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In this tutorial, we explore adaptive experimentation using Meta’s Ax with the modern Client API. We work through a complete workflow where we tune a RandomForest model on a synthetic classification dataset while balancing predictive accuracy against model footprint. We begin by defining a mixed search space with integer, float, log-scaled, and categorical parameters, then…

Onton, a San Francisco-based search and discovery company, has released Ontology 1, a neurosymbolic model for complex, conversational, multimodal product search. On a 90-query benchmark scored by three independent LLM judges, Ontology 1 reached a mean precision@10 of 0.630, against 0.543 for Google Shopping and 0.469 for Amazon. It did this while indexing roughly 1%…

print("\n[10] Plots") k = min(4, S) idxs = np.linspace(0, S - 1, k).astype(int) fig, axes = plt.subplots(3, k, figsize=(3.1 * k, 7.2)) axes = np.atleast_2d(axes) for c, i in enumerate(idxs): axes[0, c].imshow(rgb[i].transpose(1, 2, 0)); axes[0, c].set_title(f"frame {i}", fontsize=9) d = depth[i].squeeze(-1) axes[1, c].imshow(d, cmap="turbo", vmin=np.percentile(d, 2), vmax=np.percentile(d, 98)) axes[2, c].imshow(depth_conf[i] > THR,…