figment / docs /figment-build-small-posts-index.md
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Figment Build Small Blog Drafts

Draft status: working index for the Build Small writing set.

1. Building Figment For Build Small: What I Learned About Making Small Models Useful

File: docs/figment-build-small-lessons-draft.md

Audience: judges, builders, and public readers who want the high-level story.

Core argument: useful small-model apps are systems of restraint, and that restraint turns failures into an iteration engine.

Best current evidence to preserve in revisions:

  • hosted Omni split between app safety and model competence,
  • local v3 field-workflow holdout jump to 107/150,
  • v5 false comfort: 150/150 final validation but only 2/150 model competence,
  • corrected scoring view with six changed cases,
  • v14p repair-union: 150/150 competence/final validation/expected labels, zero deterministic patches, zero fallback, with 146/150 raw success and 4/150 model-repair cases,
  • Field Kit Workbench UI as a clearer product surface without changing the harness contract,
  • public Hugging Face artifacts and trace receipts.

2. The Eval Loop That Made Figment Better

File: docs/figment-build-small-eval-loop-draft.md

Audience: builders who want the technical learning loop.

Core argument: final app validation is not model competence; the later Figment loop worked because raw success, repair success, deterministic patches, and fallback were measured separately.

Best current evidence to preserve in revisions:

  • v5 as the run that prevented a misleading success story,
  • v6/v7 targeted replay-and-delta curriculum,
  • corrected benchmark hygiene instead of training around scorer bugs,
  • prompt-policy probes that made results worse,
  • v14p repair-union as a narrow but defensible system claim.

Likely Follow-Up Angles

  • A demo-focused post or submission section comparing Figment's evidence depth against clearer Backyard demos.
  • A short "what the trace shows" post with screenshots or annotated examples.
  • A public artifact guide that points readers to the model repo, dataset configs, and selected eval traces.