OpenAI 2026 hackathon

Ripple

3ds Max 2024 bone physics plugin: deterministic swing for bone chains (ribbons, capes, etc.), baked to keyframes. Custom solver, no MassFX/PhysX. .mzp drag-drop install.

Solo project by Heyinian Heyinian · 1 likes · 0 comments

Archive position — measured, not model output

1 like on Devpost

506 of the 7,856 archived projects have more likes, and 1,758 share exactly 1 — so this project's #1,831 place in the like-ranked listing is a tie-break inside that group, not a ranking.

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Likes on Devpost. ▲ marks this project's group.

Show the figures
LikesProjectsShare of archive
05,59271.2%
11,75822.4%
22853.6%
3–41321.7%
5–9751.0%
10+140.2%
Devpost like counts for all 7,856 archived projects, captured when this archive was built.

Executive Summary

What the company appears to be

Ripple is a self-reported 3ds Max plugin for generating deterministic bone-based secondary motion (e.g., ribbons, capes, hair) through a custom physics solver. It allows artists to preview and bake keyframes directly from within 3ds Max 2024, with an emphasis on reproducibility and workflow integration.

What changed

The project description indicates development of a v0.4 build that supports bone chain registration, previewing, tuning, baking, and cleanup — all without external dependencies like MassFX or PhysX. It also describes architectural decisions around deterministic caching, unified physics core for gravity-on/off modes, and layered validation.

Single most important open question

Is there evidence of actual usage or adoption by 3D animation studios or artists beyond the author's own development efforts?

Note: This analysis is based solely on the self-reported project description provided. No external verification, traction data, revenue figures, or customer information are available.

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What The Product Actually Is

The description states that Ripple is a bone-physics plugin for 3ds Max 2024, designed to generate secondary motion (e.g., ribbons, capes) using a custom physics solver. It operates by:

  • Registering and validating bone chains;
  • Providing non-destructive cached previews with frame-by-frame playback and A/B comparison;
  • Baking approved results directly into keyframes;
  • Supporting undo, rollback on failure, and clean scene restoration after baking.

It is delivered as a .mzp package for drag-and-drop installation. The solver uses full-frame quaternion dynamics and fixed time steps, and integrates with MaxScript and pymxs APIs.

Claim: Ripple generates natural, controllable, and reproducible secondary motion.

Evidence: Described in the "What it does" section of the write-up.

Inference: The plugin is intended to streamline animation workflows by reducing reliance on hand-keying or fragmented tools.

Label: Inferred from self-description.

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Positioning & Claim Evolution

The author positions Ripple as a tool that turns bone-based secondary motion into a reliable and repeatable artist workflow. Key claims include:

  • Artists can preview what they get, and what gets baked is exactly what was previewed.
  • The tool avoids cloning existing tools or building render-grade simulators.
  • It supports both gravity-enabled and gravity-free motion through one core solver.
  • It returns results compatible with Unreal Engine 5 and Unity via FBX.

Claim: Ripple aims to make secondary motion predictable, deterministic, and fast enough for production use.

Evidence: Stated in the "Inspiration" and "What it does" sections.

Inference: The tool is positioned as a workflow enabler rather than a standalone simulation engine.

Label: Inferred from self-description.

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Target Customer & ICP

The description implies that Ripple targets 3D animators working in 3ds Max 2024, particularly those who work with rigged and skinned characters and need to produce secondary motion such as ribbons, capes, or hair.

Claim: The target user is an animator using 3ds Max for character animation.

Evidence: Stated in the "Inspiration" section.

Inference: The tool likely appeals to studios or individuals who want faster iteration and more control over secondary motion than current methods allow.

Label: Inferred from self-description.

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Business Model & Pricing Evidence

No explicit business model or pricing information is provided. The project is described as a hackathon submission, and no mention of monetization, licensing, or subscription models appears in the description.

Claim: No evidence of pricing or business model.

Evidence: Not evidenced.

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Technical & Delivery Signals

Ripple uses:

  • A layered architecture:
    • PySide2 UI layer
    • Application-service coordination
    • Host-adapter for Max integration (pymxs, controllers, keyframes)
    • Pure numerical solver in Python, NumPy, and native extensions
  • Full-frame rigid-body core with fixed time steps
  • SHA-256 fingerprinting for cache integrity
  • Deterministic behavior via fixed topology order, constraint iterations, and state-write order
  • Integration into standard FBX workflow for Unreal Engine 5 and Unity

Claim: Ripple uses a deterministic physics solver that supports preview, bake, and rollback.

Evidence: Described in the "How we built it" section.

Inference: The tool is engineered to be production-ready with robust error handling and validation.

Label: Inferred from self-description.

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Traction & Maturity Signals

There is no evidence of traction, customers, or adoption beyond the author’s own development work. The project is described as a v0.4 build, and the next milestone (v0.5) involves implementing collision — indicating early-stage development.

Claim: No evidence of usage or customer base.

Evidence: Not evidenced.

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Competitive Context

The description does not reference direct competitors or market positioning beyond stating that Ripple is not meant to clone existing tools or build render-grade simulators. It also notes that hand-keying and fragmented scripts are current alternatives, but these methods are criticized for being slow or inconsistent.

Claim: Competes with hand-keying and disconnected scripts.

Evidence: Stated in the "Inspiration" section.

Inference: Ripple may be positioned against tools like MassFX or PhysX, though it avoids using them.

Label: Inferred from self-description.

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Key Risks & Red Flags

  • Early-stage development: v0.4 is described as incomplete; collision support is planned for v0.5.
  • No external validation: No third-party reviews, user feedback, or real-world testing beyond internal audits.
  • Limited scope: The tool only supports bone chains and does not yet include advanced features like collisions or force fields.
  • Self-reported maturity: All evidence comes from the author’s own account; no independent verification exists.

Claim: Development is in early stages with incomplete functionality.

Evidence: Described in "What it does" and "What's next for Ripple".

Inference: Lack of external validation raises questions about real-world utility or adoption.

Label: Inferred from self-description.

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Diligence Questions To Ask The Founders

  1. What is the current level of testing beyond unit tests, and how are visual checks validated?
  2. Are there any known performance bottlenecks in large scenes (e.g., >100 bones)?
  3. How does Ripple handle edge cases or unexpected inputs during preview/bake cycles?
  4. Has the tool been tested in real production environments or by other animators?
  5. What are the plans for integrating with other DCC tools beyond 3ds Max?
  6. Is there a plan to support additional physics features (e.g., collision, wind) in near-term releases?

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Investment/Partnership Verdict

There is no evidence of revenue, customers, or traction. The project is described as a hackathon submission and early-stage development effort. While the technical architecture appears thoughtful and well-documented, there is no indication that Ripple has reached a point where it could be considered for investment or partnership.

Claim: No commercial traction or viability demonstrated.

Evidence: Not evidenced.

Inference: The tool may have potential as a niche plugin but lacks data to support any strategic interest.

Label: Inferred from self-description.

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Source

Submitted to the OpenAI 2026 hackathon on Devpost. Project home on DevPost.

The analysis above was generated by a language model from the project's own one-line description. It is not independent research and contains no verified traction, revenue or customer data.