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Cover image generated by Nano Banana Disclaimer: A 50x speedup is not guaranteed. Actual performance depends on the nature of the dataset and the hardware on which the code is run. Please refer to the Benchmarks section below for more information. Introduction Recently, I finally found some time to learn the Rust programming language. I find its memory safety guarantee quite elegant, although it comes with the trade-off of a steep learning curve, especially when it comes to Rust’s ownership and lifetime system. It is very appealing to someone like me, who mainly uses a scripting language and who writes low-level code only from time to time. Writing C/C++ code can easily lead to unstable runtime behavior or unexpected results in such circumstances. ...

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. ...

taken from the PDF Preamble and a bit of personal story I recently ended a multi-year consulting engagement. I might publish a reflection on this experience sometime in the future, but for now, let’s just say it was quite mentally draining. I didn’t have any energy left to write public blog posts during my tenure there, as evidenced by the lack of new posts here over the past two years. ...

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 $f(x)$ about a point $x = a$ [2]: $$f(x) = f(a) + f'(a)(x-a) + \frac{f''(a)}{2!}(x-a)^2 + .. + \frac{f^{n}(a)}{n!}(x-a)^n + ...$$ We can approximate the cross-entropy loss using the Taylor series (a.k.a. Taylor expansion) using $a = 1$: $$f(x) = -log(x) = 0 + (-1)(1)^{-1}(x-1) + (-1)^2(1)^{-2}\frac{(x-1)^2}{2} + ... \\ = \sum^{\infty}_{j=1}(-1)^j\frac{(j-1)!}{j!}(x-1)^{j} = \sum^{\infty}_{j=1}\frac{(1-x)^{j}}{j} $$ We can get the expansion for the focal loss simply by multiplying the cross-entropy loss series by $(1-x)^\gamma$: ...