klotz: vibe coding*

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  1. This article examines how "vibe coding" – using LLMs to rapidly generate custom software – is transforming sensemaking and data visualization. Previously, bespoke tools demanded significant engineering resources or platform knowledge.

    However, the emergence of AI has lowered these barriers, allowing users to create "disposable" interactive tools tailored to specific research tasks.

    This empowers non-experts as "directors of design," but the author cautions against mindless trial-and-error, emphasizing the difference between exploratory tools for finding truth and classic visualizations for explaining it.
  2. >It is a 15KB, zero-dependency TypeScript library that allows for multiline text measurement and layout entirely in "userland," bypassing the DOM and its performance bottlenecks.
    >Pretext turns text blocks on the web into fully dynamic, interactive and responsive spaces, able to adapt and smoothly move around any other object on a webpage, preserving letter order and spaces between words and lines, even when a user clicks and drags other objects to intersect with the text, or resizes their browser window dramatically.
  3. Meta is heavily investing in AI integration, demonstrated through "AI Week" – intensive training sessions for employees. These weeks involve hackathons, demos, and hands-on experimentation with tools like Anthropic's Claude Code. The goal is to foster AI adoption across all job functions and seniority levels, with a focus on AI agents capable of automating tasks like coding and report generation.
    Meta is also restructuring teams into AI-native "pods" and setting specific AI adoption targets. CEO Mark Zuckerberg believes 2026 will see a significant impact of AI on the way Meta employees work, despite recent layoffs and the delayed launch of its own AI model.
  4. Simon Willison details creating a custom macOS presentation app, "Present," in just 45 minutes using Swift and SwiftUI. Frustrated with the risk of browser crashes when presenting a series of web pages, he built an app that displays URLs as slides, offering features like full-screen mode, keyboard navigation, and automatic URL saving. He even added remote control functionality via a web server and Tailscale.
    The project highlights the power of AI-assisted coding and expands his skillset, demonstrating how experienced software engineers can quickly learn new languages and tools to solve personal problems. The resulting app is a simple, effective solution tailored to his specific needs.
  5. Simon Willison explores "vibe coding" - building macOS apps with SwiftUI using large language models like Claude Opus 4.6 and GPT-5.4, without extensive coding knowledge. He successfully created two apps, Bandwidther (network bandwidth monitor) and Gpuer (GPU usage monitor), demonstrating the potential of this approach. The process involved minimal prompting and iterative development, leveraging the LLMs' capabilities for both code generation and feature suggestions.
    While acknowledging the need for caution regarding the apps' accuracy, Willison highlights the efficiency and accessibility of building macOS applications in this manner.
  6. zerobrew is a faster, modern Mac package manager that applies uv's model to Mac packages. It features a content-addressable store, APFS clonefile, parallel downloads, and streaming execution for dramatic speedups.
  7. Simon Willison’s annual review of the major trends, breakthroughs, and cultural moments in the large language model ecosystem in 2025, covering reasoning models, coding agents, CLI tools, Chinese open‑weight models, image editing, academic competition wins, and the rise of AI‑enabled browsers.
  8. An analysis of the current LLM landscape in 2026, focusing on the shift from 'vibe coding' to more efficient and controlled workflows for software development and data analysis. The author advocates for tools like AI Studio and OpenCode, and discusses the strengths of models like Gemini 2.5 Pro and Claude Sonnet.
  9. A Python-based log analyzer that uses local LLM (Llama 3.2 to explain the errors in simple language and summarise them (again, in simple language)
  10. Python ATAC-style green screen ADSB decoder and display.
    2025-11-08 Tags: , , , , , , by klotz

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