Blockchain RPC skills for AI coding agents

Instead of managing node groups, installing Karpenter, configuring the VPC CNI plugin, deploying the AWS Load Balancer Controller, setting up the EBS CSI driver, and keeping all of those components updated and compatible with each other - you enable a single flag and AWS handles all of it.

After a few years of writing open-source Terraform modules, I've picked up a few syntax tricks that make code safer, cleaner, and easier to maintain.

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.