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| Fantastic work, Claude! Love what we came up with—that’s exactly what people want. Let’s start by updating #file:ACTION_PLAN.md and deleting #file:M3_COMPLETION_TODO.md. Once that’s done, create a new file in the project root called TODO.md with an unordered [ ] to-do list containing short descriptions for each item (no nesting). Update the instructions in #file:copilot-instructions.md and #file:AGENTS.md to reflect this process. After updating the documentation, commit the changes to the current branch, push them, and then begin working on the tasks in TODO.md. |
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| {"raw": "X-SPANFORMER\nSPAN-AwARE ENCODER\n5.4 Qualitative Span Interpretability\nTo assess the plausibility and semantic alignment of X-Spanformer's induced spans, we perform side-by-side comparisons against syntactic and semantic reference structures. Using single-sentence prompts drawn from the validation sets of WikiText and Stream-Mix, we visualize the top-K spans selected at various layers and entropy regimes. We benchmark span boundaries against:\nSyntactic parses: Constituents produced by Berkeley Neural Parser", "type": "mixed", "id": {"id": "a0409606-f532-4dd2-b02e-2a0bae5bfeee"}, "meta": {"status": "keep", "tags": [], "doc_language": "en", "extracted_by": "pdf2seg", "confidence": 0.78, "source_file": "XSpanformer_TokenizerFree_SpanAwareEncoder_RawsonChrzanowski_Preprint_v1.0_2025-06-26.pdf", "notes": "The segment contains a mix of technical terms and structured information that can be segmented into meaningful spans, such as \"X-SPANFORMER,\" \"SPAN-AwARE ENCODER,\" specific versions like \"5.4 Qua |
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| import torch | |
| import torch.nn as nn | |
| import torch.optim as optim | |
| # Example model | |
| class SimpleNN(nn.Module): | |
| def __init__(self, input_size, hidden_size, output_size): | |
| super(SimpleNN, self).__init__() | |
| self.fc1 = nn.Linear(input_size, hidden_size) | |
| self.fc2 = nn.Linear(hidden_size, output_size) |
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| <!DOCTYPE html> | |
| <html lang="en"> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <meta name="viewport" content="width=device-width, initial-scale=1.0"> | |
| <title>Multiple Wobbly Plasma Bubbles</title> | |
| </head> | |
| <body> |
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| ## You | |
| hi copilot, attached is our first conversation together in a series of conversations about our MarsGame made in unity 2023 HDRP with netcode for gameobjects. We just implement the basic game system state machine and everything seems to be working, i will attach part 2 of our last conversation in the next prompt | |
| ## Copilot | |
| Hello, I'm glad to hear that you are making progress on your MarsGame project. It sounds like an exciting and challenging endeavor. 😊 | |
| I have read the file you uploaded, which contains the first part of our conversation about your game. Here are some of the main topics we discussed: | |
| - The game concept and setting: You explained that your game is a multiplayer survival game set on Mars, where players have to explore, gather resources, build bases, and fight enemies. | |
| - The game engine and graphics: You chose to use Unity 2023 HDRP, which is a high-end rendering pipeline that supports realistic lighting, shadows, reflections, and materials. You also mentioned some of the assets and tools |
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| using System; | |
| using System.Collections.Generic; | |
| using UnityEngine; | |
| namespace HFSM | |
| { | |
| public abstract class StateMachine<T> | |
| { | |
| T ctx; |
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| using System; | |
| public class Singleton<T> where T : class, new() | |
| { | |
| // The single instance of the type T | |
| private static readonly Lazy<T> instance = new(() => new T()); | |
| // The private constructor to prevent outside instantiation | |
| public Singleton() { } |
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| using System; | |
| using System.Collections.Generic; | |
| using UnityEngine; | |
| namespace HFSM | |
| { | |
| public class StateMachine<T> : Singleton<StateMachine<T>> | |
| { | |
| T context; |
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| using System; | |
| using System.Collections; | |
| using System.Collections.Generic; | |
| using UnityEngine; | |
| namespace HFSM | |
| { | |
| /// <summary> | |
| /// Our General Purspose Hieracrhial State Machine Engine. This class allows | |
| /// sub states to bind addition nodes of states to create a tree structure. We also |
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| ~/code/as-boilerplater | |
| ➜ npm init | |
| This utility will walk you through creating a package.json file. | |
| It only covers the most common items, and tries to guess sensible defaults. | |
| See `npm help json` for definitive documentation on these fields | |
| and exactly what they do. | |
| Use `npm install <pkg>` afterwards to install a package and | |
| save it as a dependency in the package.json file. |
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