Apache Flink on K8s and Kafka: PyFlink, Go, ops, and managed pricing
Stateful streaming, checkpoints, K8s, PyFlink, Go.
Apache Flink is a framework for stateful computations over unbounded and bounded data streams.
Stateful streaming, checkpoints, K8s, PyFlink, Go.
Apache Flink is a framework for stateful computations over unbounded and bounded data streams.
Graphs, Cypher, vectors, and ops hardening.
Neo4j is what you reach for when the relationships are the data. If your domain looks like a whiteboard of circles and arrows, forcing it into tables is painful.
Push URL updates to search engines after deploy.
Static sites and blogs change whenever you deploy. Search engines that support IndexNow can learn about those changes without waiting for the next blind crawl.
Serve open models fast with SGLang.
SGLang is a high-performance serving framework for large language models and multimodal models, built to deliver low-latency and high-throughput inference across everything from a single GPU to distributed clusters.
Install Kafka 4.2 and stream events in minutes.
Apache Kafka 4.2.0 is the current supported release line, and it’s the best baseline for a modern Quickstart because Kafka 4.x is fully ZooKeeper-free and built around KRaft by default.
Hot-swap local LLMs without changing clients.
Soon you are juggling vLLM, llama.cpp, and more—each stack on its own port. Everything downstream still wants one /v1 base URL; otherwise you keep shuffling ports, profiles, and one-off scripts. llama-swap is the /v1 proxy before those stacks.
Developing software involves Git for version control, Docker for containerization, bash for automation, PostgreSQL for databases, and VS Code for editing — along with countless other tools that make or break your productivity. This page collects the essential cheatsheets, workflows, and comparisons you need to work efficiently across the full development stack.
Self-host OpenAI-compatible APIs with LocalAI in minutes.
LocalAI is a self-hosted, local-first inference server designed to behave like a drop-in OpenAI API for running AI workloads on your own hardware (laptop, workstation, or on-prem server).
How to Install, Configure, and Use the OpenCode
I keep coming back to llama.cpp for local inference—it gives you control that Ollama and others abstract away, and it just works. Easy to run GGUF models interactively with llama-cli or expose an OpenAI-compatible HTTP API with llama-server.
Artificial Intelligence is reshaping how software is written, reviewed, deployed, and maintained. From AI coding assistants to GitOps automation and DevOps workflows, developers now rely on AI-powered tools across the entire software lifecycle.
Airtable - Free plan limits, API, webhooks, Go & Python.
Airtable is best thought of as a low‑code application platform built around a collaborative “database-like” spreadsheet UI - excellent for rapidly creating operational tooling (internal trackers, lightweight CRMs, content pipelines, AI evaluation queues) where non-developers need a friendly interface, but developers also need an API surface for automation and integration.
How to Install, Configure, and Use the OpenCode
OpenCode is an open source AI coding agent you can run in the terminal (TUI + CLI) with optional desktop and IDE surfaces. This is the OpenCode Quickstart: install, verify, connect a model/provider, and run real workflows (CLI + API).
Monitor LLM with Prometheus and Grafana
LLM inference looks like “just another API” — until latency spikes, queues back up, and your GPUs sit at 95% memory with no obvious explanation.
AWS S3, Garage, or MinIO - overview and comparison.
AWS S3 remains the “default” baseline for object storage: it is fully managed, strongly consistent, and designed for extremely high durability and availability.
Garage and MinIO are self-hosted, S3-compatible alternatives: Garage is designed for lightweight, geo-distributed small-to-medium clusters, while MinIO emphasises broad S3 API feature coverage and high performance in larger deployments.
Build workflows in Go with the Temporal SDK
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.