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Immersive Portals (iPortal)

website·Indexed 33 days ago

https://qouteall.fun/immptl/

Documentation for the Immersive Portals mod, featuring seamless portals with spatial transformations including translation, rotation, scaling, and mirroring. Includes guides on portal customization using the Portal Wand and Portal Helper, datapack-based portal generation, dimension management, and a comprehensive reference for /portal commands. Also provides API documentation for other mods and details on the Portal Gun Fabric mod.

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What's inside Immersive Portals

  1. Overview of Immersive Portals for Minecraft

    Immersive Portals is a Minecraft mod that implements seamless, transparent portals, allowing players to see through the portal to the destination and teleport without a loading screen.

  2. Overview of Spatial Transformations in ImmPtl

    In the ImmPtl mod, portals act as windows linking to transformed spaces. Supported spatial transformations include translation, rotation, scale, and mirroring. Teleportation is eye-based: when an entity passes through a portal, the mod calculates the transformed eye position and places the entity accordingly.

  3. Overview of Immersive Portals Minecraft Mod

    Immersive Portals is an open-source Minecraft mod that enables see-through portals and seamless teleportation. Key features include:

    • Rotation Transformation: Portals can be rotated.
    • Scale Transformation: Portals can be scaled to different sizes.
    • Dimension Stack: Support for stacking dimensions.
    • Mirror: Ability to create mirrored portals.

  4. Overview of Portal Animation Systems

    Introduced in version 2.3.0, there are two types of portal animations:

    1. Default Animation: Triggered by /portal commands. It is client-side only (server-side movement is abrupt), does not support relative teleportation, and only handles transitions.
    2. Custom Animation: Controlled by animation driver data. It supports multiple simultaneous animations, complex movements, both client and server-side animation, and relative teleportation (teleporting the player if the portal moves through them).

  5. Identify scenarios where dataflow and source-of-truth changes break abstractions

    Abstractions can break when the fundamental dataflow or the source of truth for a system must change due to scaling or performance requirements. Common patterns of abstraction failure include:

    1. Transition from Bulk Loading to Dynamic Loading: A UI that initially loads all data from a server before rendering becomes non-viable as data size grows, requiring a shift to partial, dynamic loading and incremental visualization.
    2. Transition from Discrete to Seamless State Transitions: A system designed with explicit loading screens between scenes (e.g., in a game) may have its abstraction broken when a requirement for seamless transitions is introduced.
    3. Transition from In-Memory Processing to Distributed/Database Processing: Logic that loads all data into memory for computation fails when data exceeds memory limits. This requires a structural change to either implement partial loading/merging or migrate the business logic directly into SQL for database-side computation.

  6. Avoid the Nominal Fallacy

    The Nominal Fallacy occurs when someone assumes they understand a concept based solely on its name. For example, assuming an LLM is a heat-based algorithm because it has a 'temperature' setting, or assuming 'chip packaging' is simply putting a chip in a box.

  7. Understand the Fundamental Attribution Error

    The Fundamental Attribution Error describes the tendency to attribute behaviors differently based on whether the subject is oneself or another person:

    • Self: Success is attributed to internal characteristics (capability, virtue); failure is attributed to external factors (luck, situation).
    • Others: Success is attributed to external factors; failure is attributed to internal characteristics.

  8. Understand Availability and Vividness Biases

    Availability bias occurs when immediate, easily recalled examples disproportionately influence thinking (e.g., fearing plane crashes more after seeing a movie about one, despite statistics). Vividness bias is a related tendency to trust vivid stories and anecdotes over abstract statistical evidence, contributing to the anecdotal and narrative fallacies.

  9. Understand the concept of Jagged Intelligence in LLMs

    LLMs exhibit 'Jagged Intelligence,' meaning their capabilities are non-linear and high-dimensional. They can outperform human experts in specific, complex tasks (e.g., solving PhD-level exam problems) while failing catastrophically at simple tasks that are intuitive to humans. Unlike human intelligence, which tends to improve linearly across correlated capabilities, AI performance is 'spiky.' This implies that a combination of humans and AI tools—where each is strong in 'orthogonal' directions—can achieve significantly higher performance than any single entity.

  10. Use Immersive Portals alternate dimensions for vertical stacking

    The Immersive Portals mod provides 5 additional dimensions that feature skies similar to the Overworld. These dimensions are specifically designed to be used for vertical dimension stacking. These alternate dimensions can be disabled if not needed.

  11. Overcome the Curse of Knowledge in communication and AI prompting

    The Curse of Knowledge is the assumption that others possess the same background knowledge as you, leading to the omission of critical details.

    Practical Applications:

    • Learning: Ask 'stupid' (fundamental) questions; if a fundamental question lacks a sound answer, a critical gap in understanding may exist.
    • AI Prompting: Avoid assuming the AI knows your specific work context. To prevent generic outputs, provide a document containing detailed work knowledge, including failed attempts, to give the AI necessary context.

  12. Apply conditional probability and Bayes Theorem

    Conditional probability $P(E|C)$ is the probability of event $E$ occurring given that event $C$ has occurred.

    Fundamental Formulas:

    • Conditional Probability: $P(E|C) = \frac{P(E \cap C)}{P(C)}$
    • Joint Probability: $P(E \cap C) = P(E|C) \cdot P(C)$
    • Independence: If $E$ and $C$ are independent, $P(E \cap C) = P(E)P(C)$ and $P(E|C) = P(E)$.
    • Bayes Theorem: Allows reversing conditional probabilities: $P(A|B) = \frac{P(B|A) \cdot P(A)}{P(B)}$.

    Key Terms:

    • Prior: The assumed distribution before new information is known.
    • Posterior: The updated distribution after incorporating new information.