# Messaging Toolkit for ZeroProofML v0.4.0

## Landing Page Copy
- **Headline:** "ZeroProofML v0.4 — Train on Smooth, Infer on Strict"
- **Subhead:** "Signed Common Meadows with projective training for robust rational models."
- **Bullets:**
  - Totalised arithmetic with absorptive bottom `⊥`; no transreal tags.
  - Optional projective tuples `(N, D)` keep optimizers smooth while enforcing strict SCM semantics at inference.
  - Gradient policies + coverage metrics for safety-critical robotics and control.
  - SCM-ready losses (implicit, margin, sign) with deterministic inference thresholds.
- **CTA Buttons:** "Read the v0.4.0 blog" (link to `docs/announcements/v0_4_0_blog.md`) and "Install from PyPI" (`pip install zeroproofml==0.4.0`).

## Social Posts
- **Twitter/X:**
  - "ZeroProofML 0.4.0 lands: Signed Common Meadows replace transreal arithmetic. Train on smooth projective tuples, infer with strict `⊥` semantics. Coverage-first losses + 90% CI gate. pip install zeroproofml==0.4.0 #robotics #mlops"
- **LinkedIn:**
  - "We just shipped ZeroProofML v0.4.0, completing the Algebraic Turn. The library now runs on signed common meadows with a single absorptive bottom `⊥`, optional projective tuples for smooth training, and SCM-aware loss/coverage controls. Read the blog, then `pip install zeroproofml==0.4.0`."
- **Mastodon:**
  - "ZeroProofML v0.4.0 is out! Signed Common Meadows (SCM), projective tuples for rational heads, and strict coverage gates. Train on smooth, infer on strict. `pip install zeroproofml==0.4.0`. #AI #Robotics #NumericalStability"

## Email/Newsletter Snippet
"ZeroProofML 0.4.0 completes our shift to signed common meadows. Division by zero now yields an absorptive `⊥`, gradient policies keep learning stable, and optional projective tuples smooth out rational heads without guard logic. CI enforces 90% coverage and release-ready wheels are published. Try it via `pip install zeroproofml==0.4.0` and read the full release notes."
