Deploy AI-managed automations from local runs to production using Trigger.dev monitoring and error handling to reduce workflow failures.
Real-world deployments show 40% test cycle efficiency improvement, 50% faster regression testing, and 36% infrastructure cost savings.
Testing isn't optional. Every AI platform interprets your data differently. What works perfectly in ChatGPT might fail completely in Perplexity. Test ...
Abstract: The growing complexity of software systems and the need for more rapid, high-quality software releases have created the need for intelligent and automated testing mechanisms. Drawing on ...
The dataset utilized in this paper is introduced in https://doi.org/10.1007/s10664-022-10247-x. It includes performance measurements from 500+ JMH microbenchmarks ...
Unified integration of OpenCog core components as a single monorepo, designed for ease of deployment, automation, and interactive neural-symbolic exploration. All components are directly included (no ...
DynPen: Automated Penetration Testing in Dynamic Network Scenarios Using Deep Reinforcement Learning
Abstract: Penetration testing, a crucial industrial practice for securing networked systems and infrastructures, has traditionally depended on the extensive expertise of human professionals.
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