There continue to be reasons for software to be slow

A discussion on the various reasons why performance is slow, and which ones are unlikely to be affected by LLMs. Originally written as a counter-point to Dan Luu

Fast and Hard Code

People pick languages differently with LLMs.

What's new with ML in production

What's different about LLMs versus traditional ML

Research – Inception

We are leveraging diffusion technology to develop a new generation of LLMs. Our dLLMs are much faster and more efficient than traditional autoregressive LLMs.

The LLM Critics Are Right. I Use LLMs Anyway.

I almost agree with all of the LLM critics, yet I still use LLMs a lot. I know this sounds like I am delusional, but I don't think I am alone with it.

The LLM Critics Are Right. I Use LLMs Anyway.

I almost agree with all of the LLM critics, yet I still use LLMs a lot. I know this sounds like I am delusional, but I don't think I am alone with it.

LLMs are not like manufacturing machines - Ross Wintle

LLMs are not just machines. They are machine-building machines! This comes with way more responsibility. And understanding it may change how you use them.

The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities (Version 1.0)

chad/whichlang

What programming language do LLMs default to when you don't tell them? A small benchmark.

Fine-Tuning LLMs is a Huge Waste of Time

People think they can use Fine-Tune for Knowledge Injection. People are Wrong

Dan Shipper 📧 on Twitter / X

this is true and is a big reason why you don’t need to be a highly technical researcher to use LLMs in surprising and novel ways https://t.co/TuxNzXzToU— Dan Shipper 📧 (@danshipper) July 27, 2025

Introducing Mercury 2.5 – Inception

Mercury 2.5 is the most capable diffusion LLM on the market. It runs at 1,107 tokens/sec and offers a 40% increase in intelligence over Mercury 2, comparable to cost-optimized frontier models.