machine learning.
Explore how AI-powered tools like Slack, Salesforce, Canva, and Rootly are revolutionizing the way we work.
How Rootly builds AI for incident management in Slack with privacy in mind, protecting customer data while helping teams cut through information overload.
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