This work proposes a methodology for learning game dynamics in Slay the Spire, a roguelike deckbuilder video game where previous AI attempts have been stymied by combinatorial complexity: over 1000 distinct game effects, context-dependent card synergies, and decision horizons spanning 1000+ actions per run. Existing approaches have plateaued. Monte Carlo Tree Search methods (STS Lightspeed, Bottled AI) achieve only 30-52% win rates despite 600K+ playouts per decision, while reinforcement learning struggles with credit assignment across such long horizons. Sparse terminal rewards provide insufficient signal, and dense reward shaping introduces catastrophic reward hacking. Rather than fighting this complexity through search or end-to-end learning, this work takes a different approach: crystallizing the game state perfectly, maintaining exact numerical access to every variable including RNG, and learning the structure of the st
Thomas Friedel tfriedel
Uses uvx markitdown to convert many document and file types to Markdown.
Great replacement for markitdown-mcp.
Works in Claude (upload markdown-converter.skill),
Claude Code (expand the skill zip file into ~/.claude/skills/ or just create a skill directory with SKILL.md)
or in any agent using Skillz.
You are a teacher of algorithms and data-structures who specializes in the use of the socratic method of teaching concepts. You build up a foundation of understanding with your student as they advance using first principles thinking. Explain the subject that the student provides to you using this approach. By default, do not explain using source code nor artifacts until the student asks for you to do so. Furthermore, do not use analysis tools. Instead, explain concepts in natural language. You are to assume the role of teacher where the teacher asks a leading question to the student. The student thinks and responds. Engage misunderstanding until the student has sufficiently demonstrated that they've corrected their thinking. Continue until the core material of a subject is completely covered. I would benefit most from an explanation style in which you frequently pause to confirm, via asking me test questions, that I've understood your explanations so far. Particularly helpful are test questions related to sim
| # They may appear as warning if opening vscode through terminal, but they work. | |
| # Never use more than one --enable-features and --disable-features as it can overwrite the previous. | |
| # This was built using Arch Linux (Wayland) if some flags / features are not compatible with other OS, up to whoever uses, to remove them. | |
| # There are two threads that if ever get implemented into vscode, could bring improvements to the usage experience. | |
| # https://github.com/microsoft/vscode/issues/27378 | |
| # https://github.com/microsoft/vscode/issues/221145 | |
| # CPU |
This is a list of hacks gathered primarily from prior experiences as well as online sources (most notably Stanford's CS231n course notes) on how to troubleshoot the performance of a convolutional neural network . We will focus mainly on supervised learning using deep neural networks. While this guide assumes the user is coding in Python3.6 using tensorflow (TF), it can still be helpful as a language agnostic guide.
Suppose we are given a convolutional neural network to train and evaluate and assume the evaluation results are worse than expected. The following are steps to troubleshoot and potentially improve performance. The first section corresponds to must-do's and generally good practices before you start troubleshooting. Every subsequent section header corresponds to a problem and the section is devoted to solving it. The sections are ordered to reflect "more common" issues first and under each header the "most-eas
| # coding: utf8 | |
| """ | |
| * VAT: https://arxiv.org/abs/1507.00677 | |
| # 参考にしたCode | |
| Original: https://github.com/fchollet/keras/blob/master/examples/mnist_cnn.py | |
| VAT: https://github.com/musyoku/vat/blob/master/vat.py | |
| results example | |
| --------------- |
Note: this was written in April/May 2014 and the API may has definitely changed since. I have nothing to do with Tinder, nor its API, and I do not offer any support for anything you may build on top of this. Proceed with caution
I've sniffed most of the Tinder API to see how it works. You can use this to create bots (etc) very trivially. Some example python bot code is here -> https://gist.github.com/rtt/5a2e0cfa638c938cca59 (horribly quick and dirty, you've been warned!)