Rate limiting breaks without input data
Rate limiting without data breaks architectural analysis. We examine why the lack of observability makes optimization impossible.
Observability
Observability on ThecoreGrid focuses on understanding, monitoring, and debugging complex distributed systems in production.
We cover logging, metrics, tracing, and profiling as core pillars for gaining visibility into system behavior under real workloads. Topics include instrumentation strategies, telemetry pipelines, alerting design, SLI/SLO definition, and incident detection in highload environments. We analyze trade-offs between signal quality, cost, and system overhead, along with challenges of cardinality, sampling, and data retention. Content is grounded in BigTech practices, including incident post-mortems and lessons from operating large-scale systems. You’ll find deep dives into modern observability stacks, correlation techniques, and debugging methodologies for microservices and cloud-native platforms. Instead of tool-focused tutorials, the Observability tag delivers engineering insights for SREs, platform teams, backend engineers, and architects responsible for system reliability, performance, and operational transparency.
Event-driven architecture in banks without illusions
Event-driven architecture in banks: how to reduce coupling and not lose reliability. Outbox/inbox patterns, contracts, and real compromises.
ThecoreGrid Radar: Agentic systems under control, LLM infrastructure efficiency, a new wave of GPU compilation
AI Infrastructure, GPU Compilers, Agentic Systems, Distributed Systems, High Performance Computing, HPC, Telecommunications, SRE
FSM Benchmark for Evaluating Network AI Agents
FSM benchmark network configuration: how NetAgentBench reveals failures of LLM agents in dynamic network scenarios and multi-turn behavior.
Agentic systems without context overload
How an agentic system manages the context window through Journal, Review, and Timeline, reducing latency and improving consistency in multi-agent reasoning.
Root cause analysis as code in SRE systems
Root cause analysis (RCA) hinges on scale and the human factor. Meta’s approach with DrP demonstrates how to turn debugging into a reproducible engineering process. The problem does not manifest immediately — until the system reaches organizational scale. Incidents begin to recur, but each time they are investigated anew. Knowledge of where to look for … Read more
Symbolic execution for BPF malware analysis
Symbolic execution simplifies the analysis of BPF malware and eliminates a bottleneck in reverse engineering. This approach allows for the automatic reconstruction of “magic” packets to trigger backdoors. The problem does not manifest immediately — until the analysis of BPF malware encounters the complexity of the filters themselves. The classic Berkeley Packet Filter operates as … Read more
Agent Reliability Score and Platform Contracts
Agent Reliability Score explains how the platform affects the reliability of AI agents and why context control is critical for production systems.
GitOps Policy for Kubernetes through Kyverno and Argo CD
GitOps policy for Kubernetes becomes manageable when enforcement is built into the delivery pipeline. The combination of Kyverno and Argo CD bridges this gap at the admission level.
LLM evaluation at scale on Apache Spark
LLM evaluation at scale on Apache Spark: how the distributed architecture, caching, and statistical validation of models are structured.