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)
Fine-Tuning LLMs is a Huge Waste of Time
People think they can use Fine-Tune for Knowledge Injection. People are Wrong
The scientific case for being nice to your chatbot
New research confirms that LLMs often perform better when you encourage them. But why?
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 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.
"Useful" is not sufficient
So Linus Torvalds, head of the Linux kernel development, put his foot down on the Linux Kernel development mailing list when someone was bringing up criticism of LLMs: “Linux is not one of those anti-AI projects, and if somebody has issueswith that, they can do the open-source thing and fork it. Or just walk away. […]
open-slopware
Free/Open Source Software choosing to use and/or support LLM usage/AI, as well as alternatives and tips to requesting better policies or forking.
A rambling post on ollama / llama.cpp and when to use each. Pros and cons and everything in between.
I'm not a professional LLMer by any means, but I figured I'd lay out my little journey and the findings along the way. When I first saw you could run…
I don't pay for ChatGPT, Perplexity, Gemini, or Claude – I stick to my self-hosted LLMs instead
There's no point in relying on AI tools when my local LLMs can handle everything
Do I have to be polite to my LLM?
It won’t change results much, but it might be good for you
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.
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.
Vaibhav (VB) Srivastav on Twitter / X
I do agree to some extent - I do use variety of proprietary models for day to day use both as a soundboard and for workHowever, majority of the issues with local LLMs today is in the scaffolding/ runner - in most cases if you’re getting sub-par outputs - it’s the chat template,…— Vaibhav (VB) Srivastav (@reach_vb) November 25, 2025
