An AI-native analytics agent sits between users and the data warehouse, translating natural-language questions into governed SQL or Python workflows and dashboards.
This article explains how an AI Gateway centralizes LLM access, enabling secure routing, governance, cost control, and visibility for scalable AI adoption.
Feature flags help teams move fast, but when they’re not cleaned up, they quietly add extra code, slow down performance, and make applications harder to maintain.
Transitioning AI agents from POC to production requires moving beyond permissive access to a zero-trust architecture. This covers the essential security layers.
Edge AI runs AI on devices for real-time decisions, cutting latency, boosting privacy, lowering costs, and working without internet for faster, reliable systems.
A practical, step-by-step guide to building an LLM-driven orchestrator with safety guardrails, autonomous recovery, and lower operational cost for data pipelines.
GenAI is easy to prototype but hard to productionize. Vertex AI Agent Builder provides a unified platform for RAG, orchestration, security, and scalable deployment.
AI generates code faster than tests can cover. Coverage stays green while gaps grow. Treat AI code as untested by default and scale testing to match generation speed.
CV data issues keep recurring. I built cv-quality — a toolkit to audit datasets, catch annotation errors, find mislabeled samples, and streamline labeling.
GPUStack is an open-source tool that turns a bunch of scattered GPU machines into one managed cluster for deploying AI models behind an OpenAI-compatible API.
LLMs confidently hallucinate plausible data and asking them to "be careful" doesn't fix it. The most effective safeguard is an automated verification layer.
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