Tons of great anti-llm evidence here

The generative AI industry is killing open source and open access

ChatGPT “power users,” or those who use it the most, are becoming dependent upon — or even addicted to — the chatbot. 

This discusses the rapid evolution of coding practices, particularly the rise of “vibe coding,” where AI handles most of the coding tasks. Overall, the article emphasizes the transformative potential of AI in software development and the need for the industry to adapt to these changes quickly.

Prototype and productionize AI features with a modern JS/TS stack

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

Good (negative) review of Perplexity.

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

A guide on how to convert .epub e-books into high-quality audiobooks narrated by neural text-to-speech

this puts even state-of-the-art current AI systems in a position where professional responsibility dictates the avoidance of them in any serious application.

Notes on the Practical Wisdom of AI Use

Prompt, run, edit. Create web applications with AI help.

Still, I have reservations about AI in radiology, particularly when it comes to education. One of the main promises of AI is that it will handle the “easy” scans, freeing radiologists to concentrate on the “harder” stuff. I bristle at this forecast, since the “easy” cases are only so after we read thousands of them during our training—and for me they’re still not so easy! The only reason my mentors are able to interpret more advanced imaging is that they have an immense grounding in these fundamentals.