What's different about LLMs versus traditional ML
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)
LLMs and performative productivity
It's worth asking whether LLMs are actually making us more productive at all—and if so, what we might be sacrificing in return.
distil labs – Cut LLM Costs by 80% with Custom SLMs
Your LLM costs are eating your product margins. distil labs trains a specialized model layer on your workload and serves it behind one OpenAI-compatible endpoint.
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.
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
The scientific case for being nice to your chatbot
New research confirms that LLMs often perform better when you encourage them. But why?
LLMs are bullshitters. But that doesn't mean they're not useful.
Note: This is a personal essay by Matt Ranger, Kagi's head of ML In 1986, Harry Frankfurt wrote On Bullshit. He differentiates a lying from bullshitting: Lying means you have a concept of what is true,...
LLMs are a tool for thought
The oxygen from the air diffuses into your bloodstreams, is used for cellular respiration, and is transformed into carbon dioxide.
There's Something Fundamentally Wrong With LLMs
LLMs aren't trained on the "vast majority of speech," experts warn, a major blind spot that could have sweeping consequences.
ethicalabs-ai/BlossomTuneLLM-MLX
Federated Fine-Tuning of LLMs on Apple Silicon with Flower.ai and MLX-LM
LLM is a learned distribution p on sequences of tokens. If you just sample a bunch of text from p willy nilly and put it in the training data and train you just get back p. But if you sample a bunch of text from p and throw out whatever's bad and train on what's left then you learn p(x|x is not bad)
