A detailed walkthrough of my current workflow for using LLms to build software, from brainstorming through planning and execution.

One paper that caught my attention a bit ago was Selective attention in hypothesis-driven data analysis by Itai Yanai and Martin Lercher. In their study, students who were given specific hypotheses to test were much less likely to notice an obvious “gorilla in the data” compared to students who explored the data freely.

• https://www.zyphra.com/post/beta-release-of-zonos-v0-1
• https://elevenreader.io/

It also raises important questions about whether AI will exacerbate or mitigate existing economic inequalities — particularly if lower-wage workers have less access to AI’s productivity-boosting benefits.

• https://anythingllm.com/

The AI Daily brief Feb 2, 2025

https://github.com/anthropics/prompt-eng-interactive-tutorial

LlamaParse is the world’s first genAI-native document parsing platform - built with LLMs and for LLM use cases.