After successfully adopting Terraform for GitHub repository management, the next step in our Infrastructure as Code (IaC) journey was clear: dogfood our own product and manage our domains and DNS zones using the DNSimple Terraform provider.

DriftHound is a Rails WebApp that receives Terraform drift reports via API and provides visibility into infrastructure drift across your projects.

A lightweight, single-binary OpenTelemetry viewer for local development. Visualize traces, logs, and metrics from your instrumented applications — no Docker, no databases, no complex setup.

Run multiple agents in parallel — each in its own container, with its own workspace, collaborating on your code or project files simultaneously.

A personal knowledge base that turns markdown notes into a semantically-connected, AI-augmented knowledge graph.

Atomic stores knowledge as atoms — markdown notes that are automatically chunked, embedded, tagged, and linked by semantic similarity. Your atoms can be synthesized into wiki articles, explored on a spatial canvas, and queried through an agentic chat interface.

More and more enterprises want the benefits of AI-assisted coding, automatic completions, suggestions, and inline generation, without sending their source code to external APIs.

This has naturally increased interest in self-hosted coding assistants, where all inference runs on internal hardware and all models stay inside a controlled environment.

We built a complete prototype of such a system. In this article, we walk through its architecture, explain how Kubernetes is used to deploy it, and how different system parameters interact to determine real-world performance. In a separate post, we study how the llama.cpp inference server behaves under increasing load.

This is a story about a tricky issue I resolved recently.

This article explains how to use the In-Place Pod Resize feature in Kubernetes, combined with Kube Startup CPU Boost, to speed up Java application startup.

The open-source, datacenter-scale inference stack. Dynamo is the orchestration layer above inference engines — it doesn't replace SGLang, TensorRT-LLM, or vLLM, it turns them into a coordinated multi-node inference system. Disaggregated serving, intelligent routing, multi-tier KV caching, and automatic scaling work together to maximize throughput and minimize latency for LLM, reasoning, multimodal, and video generation workloads.

Exports helm release, chart, and version statistics in the prometheus format.

As part of an ongoing series, the Developer Experience SIG interviews organizations about their real-world OpenTelemetry Collector deployments to share practical lessons with the broader community. This post features Adobe, a global software company whose observability team has built an OpenTelemetry-based telemetry pipeline designed for simplicity at massive scale, with thousands of collectors running per signal type across the company’s infrastructure.

Traceway is a self-hosted observability platform that ingests OpenTelemetry traces and metrics, groups exceptions automatically, and gives you endpoint performance, distributed tracing, and alerts — all in a single binary. No OTel Collector or separate time-series database required.

Log analytics in a single binary. No dependencies. Lynx Flow query language.