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Falsiflow 1k-Star Launch Plan

This plan turns the adoption priorities into concrete public-launch work. It is not a promise that stars will arrive; it is the checklist for making the project easy to understand, safe to try, and worth sharing.

Launch Gate

Do not start broad distribution until these checks are green and linked from the release notes:

  • GitHub Pages public demo loads at https://azurliu.github.io/falsiflow/.
  • make test passes on the release commit.
  • falsiflow release-check --out-dir data/falsiflow/release_check --force reports release_ready, package_ready, and adoption_ready.
  • Falsiflow Publish has a successful release-triggered publish-pypi job.
  • https://pypi.org/pypi/falsiflow/json reports package falsiflow at the release version.
  • Falsiflow External Evidence reports external_ready with the public demo, PyPI, checkout pipx, public-package pipx, and Windows smoke evidence.

If any item is missing, the launch can still be prepared, but public posts should say what is still pending instead of implying full external readiness.

First-Screen Conversion Assets

  • README headline: CI gates for claims before they ship.
  • Public demo: a ready/blocked proof surface for AI eval, product metric, R&D, and vendor handoff claims.
  • One command path: pipx install falsiflow, with source installs kept for contributors.
  • Reusable GitHub Action snippet for downstream repositories.
  • Maintained downstream smoke fixture: examples/downstream_ai_eval_smoke.
  • Product-metric downstream smoke fixture: examples/downstream_product_metric_smoke.
  • Live downstream proof repo: https://github.com/AzurLiu/falsiflow-downstream-ai-eval-demo.
  • Shareable downstream proof strip: docs/assets/falsiflow_downstream_pr_proof_strip.svg.
  • Comparison boundary: Falsiflow complements Great Expectations, Evidently, Deepchecks, MLflow, and plain GitHub Actions rather than replacing them.

Demo Narrative

The public demo and launch posts should repeat the same compact story:

  1. A team writes a claim such as "the model improved" or "activation lifted."
  2. Placeholder evidence stays claim_check_blocked.
  3. Source-backed rows, required metadata, source files, audit reports, and bundle verification can make the same claim claim_check_ready.
  4. claim_ready means the configured evidence gate passed. It is not proof of scientific truth, safety, regulatory approval, or business impact.

Distribution Sequence

Day 0:

  • Use the current PyPI release, live PR demo, and external-evidence workflow as the launch trust baseline.
  • Update the release body with the demo URL, PR #17, blocked/ready PR runs, PyPI URL, CI runs, publish run, external-evidence workflow, and responsible-use boundary.
  • Post a concise Show HN or equivalent launch thread.
  • Post the longer launch note in one MLOps or evaluation community.
  • Share the GitHub Action snippet with a copy-paste downstream smoke example. Use examples/downstream_ai_eval_smoke as the maintained fixture. Link the live downstream PR story: https://github.com/AzurLiu/falsiflow-downstream-ai-eval-demo/pull/1. Use docs/assets/falsiflow_downstream_pr_proof_strip.svg when a full screen recording is too heavy.
  • Share examples/downstream_product_metric_smoke with product or growth engineering readers who care more about activation, conversion, retention, guardrail, or rollback-readiness claims than model evals.

Day 1:

  • Reply to install failures and unclear positioning questions first.
  • Convert repeated questions into issues with labels, owner, affected artifact, and verification command.
  • Submit to one or two relevant awesome lists only after the README and PyPI path are stable.

Day 3:

  • Publish the first article draft from docs/launch_articles/.
  • Add screenshots or short clips captured from the live Pages demo.
  • Review traffic, stars, forks, clones, demo visits, installs, and repeated questions in launch_metrics.md.

Day 7 and Day 14:

  • Publish the second and third articles.
  • Ship one docs/demo improvement based on real user confusion.
  • Add or refine one starter template only if the request includes a clear claim, evidence contract, source-file fixture, and responsible-use boundary.

1k-Star Workstreams

Workstream Outcome Evidence
Release trust PyPI, CI, external evidence, and release notes are coherent. Release body, CI URLs, PyPI JSON, external-readiness artifact.
Fast try A new visitor can understand and run the project in minutes. README first screen, Pages demo, quickstart, GitHub Action example.
Strong positioning The project has a crisp reason to exist. Positioning doc, comparison table, launch posts, replies to common questions.
Case breadth The workflow is not a one-domain toy. Public casebook, casebook-check, AI eval, product metric, R&D, vendor cases.
Contribution path New contributors can help without touching the core engine. Good-first issues, issue templates, contributing guide, docs/demo tasks.
Repeatable launch review Growth work creates maintainable follow-up, not vague activity. launch_metrics.json, launch_metrics.md, weekly maintainer review.

Article Drafts

Stop Conditions

  • Do not publish a release announcement if PyPI returns 404 for the package JSON API.
  • Do not call the project externally ready until external-check --strict reports external_ready.
  • Do not frame stars as quality proof. Track stars as distribution feedback, then judge the project by install success, issue quality, contributor pull requests, and repeated use cases.