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.

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).

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.

LLM inference looks like “just another API” — until latency spikes, queues back up, and your GPUs sit at 95% memory with no obvious explanation.

Most local AI setups start the same way: a model, a runtime, and a chat interface.

OpenClaw is a self-hosted AI assistant designed to run with local LLM runtimes like Ollama or with cloud-based models such as Claude Sonnet.

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.

Temporal is an open-source, enterprise-grade workflow engine that enables developers to build durable, scalable, and fault-tolerant workflow applications using familiar programming languages like Go.

Garage is an open-source, self-hosted, S3-compatible object storage system designed for small-to-medium deployments, with a strong emphasis on resilience and geo-distribution.

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.

Choosing the right browser automation stack and webscraping in Go affects speed, maintenance, and where your code runs.

Desktop launchers on Ubuntu 24 (and most Linux desktops) are defined by .desktop files: small, text-based config files that describe an application or link.

The AWS Free plan is not working for me and Pay-as-you-go is hidden for new CloudFront Distributions on AWS Console .

Choosing the right browser automation stack in Python affects speed, stability, and maintenance. This overview compares Playwright vs Selenium vs Puppeteer vs LambdaTest vs ZenRows vs Gauge - with a focus on Python, while noting where Node.js or other languages fit in.

Two strong options for building terminal user interfaces today are BubbleTea (Go) and Ratatui (Rust). One gives you an opinionated, Elm-style framework; the other a flexible, immediate-mode library.