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Advice

Getting Started with Language Models in 2025

After a year of building AI applications and contributing to projects like Instructor, I've found that getting started with language models is simpler than most people think. You don't need a deep learning background or months of preparation - just a practical approach to learning and building.

Here are three effective ways to get started (and you can pursue all of them at once):

  1. Daily Usage: Put Claude, ChatGPT, or other LLMs to work in your daily tasks. Use them for debugging, code reviews, planning - anything. This gives you immediate value while building intuition for what these models can and can't do well.

  2. Focusing on Implementation: Start with Instructor and basic APIs. Build something simple that solves a real problem, even if it's just a classifier or text analyzer. The goal is getting hands-on experience with development patterns that actually work in production.

  3. Understand the Tech: Write basic evaluations for your specific use cases. Generate synthetic data to test edge cases. Read papers that explain the behaviors you're seeing in practice. This deeper understanding makes you better at both using and building with these tools.

You should and will be able to do all of these at once. Remember that the goal isn't expertise but to discover which aspect of the space you're most interested in.

There's a tremendous amount of possible directions to work on - dataset curation, model architecture, hardware optimisation, etc and other exiciting directions such as Post Transformer Architectures and Multimodal Models that are happening all at the same time.

What Makes Good Documentation

Over the past year, we've grown instructor's documentation to over 60,000 lines of content. This means for every line of code in our library, we've written 5 lines of documentation. Through this process, I've realized that great documentation isn't just about explaining features - it's about demonstrating value.

Everything I've learnt about writing good Python code

In the past 6 months, I've 10xed the amount of python code I've written. In this article, I'll show you a few easy actionable tips to write better and more maintainable code. I've been lucky enough to have Jason (@jxnlco on twitter) review a good chunk of my code and I've found that these few things have made a massive difference in my code quality.

  1. using the @classmethod decorator
  2. learn the stdlib
  3. write simpler functions
  4. being a bit lazier - earn the abstraction
  5. decouple your implementation
  • in Advice
  • 6 min read

Learning with Adult Responsibilities

Introduction

Over the past 6 months, I've been trying to learn more about AI and LLMs. ChatGPT had me hooked when I tried it for the first time. Over the course of this period, I've been chatting to more people, shitposting on twitter and working to learn as much as I can in my spare time.

That amounts to roughly 10-20 hours a week since I don't have much of a social life which has been about 4-500 hours in total since the time I started exploring this space so take my experience with a grain of salt. I'm relatively new and you're probably 2-3 months behind me at most, much less if you do it full time.

I've had some people reach out to me for advice on what to do and I figured I'd write a longer blog post so that I could refer to it myself and consolidate some of my ramblings.