Python

Gemini CLI Usage Analyzer Project Banner Introduction Last week, I developed a lightweight command-line tool for analyzing Gemini CLI token usage and open-sourced it on GitHub. You can find the project at ceshine/gemini-cli-usage-analyzer. This post outlines why I built the tool, the technical challenges encountered during development, and the solutions implemented to resolve them. Note: Currently, the tool focuses on single-project analysis. Unlike Claude Code, which centralizes logs (e.g., in ~/.claude on Linux) to analyze overall cross-project usage by default, Gemini CLI lacks a built-in mechanism for unified log management across different projects. Support for aggregating statistics across multiple projects is on the development roadmap. ...

Photo Credit MaxViT: Multi-Axis Vision Transformer(1) is a paper jointly produced by Google Research and University of Texas at Austin in 2022. The paper proposes a new attention model, named multi-axis attention, which comprises a blocked local and a dilated global attention module. In addition, the paper introduces MaxViT architecture that combines multi-axis attentions with convolutions, which is highly effective in ImageNet benchmarks and downstream tasks. Multi-Axis Attention Source: [2] ...

Photo Credit Introduction Recall that an one-dimensional Taylor series is an expansion of a real function $f\left(x\right)$ about a point $x=a$ [2]: $$f\left(x\right)=f\left(a\right)+f^{\prime }\left(a\right)(x-a)+\frac{f^{\prime \prime }\left(a\right)}{2!}(x-a{)}^2+..+\frac{f^n\left(a\right)}{n!}(x-a{)}^n+...$$ We can approximate the cross-entropy loss using the Taylor series (a.k.a. Taylor expansion) using $a=1$: $$f\left(x\right)=-log\left(x\right)=0+(-1)\left(1{)}^{-1}\right(x-1)+(-1{)}^2(1{)}^{-2}\frac{(x-1{)}^2}{2}+...=\sum _{j=1}^{\infty }(-1{)}^j\frac{(j-1)!}{j!}(x-1{)}^j=\sum _{j=1}^{\infty }\frac{(1-x{)}^j}{j}$$ We can get the expansion for the focal loss simply by multiplying the cross-entropy loss series by $(1-x{)}^{\gamma }$: ...

credit Introduction At the start of 2022, we have a new pure convolution architecture (ConvNext)[1] that challenges the transformer architectures as a generic vision backbone. The new Visual Attention Network (VAN)[2] is yet another pure and simplistic convolution architecture that its creators claim to have achieved SOTA results with fewer parameters. Source: [2] What ConvNext tries to achieve is modernizing a standard ConvNet (ResNet) without introducing any attention-based modules. VAN still has attention-based modules, but the attention weights are obtained from a large kernel convolution instead of a self-attention block. To overcome the high computation costs brought by a large kernel convolution, it is decomposed into three components: a spatial local convolution (depth-wise convolution), a spatial long-range convolution (depth-wise dilation convolution), and a channel convolution (1x1 point-wise convolution). ...

credit Introduction Hierarchical Transformers (e.g., Swin Transformers[1]) has made Transformers highly competitive as a generic vision backbone and in a wide variety of vision tasks. A new paper from Facebook AI Research — “A ConvNet for the 2020s”[2] — gradually and systematically “modernizes” a standard ResNet[3] toward the design of a vision Transformer. The result is a family of pure ConvNet models dubbed ConvNeXt that compete favorably with Transformers in terms of accuracy and scalability. ...