Turn text into a meaning-bearing vector
one move: represent meaning as vectors
Open the code pane immediately, then use the Spec tab, optional design question, tests, and artifact when you want them.
Finish each move before carrying the artifact forward.
Embeddings place semantically similar sentences near one another in vector space.
Deliverable: Write the mechanism in one sentence.Encode sentences and compute cosine similarity between matched and distractor pairs.
Deliverable: Type the smallest runnable implementation.Vector dimension and normalization must be stable across repeated calls.
Deliverable: Record the expected output and one edge case.Compare paraphrases, unrelated sentences, and pooling strategies.
Deliverable: Change one variable and explain the result.An embedding card with similarity examples and model version.
Deliverable: Save the code, output, and a short failure note.Vector dimension and normalization must be stable across repeated calls.
Compare paraphrases, unrelated sentences, and pooling strategies.
An embedding card with similarity examples and model version.
Mapped from the supplied Build Everything PDF, source page 38. The five moves are Deriva’s implementation contract for this project.