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If you're building the Enterprise AI roadmap, clear your schedule for October 22 (Sponsor)The Enterprise Ready Conference is a one-day event in SF for product and engineering leaders shaping the future of Enterprise AI.Hosted by WorkOS, the event features a curated lineup of speakers with hands-on experience building for the enterprise, including leaders from Postman, Webflow, Rippling, Render, Socket, Opal, and RunReveal.You'll hear how they're driving Enterprise AI adoption in real-world...

Stop pulling developers into QA. (Sponsor)Testing slows developers down. Every hour spent investigating failures, fixing flaky tests, or running regressions delays releases and limits what your team can ship.QA Wolf takes testing off your team's plate by combining AI with expert QA engineers to build and maintain 80% automated coverage in weeks, not months. Tests run in parallel, suites finish in minutes, and failures are real—not false alarms. Workflows stay current as your app evolves, and...

6 templates to build your AI governance framework (Sponsor)30% of employees are secretly using AI — and while it may make them more productive, unsanctioned AI use creates serious data and compliance risks. 👉 What you can do: Use proven playbooks for responsible AI rollout. Use the AI Governance Kit as your starting point, with ready-to-use templates including:A step-by-step, long-term governance planAn internal AI use policy templateA scoring matrix to prioritize high-impact use casesTools...

The MCP Security Guide for Early Adopters (Sponsor)Is your team exploring MCP integrations?The Model Context Protocol (MCP) is quickly emerging as the go-to standard for connecting LLMs to external tools and data. But as adoption picks up, many teams are implementing MCP without a clear security playbook.This practical guide breaks down real-world MCP security risks and offers actionable steps to help teams secure LLM integrations.Inside the guide:Key risks with local and remote MCP...

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LLM Observability Best Practices (Sponsor)This best practices guide is full of practical tips on:Monitoring LLM workflows end-to-endDetecting and mitigating security risksEnsuring reliable, high-quality outputsGet your copy here >What's inside: LLM workflows often involve multiple calls to managed LLM platforms, making it difficult to identify the root causes of errors or latency issues during troubleshooting. Assessing the functional performance of LLM apps can be equally complex.Written by...

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