Tag: #llm

14 Days of Intensive “Agentic Coding”: What I Learned from Working with IBM Bob, an Agentic IDE

This post shares lessons from a 14-day engineering project using IBM Bob’s Agentic IDE. It is not a product review or benchmark, but a personal experience report about how intensive Agentic Coding changed the author’s engineering behavior. A central observation is the “Navigation System Syndrome”: as trust in the Agentic IDE increased, the author worked at a higher abstraction level and inspected fewer implementation details. The post explores why continuous specification management, verification, documentation, observability, Engineering Provenance, and human responsibility remain essential when AI becomes a more capable part of software engineering.

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AI generated Podcast Episode: AI Coding Assistants, Agentic IDEs, and Privacy: From Chatbots to Operational Systems

The AI generated explores the complexities of privacy in the context of AI coding assistants and agentic IDEs. It emphasizes that privacy is now a structural choice within development environments rather than a simple policy acceptance. The effectiveness of six platforms is assessed based on their privacy features, highlighting trade-offs between integration, cost, and data handling.

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Revisiting the AI Operational Complexity Cube: From LLM Testing to AI Systems in Production

The article continues the exploration of the AI Operational Complexity Cube, emphasizing that modern AI systems encompass software, infrastructure, and probabilistic AI components. It highlights the need for comprehensive testing approaches that consider interactions across these dimensions, as proper evaluation requires observing behaviors that emerge from integrated systems rather than isolated code.

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The Cup Is Not the Coffee: What Data Quality Means in the AI Era

AI systems rely heavily on data quality, which is often overlooked despite modern technical architectures. Issues like outdated, incomplete, or misaligned data can undermine system reliability, regardless of the sophistication of the components. Effective AI requires both high-quality data and solid technical infrastructure to meet user expectations and ensure trust.

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Who Reviews AI-Generated Software?

AI is transforming the software development lifecycle, shifting focus from coding to reviewing AI-generated systems. While AI tools simplify software generation, building trustworthy systems remains complex. Traditional review processes may no longer suffice. This raises a critical question: how can humans responsibly.

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