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A graph in the context of AI work is simpler than it sounds: it's a map of which jobs need to happen and what each one depends on.

If you're new to Iroh, my mental model is roughly "Tailscale at the application layer instead of the network layer".
If your question is, "why not just use Tailscale?", look at it from an app developer's perspective. If you want to release an app and have instances of your app be able to easily connect to each other, you could theoretically embeded Tailscale functionality into your app, but then the users of your app need Tailscale accounts, and your app is dependent on Tailscale.
Iroh lets you embed this functionality directly, and provides public fallback relays. If your app gets too big for the public relays, using your own relays is the flip of a switch.

One threat we’re particularly focused on is prompt injection, and we handle it in layers. The model is trained to recognize and resist it. The harness marks anything coming from an untrusted source. Deterministic code checks the result. And an ensemble of classifiers runs where the agent can't reach them.

a turn by turn context awareness enables a context engineering mindset. mastering llms is all about mastering malloc().

give Sol a grid with a "randomized" obstacle to navigate.
Tell it to use a hashmap solution for formulating pathing.
As soon as it runs into its first pathfinding problem (It will) it will start writing edge-case solutions, rather than fixing the core hashmap.

with CmdAtom, a lot of "leader key" mappings will just be unnecessary, bc you can compose macros on the fly.