Llm

I have tested how OpenCode works with several locally hosted on Ollama and llama.cpp LLMs, and for comparison added some Free models from OpenCode Zen.

The biggest capability jump in OpenCode comes from specialised agents: deliberate separation of orchestration, planning, execution, and research.

OpenHands is an open-source, model-agnostic platform for AI-driven software development agents. It lets an agent behave more like a coding partner than a simple autocomplete tool.

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).

Oh My Opencode turns OpenCode into a multi-agent coding harness: an orchestrator delegates work to specialist agents that run in parallel.

llama.cpp is a C/C++ inference engine for GGUF models: llama-cli for interactive chat, llama-completion for scripted prompts, and llama-server for an OpenAI-compatible HTTP API.

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.

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).

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

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.

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

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.

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.

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.

Observability is the foundation of reliable production systems.

Without metrics, dashboards, and alerting, Kubernetes clusters drift, AI workloads fail silently, and latency regressions go unnoticed until users complain.

Production-focused guide to building RAG systems: chunking, vector stores, hybrid retrieval, reranking, evaluation, and when to choose RAG over fine-tuning.