"As a lifelong programmer, I find myself living in two worlds at once: one where AI has become an essential co-pilot in my daily work, and another where its limitations still remind me who's actually steering the ship. Every day I see firsthand how machine learning models and coding assistants can accelerate development, generate insights, and even spark creativity - yet just as often, I see how hallucinations, privacy concerns, or opaque decision-making can derail a project faster than a bad merge."
"Basu: AI Models can be treated like a service dependency - monitored for metrics & performance. Production integration/deployments need to consider firewall, privacy & ethical concerns such as bias or hallucination risks. Developers should also plan for versioning and fallback logic in case the AI service degrades or changes unexpectedly."
"Whether you're building for mobile, web, or enterprise, the conversation is a practical roadmap for navigating the intersection of innovation and responsibility in AI-driven development - where every prompt, every deployment, and every decision still needs a human in the loop."
AI accelerates development, generates insights, and sparks creativity while also producing hallucinations, privacy risks, and opaque decision-making that can derail projects. Treat AI models as service dependencies, instrument them for metrics and performance, and implement monitoring, logging, and alerting. Address firewall, privacy, bias, and hallucination risks through governance, testing, and human-in-the-loop controls. Plan for versioning, rollback, and fallback logic to handle model degradation or unexpected changes. On mobile and web, monitor memory, storage, CPU/GPU usage, optimize performance for resource constraints, and ensure reliable fallback behavior when local models fail. Consider the rise of agentic AI and its impact on developer workflows, emphasizing governance and clear responsibilities.
Read at App Developer Magazine
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