Affinity Model
Affinity is emergent — store reactions, derive affinity as executable graph logic.
The idea
No ratings table, no affinity column. People react; affinity is what the graph derives from it.
Reactions are facts
Store what people did. Each tap is one write into the semantic tree.
me.contexts[123].targets[1].hearts(2)
Affinity is derived
A formula, not a stored number. When a reaction changes, only the nodes that depend on it recompute (k).
targets["[i]"]["="]("affinity_score",
"score_sum / reactions")explain() shows why
Ask any derived value for its receipt: the expression and every input value it used.
me.explain("contexts[123].targets[1].affinity_score")Live: the real kernel in your browser
Tap a reaction. Every number is read from the kernel, and the explain() panel is the kernel's own output.
Facts → derived contexts[123]
Writes k = nodes recomputed (explain().meta.k)
waiting for the kernel…
What runs here: the unmodified this.me@4.1.0 (jsDelivr, unpkg fallback), sha256-checked before import. 4.1.0 formulas are arithmetic and comparisons over paths: no string literals, ternaries, map/filter/reduce, sum or count. So the walkthrough's emoji ternary becomes weight facts, reactions are per-target tallies, sums are explicit, and indices use brackets (targets[1]; targets.1 silently reads undefined). With zero reactions, score_sum / reactions divides by zero and the kernel returns undefined. Page code only draws, sends the writes, and runs the fresh-rebuild comparison.
Every call this demo made to seed the kernel
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Go deeper
- Affinity Model — kernel walkthroughThe full write-up: reactions, broadcast formulas, secret scopes, explain().
- Affinity_Model.tsThe kernel's own demo script, with assertions.
- This page's sourceOne HTML file: kernel loader, facts, formulas, rendering.
- this.me@4.1.0 on npmThe exact kernel version running above.
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