Rainer Ameth Aguilar Franco@raineraguilar2021126
2
Events attended
1
Submissions made
Panama
1 year of experience
About me
Rainer Aguilar is a technology professional with extensive experience in technical support, data analytics, process optimization, and emerging technologies. Currently working at Dell Technologies, he combines a strong background in troubleshooting complex technical issues with a growing specialization in data-driven decision making and automation. With more than a decade of experience in the technology sector, Rainer has built a reputation for solving challenging problems, improving operational efficiency, and delivering practical solutions that bridge business needs and technical implementation. His expertise includes Python, PowerShell, SQL, Microsoft 365, Linux environments, and modern AI assistants such as GitHub Copilot, Gemini, and ChatGPT. As a Data Analyst focused on the AdTech ecosystem, he works with large-scale datasets, attribution modeling, and advertising performance optimization, helping transform raw data into actionable business insights. His approach emphasizes evidence-based decision making and democratizing access to data across teams. Passionate about continuous learning and community engagement, Rainer has also contributed to technical education initiatives, including presenting and hosting SQL and data analytics training sessions. He enjoys exploring AI-assisted development, automation, cloud technologies, and innovative solutions that improve developer workflows and operational excellence. Driven by curiosity, analytical thinking, and a commitment to innovation, Rainer continues to expand his expertise at the intersection of data, software development, artificial intelligence, and modern technology operations.
🤓 Latest Submissions
BobGuard: Evidence-Driven Incident Debugging
BobGuard addresses a common debugging bottleneck: when a production incident occurs, developers must manually correlate logs, stack traces, source code, deployment history, and runbooks before they can identify the cause and safely prepare a fix. This context switching slows diagnosis and increases the risk of incomplete fixes and repeated work. Our prototype uses IBM Bob 2.0 to guide an evidence-driven incident workflow. A developer provides a reproducible incident and its supporting project documents. In Agent mode, Bob investigates the code and evidence, coordinates independent analysis tasks in parallel where supported, and produces a root-cause explanation tied to specific files, log entries, and operational guidance. The workflow then verifies a proposed fix with a regression test and creates an incident and release-readiness report covering test results, security findings, rollback steps, and remaining risks. A small policy filter demonstrates redaction of selected test-secret patterns in the report; it is intentionally presented as a limited control, not a guarantee of complete data protection. The target users are developers and small engineering or operations teams responsible for diagnosing and resolving application incidents. They interact with BobGuard by supplying an incident, reviewing Bob’s cited findings, checking the proposed change and test results, and using the report to inform release decisions. BobGuard’s distinctive idea is to make the debugging process—not just code generation—observable and verifiable. It combines document understanding, coordinated investigation, regression testing, and release communication in one workflow, while distinguishing verified results from simulations or unavailable capabilities. We evaluate its impact by comparing the assisted workflow with a manual baseline, tracking diagnosis time, manual steps, first-fix test success, and rework rather than claiming unmeasured productivity gains.
27 Sep 2026
