This week, we got to know DZone contributor Mayowa Fajobi and his journey into open source and platform engineering, as well as what he enjoys outside of work.
Learn how to secure enterprise data in AI systems using data classification, access controls, encryption, vendor controls, prompt security, and output validation.
A self-hosted way to track a brand's mention, citation, and share of voice across AI answer engines — sampled on a schedule so you follow the trend, not a single answer.
If you are building a system that both evaluates people and talks to them, assume the two functions will entangle unless you deliberately separate them.
This article explores how to integrate an ONNX transformer model into an iOS application using Swift. It covers loading and running models with ONNX Runtime.
A seven-point governance checklist for engineering teams, with automated CI/CD pipeline gates for model registration, bias testing, and drift detection.
Enterprise AI needs knowledge graphs alongside RAG to enable relationship-aware, explainable, secure, and contextually accurate retrieval and reasoning.
Anthropic has opened a biology lab to test Claude with real experiments and lab equipment, expanding its AI life sciences work beyond computer simulations.
SpaceXAI’s Grok 4.7 improves coding benchmarks and keeps API prices low, but heavy token consumption could raise the cost of completing real-world tasks.
Six techniques to cut LLM API costs by up to 90%: prompt caching, model routing, batch processing, and more. (Includes a pip-installable Python library.)
Learn how to accept image submissions properly and evaluate them for AI content in the background, keeping unapproved content out of normal publication workflows.
Full-stack AI development is building an AI feature end to end. In 2026 the hard part is agentic reliability, evals, and production, not the model call.
AI is generating code faster than humans can review it. The fix is cognitive architectures that understand not just "what changed" but "why" and whether it's safe.
More AI test cases don't automatically increase confidence. Smart, risk-based statistical sampling provides more reliable AI validation than expanding a test suite.