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.
A selection of architectural insights and releases we’ve been reading this week. Infrastructure 🔹 Robust Constraint-Aware Bayesian Tuning of BBRv2 for QUIC under Tactile Internet ConstraintsA new approach to tuning BBRv2 for QUIC is proposed, taking into account the constraints of the Tactile Internet. This can improve performance under conditions of high latency and unstable … Read more
AI Moderation Platform in the Marketplace: How DoorDash Reduced Incidents, Separated the Low-Cost Layer from LLM Scoring, and Why Boolean Logic Proved to Be a Poor Choice
QoS-aware autoscaling for AI inference: distributed scheduling with user devices, lower dedicated capacity, and better tail latency under growth
How AI agents are reshaping cloud security from shared infrastructure to isolation-first architecture. Explore workload isolation, Kubernetes sandboxing, SPIFFE identity, short-lived credentials, failure containment, and verifiable trust
Text2SQL caching for production: how SQL templates, embeddings, and entity extraction reduce latency, token cost, and load on the LLM without sacrificing accuracy
PTX Tensor Core GEMM on NVIDIA L4: why hand-written kernels help for INT8 and INT4, and why FP16 still favors WMMA
Latency budget in LLM serving changes the priorities of scheduling. CASCADE demonstrates how to link scheduling and KV-cache for increased goodput. The problem arises when all requests are formally equal in SLO, but in reality, they are not. In one cluster, chat, code generation, and reasoning coexist simultaneously. Their costs differ by orders of magnitude: … Read more
How AI agents are reshaping distributed systems through memory-centric architecture, orchestration, stateful agents, identity, sandboxing, and observability. Explore key trends and the architectural outlook for the next 6–12 months
How AI code migration addresses the challenge of large-scale codebase changes, and the architectural trade-offs involved in using LLM agents
How Agentic Nesting Transforms Enterprise Application Integration via Multi-Agent Architecture and Semantic System Orchestration
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