Defragging.ai is the field guide and assessment platform for organizations and builders reorganizing fragmented AI systems — across hardware memory, model context, agent state, and tool sprawl. We publish the canonical reference, run the diagnostic, and offer productized engagements for teams that want help executing.
Under it all, the same small editorial publication: essays written slowly, with citations, for people who would rather understand something than be the first to react to it.
The internet has no shortage of AI content. What it does not have enough of is writing that holds up a year later. That is the only test we care about.
An essay makes it onto Defragging.ai when it meets three bars: the question is worth asking, the answer is grounded in primary sources, and the prose earns the reader's time. If any one fails, the piece sits in the drawer until it doesn't.
We publish when an essay is finished, not when a news cycle demands a take. There is no posting schedule.
Every empirical claim links to a primary source with an access date. If we can't cite it, we don't say it.
We are uninterested in launches, valuations, and benchmark theatre. We are interested in what actually changes.
We assume you are smart and busy. We will not pad an essay, recap the obvious, or perform expertise.
Colophon
Type · Headlines and long-form prose set in Newsreader; UI in IBM Plex Sans; code in JetBrains Mono.
Palette ·
Ink #0B1220, Paper
#FFFFFF, Blue
#1E5BFF, Cyan
#00C2D1.
Built with · Astro, Tailwind, and MDX. Citations are first-class in the content schema — they ship with every essay.