Knowledge Graph for TAM through LLM and LangExtract
How Knowledge Graph and LangExtract enhance data extraction accuracy and traceability in Total Airport Management systems –>
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.
Edge AI on Kubernetes without Loss of Consistency
Edge AI Kubernetes as a unified platform: how to scale the edge without fragmentation and maintain control over distributed infrastructure.
ParaQAOA Accelerates QAOA for Max-Cut
arXiv is the largest open preprint repository (since 1991, under the auspices of Cornell), where researchers quickly post working versions of papers; the materials are publicly accessible but do not undergo full peer review, so results should be considered preliminary and, where possible, checked against updated versions or peer‑reviewed journals.
Hugging Face inference selection for agent systems
Hugging Face inference as a fallback for agent systems: hosted vs local, trade-offs, architecture, and deployment via llama.cpp.
Distributed inference simulation without discrepancies
Distributed inference simulation with Uniference: how DES bridges the gap between modeling and deploying AI systems.
ThecoreGrid Radar: Autonomous AI Engineers, Streaming-First Data, and the Shift to Platform Ownership
ThecoreGrid Radar brings a digest of the week’s top architectural insights. The industry is shifting toward autonomous AI engineers, facilitating full automation of coding, machine learning experiments, and code security enforcement.
Leak through CMS and a new class of models: how Anthropic faced a dual risk
Draft materials about the new AI model became publicly accessible due to a CMS configuration error. The incident highlighted two things simultaneously: the fragility of content pipelines and the increasing risks posed by the models themselves.
ARC-AGI: How to Measure Intelligence Through Learning Ability Rather Than Accumulated Skills
Most AI benchmarks evaluate outcomes. ARC-AGI shifts the focus to the process — how effectively a system learns new things. The problem manifests at the metric level. Modern systems demonstrate a high level of automation, but this is often a result of scaling data and computations, rather than an increase in generalization ability. A skill … Read more
Reducing Friction in Agentic AI: Local Validation and Isolated Environments in AWS
AI agents are limited not by models, but by architecture. If feedback is slow, autonomy does not work. The problem manifests when an AI agent tries to close the loop of “generated → validated → corrected.” In typical cloud systems, this loop is stretched: deployment takes minutes, tests depend on resource provisioning, and errors only … Read more
Automation of Design System Specifications: How Uber Eliminated Documentation Drift Using AI Agents
When component specifications lag behind implementation, the team starts building the system based on assumptions. At Uber, this turned into a systemic, large-scale problem—and was solved through agent-based automation. The problem does not arise at the moment of writing specifications, but later—when the system begins to evolve faster than the documentation. The Uber Base design … Read more