GPT-Live: how OpenAI separated live path and logic
OpenAI’s GPT-Live architecture: how to distinguish between the live path and application logic, reduce latency, and scale stateful voice interaction.
AI
AI on ThecoreGrid focuses on production-grade engineering for machine learning and LLM systems in highload environments.
We cover how to design scalable AI architectures, build reliable data and feature pipelines, and choose infrastructure for training and inference with predictable latency, cost, and resilience. The content is curated from real BigTech practices: incident post-mortems, MLOps and DevOps patterns, observability, security, and governance for AI-powered products. Instead of hype or beginner tutorials, you get deep technical analysis of real-world implementation: LLM integration into existing services, RAG architecture decisions, orchestration strategies, vector databases, caching, CI/CD for ML, and model quality control in production. The AI tag is built for architects, ML engineers, backend/platform teams, and SREs who deploy AI in critical systems and need robust, maintainable, and scalable solutions.
Data Mesh for AI Agents: How to Prepare the Data Layer
Data Engineering for AI agents: how data mesh, semantic models, and MCP tools help balance precision, security, and cost in enterprise systems.
AI Architecture: 4 Layers to Production
AI architecture for enterprise: 4 layers of the production stack, trade-offs between managed API and self-hosting, integration, serving, and operations.
Agent Optimization in Foundry: Request Economics
Agent optimization in Microsoft Foundry begins not with reducing token cost, but with the price of a successful outcome. For an agentic system, this is more important because one result often requires multiple model requests. The main issue here is not the model itself, but that a prototype can easily become the production default. In … Read more
MetaRoCE and Ethernet for AI Infrastructure
MetaRoCE for AI Infrastructure: How Offloading Intelligence to the NIC Helps Ethernet Maintain Throughput, Low Tail Latency, and Graceful Recovery.
MetaRoCE for AI Infrastructure on Ethernet
MetaRoCE for Ethernet-based AI infrastructure: how Meta moves intelligence into the NIC, eliminates PFC, and creates a loss-resilient transport for million-GPU scale.
AI Security in Production: Roblox Prompt to Prod
AI Security in Production at Roblox: how sandboxing, guardrails, and exemplars help safely lead AI agents from prompt to production.
Automated RCA in microservices at Atlassian
Automated RCA in microservices turns metrics, logs, and traces into ranked root cause hypotheses for faster incident validation and cleaner diagnosis
AWS Aurora and DynamoDB for AI Context
Data replication for AI agents: how Aurora, DynamoDB, and Keyspaces help avoid stale reads, race conditions, and context errors.
eBPF for AI API: control without code changes
eBPF for AI API in Kubernetes: how to intercept traffic, limit AI agents, and control behavior without code changes and container restarts.