End-to-end observability strategy for LLM inference and LLM applications
LLM systems fail in ways that traditional API monitoring cannot surface — queues fill silently, GPU memory saturates long before CPU looks busy, and latency blows up at the batching layer rather than the application layer. This guide covers an end-to-end
observability strategy for LLM inference and LLM applications:
what to measure, how to instrument it with Prometheus, OpenTelemetry, and Grafana, and how to deploy the telemetry pipeline at scale.
Chunking is the most under-estimated hyperparameter in Retrieval ‑ Augmented Generation (RAG):
it silently determines what your LLM “sees”,
how expensive ingestion becomes,
and how much of the LLM’s context window you burn per answer.
From basic RAG to production: chunking, vector search, reranking, and evaluation in one guide.
Production-focused guide to building RAG systems: chunking, vector stores, hybrid retrieval, reranking, evaluation, and when to choose RAG over fine-tuning.
Strategic guide to hosting large language models locally with Ollama, llama.cpp, vLLM, or in the cloud. Compare tools, performance trade-offs, and cost considerations.
A performance engineering hub for running LLMs efficiently: runtime behavior, bottlenecks, benchmarks, and the real constraints that shape throughput and latency.
Running large language models locally gives you privacy, offline capability, and zero API costs.
This benchmark reveals exactly what one can expect from 14 popular
LLMs on Ollama on an RTX 4080.
The Rust ecosystem is exploding with innovative projects, particularly in AI coding tools and terminal applications.
This overview analyzes the top trending Rust repositories on GitHub this month.
The Go ecosystem continues to thrive with innovative projects spanning AI tooling, self-hosted applications, and developer infrastructure. This overview analyzes the top trending Go repositories on GitHub this month.
This comprehensive guide provides background and a detailed comparison of Anaconda, Miniconda, and Mamba - three powerful tools that have become essential for Python developers and data scientists working with complex dependencies and scientific computing environments.
Melbourne’s tech community continues to thrive in 2026 with an impressive lineup of conferences, meetups, and workshops spanning software development, cloud computing, AI, cybersecurity, and emerging technologies.