The Mac Studio Moment
When your own AI hardware starts to make sense, and why buying it should be a consequence, not the beginning.
The IETF writes the RFCs. The IEEE writes the cables. The MEF and OIF and IETF and ITU all write specifications that vendors implement. None of them write the document that tells you how to actually deploy any of it
When your own AI hardware starts to make sense, and why buying it should be a consequence, not the beginning.
Looking glasses go down. The hosted ones get neglected, the vendor-sponsored ones get retired when a vendor exits, the community-run ones suffer when the volunteer doing it changes jobs. Five-year
Local LLMs sound like rocket science at first. The first lesson is simpler: control, limits, and an honest view of performance.
I want to write about this carefully because the topic is real and the people doing the work deserve better than dramatic prose. A network operator group is not a
A non-technical glossary for the words that keep appearing in AI conversations: LLM, model, prompt, RAG, adapter, LoRA, MCP, gateway, and more.
I joined a NOG mailing list in 2019. It was the first time I'd been in a community where network operators talked to each other as peers rather
LLMs forget. Projects should not. Memory made of notes, decisions, and Git history turns single AI sessions into real progress.
The step after the chat window: not only asking AI, but letting it read files, change code, and verify results.
Cloud AI is a good starting point. But you need a simple rule for what belongs in the chat window and what does not.