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A collection of command-line scripts and tools for managing virtual machines using Libvirt, KVM, and QEMU, covering common VM operations, automation, and best practices.
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A guide to resolving OpenGL/EGL initialization and rendering issues when running ORB-SLAM3 in Docker containers on hosts with NVIDIA GPUs, covering proper GPU passthrough, EGL context setup, and common pitfalls.
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Learn how to quickly create a new terminal in Jupyter using a custom keyboard shortcut. This is especially useful for developers who frequently need to run shell commands while working in Jupyter notebooks.
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A comprehensive Docker setup for bioinformatics work, featuring non-root user configuration, Python virtual environments, R with Seurat, and Jupyter Lab with multi-profile docker-compose support.
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The term ‘HAL’ means different things in different domains—from kernel-level platform abstraction to application-level sensor control. Understanding these differences helps clarify domain-specific architecture choices.
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The HAL is not necessarily a module — it’s a boundary of responsibility between hardware-dependent and hardware-independent code.
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Quick notes to make a Cursor AppImage launchable from your desktop menus and include a proper icon.
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Having builds fail with:
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GraphHopper makes a deliberate design choice: paths are represented by edges, not nodes. This matters for correctness (turn restrictions/costs), fidelity (multiple parallel edges between the same nodes), and efficiency (edge properties drive routing).
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Install arm64 package directly in x86 ubuntu for cross compile.
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Some chats from ChatGPT
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Why use a multinomial function to get the next word prediction in a GPT (generative) model rather than outputting the id with the largest probability?
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Ubuntu下使用WPS, LibreOffice编辑文档、演示稿时，发现Ubuntu自带的（免费）字体丑。同样的文档在Windows，MAC下看着没问题，在Ubuntu下排版都乱了（字体宽度不一样）。将Windows的中文字体拷过来就好了。
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Fundamental Theorem of Linear Programming Given a linear program in standard form where A is an m × n matrix of rank m,
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Python demo of Figure 2.16 Sparse Error Correction via Logan’s Phenomenon from High-Dimensional Data Analysis with Low-Dimensional Models - John Wright, Yi Ma, Page 65
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Problem summary of High-Dimensional Data Analysis with Low-Dimensional Models - John Wright, Yi Ma
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Python implementation of Algorithm L1-Minimization by Projected Subgradient from High-Dimensional Data Analysis with Low-Dimensional Models - John Wright, Yi Ma, Page 63
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Let \(A\) be a matrix with size \(m \times n\). We want to project point z onto a plane \(\begin{align} \arg \min_{x} \lVert z-x \rVert_2 \\ \text{s.t. } Ax = y \end{align}\)
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Python implementation of Algorithm L0-Minimization by Exhaustive Search from High-Dimensional Data Analysis with Low-Dimensional Models - John Wright, Yi Ma, Page 48