It is time I embarked on a journey that I’ve been putting off for too long. The spectre of being replaced by artificial intelligence has haunted software engineers for years but what was once speculation about the future has now become a very realistic scenario. The debate as to whether all coders will be completely replaced or if AI tools will make us (n)x more productive in the next few years is live but whatever the outcome we, as developers need to be prepared. As a contract software engineer I have been meaning to learn more about AI for a long time but my paid contract work has always been my priority and AI learning has been sidelined but it’s time to confront my AI daemons and at last take my first steps into my AI learning journey. I want to share my learning journey because I believe connecting with other learners is a really important part of the process and hopefully other people will find what I share useful.

The first step is to define my aims:
on a high level to make is less likely that I will lose my job to AI or to other developers who know more about AI than me – this may or may not be possible!
to learn more about AI coding assistants,
to speed up and improve the development of my own websites,
and apps,
to improve my core skills in maths, statistics, data science,
learn the basics of how LLMS, machine learning, deep learning, generative AI, neural networks,
to set up communities of other AI learners,
to consider and write about AI ethics, philosophy and impacts on society,
to have fun on the journey

The first challenge is to face to overwhelming quantity of information. Where to start? How to learn? Everyone has different learning preferences but I like to dive into several learning methods at once:
online tutorials,
youtube videos,
audiobooks,
physical books,
projects,
podcasts,
connecting with a network of other learners

So the first step was to use AI assistants – Perplexity, ChatGPT, Gemini and PI – to recommend lists of places to start. I filtered through the results and I have listed some below. I haven’t checked out all of the results so I can’t vouch for them yet. In a later post I will filter down the results into what I’m starting on. I hope you find the below lists useful:

AI Assistants to Try

Gemini – Google
GPT-4 – Open AI
Perplixity
Grok
Copilot – Microsoft
Claude – Anthropic
Pi – Inflection
Siri – Apple

Online Courses

Brilliant.org
Front End Masters course on AI Agents
Pluralsight learning AI path
Fast.ai course
Google’s Machine Learning Crash Course
Kaggle Learn
AI For Everyone” by Andrew Ng (DeepLearning.AI on Coursera)
CS50’s Introduction to AI with Python from Harvard
IBM AI Engineering Professional Certificate
LangChain – Develop LLM Powered Applications
NVIDIA Deep Learning Institute
Microsoft AI & ML Engineering
DeepLearning.AI Specialization (Coursera)
Udacity – Intro to Machine Learning with PyTorch or TensorFlow
MIT OpenCourseWare – Introduction to Deep Learning
Introduction to Artificial Intelligence (AI) (IBM on Coursera)
Elements of AI (University of Helsinki & MinnaLearn)
Machine Learning Specialization (Stanford University / DeepLearning.AI on Coursera)
Udacity Artificial Intelligence Nanodegree

Books

Ai-Assisted Programming: Better Planning, Coding, Testing, and Deployment
Artificial Intelligence For Dummies
AI Engineering: Building Applications with Foundation Models
Build a Large Language Model (From Scratch)
Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems

Audio books

The Coming Wave: AI, Power and Our Future
The Singularity Is Nearer: When We Merge with AI
Co-Intelligence: Living and Working with AI
Supremacy
Nexus: A Brief History of Information Networks from the Stone Age to AI

YouTube Channels

Two Minute Papers
Yannic Kilcher
Sentdex
StatQuest with Josh Starmer
Tina Huang
Anthropic
MattVidPro AI
Skill Leap AI
Stanford Online
CodeEmporium
3Blue1Brown
Henry AI Labs
ColdFusion
AI Explained
Matt Wolfe
Wes Roth
Tech With Tim
Krish Naik
DeepLearning.AI

Podcasts

The TWIML AI Podcast
Latent Space: The AI Engineer Podcast
Practical AI
Eye on AI
The Cognitive Revolution
Me, Myself, and AI
Data Skeptic
High Agency Podcast
The Gradient Podcast
The AI Podcast by NVIDIA
Practical AI (by Changelog)
AI Alignment Podcast (by the Future of Life Institute)
Machine Learning Street Talk
Gradient Dissent (by Weights & Biases)
AI in Business
No Priors
Latent Space: The AI Engineer Podcast
AI Daily Brief
The Robot Brains Podcast

X
Andrew Ng (@AndrewYNg)
Lex Fridman (@lexfridman)
Stability AI (@StabilityAI)
OpenAI (@OpenAI)
Yann LeCun (@ylecun)
@karpathy – Andrej Karpathy
@sama – Sam Altman
@hardmaru – Hardmaru (David Ha)
@huggingface – Hugging Face
@thegradientpub – The Gradient

News sources

MIT Technology Review
TechCrunch (AI Section)
WIRED (AI Section)
AI Magazine
Google AI Blog
Berkeley Artificial Intelligence Research (BAIR) Blog
OpenAI Blog
The Rundown
The Batch (by Andrew Ng)
Mindstream
Ben’s Bites
The Neuron
Hacker Noon
Towards Data Science
The Verge – AI Coverage
The Gradient
arXiv-sanity (by Andrej Karpathy)
Papers with Code
Import AI (by Jack Clark, Anthropic co-founder)
TLDR AI (by TLDR.tech)
Reddit – r/MachineLearning, r/Artificial, r/LocalLLaMA
Analytics Vidhya
KDnuggets
IEEE Spectrum
BAIR Blog (Berkeley Artificial Intelligence Research)
Superhuman

Events and Meetup groups in London

The AI Summit London 2025
Mindstone Practical AI Meetup
Generative AI Summit
London AI Developers Group
rev™ London AI with Google Cloud
AI Everywhere London
Data & AI London
London.AI Meetup
London Applied Artificial Intelligence Meetup
Tech & AI LIVE London
Gartner Data & Analytics Summit 2025
London Machine Learning Meetup
AICamp London (Generative AI, LLMs and Agent)
PyData London
London Futurists
The King’s Festival of Artificial Intelligence
AI UK
The Alan Turing Institute

Companies to research
Microsoft
Nvidia
Alphabet (Google)
OpenAI
Anthropic
AMD (Advanced Micro Devices)
Databricks
Stability AI
Palantir Technologies
Cohere
IBM
Tesla
OpenAI
Amazon
AMD