TLDR Index
TLDR Web Dev

TLDR WEB DEV 2024-12-09

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01ARTICLES & TUTORIALS

WRITING DOWN (AND SEARCHING THROUGH) EVERY UUID

The website everyuuid.com displays all possible UUID v4s, which was a challenge because of the sheer number of them (2^122). To overcome browser rendering limitations, the creator implemented a virtual scrolling system that handled rendering and scrolling manually. Generating and displaying the UUIDs in a seemingly random yet consistent order required a custom algorithm that used a Feistel cipher to achieve a bijective mapping for scrambling (so that each UUID appeared exactly once). The creator added a search function which, while not perfectly efficient, allows users to find UUIDs containing specific substrings.

eieio.games19 min read

USING TRANSFORMERS.JS FOR AI IN THE BROWSER

Transformers.js enables AI-powered tasks directly in the browser using a simple pipeline API, supporting applications like sentiment analysis, summarization, translation, and object detection. It requires downloading significant model data but is lightweight enough for progressive enhancement, making it practical for many real-world use cases. An example is shown in this article through building an application that detects cats in images using the device camera.

raymondcamden.com5 min read

02OPINIONS & ADVICE

THE 70% PROBLEM: HARD TRUTHS ABOUT AI-ASSISTED CODING

AI-assisted development helps with rapid prototyping and daily tasks, but it still relies on human expertise for code quality and maintainability. Experienced developers use AI to speed up known tasks, while junior developers may struggle with AI-generated code, often missing important edge cases and other performance considerations.

addyo.substack.com9 min read

WRITING SYSTEM SOFTWARE: CODE COMMENTS

Well-written code needs comments. For example, the Redis source code has comments that clarify not only what the code does but also the crucial why, reducing cognitive load for readers. This article presents nine categories of comments, ranging from function and design comments to more nuanced types like "why," "teacher," and "checklist" comments, with examples illustrating their utility.

antirez.com27 min read

THE 6 MISTAKES YOU'RE GOING TO MAKE AS A NEW MANAGER

New engineering managers often struggle due to the reluctance to delegate tasks, a shift in dopamine sources from individual achievement to team success, and the temptation to prioritize team size over quality of output. Instead, they need to find the right level of engagement with team projects (guided autonomy) and manage perceptions both internally and externally.

terriblesoftware.org6 min read

03LAUNCHES & TOOLS

Sponsored

INTRODUCING WAITLIST MODE FROM CLERK

Waitlist mode is a new sign-up mode and component that lets users register interest and join a waitlist for early access to your product.

go.clerk.comSponsor

JSON5

JSON5 is a human-readable extension of the JSON format that adds features like comments and single-quoted strings, making it easier for devs to write and maintain configuration files.

json5.orgTool

MISE

Mise is a command-line tool that manages development tools, environment variables, and tasks, similar to asdf, direnv, and make.

github.comGitHub repository

COUNTLESS.DEV

Countless.dev is a free, open-source way to see and compare every AI model easily.

countless.devTool

04MISCELLANEOUS

WHY GELSINGER WAS WRONG FOR INTEL

Pat Gelsinger, the previous CEO of Intel, was the wrong CEO for Intel due to an underlying arrogance and disregard for limitations. This was evident in Gelsinger's narrative of his past successes, which minimizes his role in past failures and overstates his impact on competitors. Gelsinger's leadership perpetuated Intel's ingrained cultural problems, including a failure to listen to employees and customers.

bcantrill.dtrace.org14 min read

HOW I ALGORITHMICALLY DONATED $5,000+ TO OPEN SOURCE VIA GITHUB SPONSORS AND PYPI DATA

This developer created an algorithm to donate $5,000 to open-source maintainers on GitHub. The algorithm used GitHub Sponsors and PyPI data to identify underfunded projects based on download numbers, project size, security risk, and existing sponsorship. A weak correlation was found between project popularity and funding.

kvinogradov.com6 min read