Migrating an AI assistant is not the same as copying an application config. The hard part is preserving identity, memory, tool behavior, scheduled work, and messaging access without two gateways acting as the same bot.

Ollama and llama.cpp are often compared as if they were rival inference engines. The real choice is between a managed model service and a toolkit you operate directly.

ROCm and Vulkan both accelerate AMD GPUs for local LLM hosting, but they are not interchangeable. The right choice depends on the engine, GPU, and workload.

A model can advertise a 128K context window and still fail at 40K tokens on a 16 GB GPU. The architecture ceiling never promised that weights, KV cache, compute buffers, and the desktop compositor would fit on your card at the same time.

AI hype follows a rhythm: roughly every three years, the architecture everyone is betting on gets replaced by something newer. The next shift is already forming in research labs.

Agent Skills and MCP servers are often presented as competing ways to extend an AI agent. That framing is wrong: a skill teaches the agent how to work, while an MCP server gives it governed access to live capabilities.

OpenCode’s command-line interface is built for scripting, CI pipelines, and unattended agent runs. This article is a practical guide to using it in daily work.

Superpowers packages a full spec-driven methodology into installable Claude Skills, enforcing brainstorming, planning, subagent-driven implementation, and strict TDD instead of leaving that structure up to you.

Pi Coding Agent is a minimal, open-source terminal coding harness that ships with four default tools and leaves most of the behaviour to extensions, skills and your own workflow.

Markdown looks like one language until the same file renders differently on GitHub, Hugo, Obsidian, or Pandoc. And the problem is not that Markdown is unreliable.

Every API call feels like a simple transaction - until enough of them accumulate that your fine-tuning data, evaluation harnesses, and tool schemas are all shaped around one vendor, and switching stops being a routing change.

Most Claude Code sessions get slow and cluttered for the same reason: every exploratory grep, every log dump, and every “let me check one more file” stays in the main conversation forever.

Ollama is one of the easiest ways to run a local language model, but convenience can conceal the moment when a local experiment becomes a shared inference service that needs better scheduling and observability.

AI coding agents ship features fast, but specs, tests, and code quietly drift apart. This guide covers a traceability model, spec-to-test and spec-to-code mapping, and the CI checks that catch drift before a merge.

A dead-letter queue is the safety net that catches messages your consumers cannot process, so one broken payload does not block or silently drop everything behind it in the queue.

An LLM Wiki fails when old facts remain plausible, contradictions become polished, and generated summaries drift from their sources.