Papers, blog posts, and talks I send every engineer I mentor on getting up to speed with AI.
Papers, blog posts, and talks every AI-focused engineer should read.
Start with Attention Is All You Need if you want the theory. Skip it if you don't. Either way, read Kaplan et al. on scaling laws.
The LLM Prompt Engineering guide from OpenAI. Anthropic's prompt best practices. Papers on RAG, fine-tuning, and in-context learning.
Read about jailbreaks not to use them, but to understand the threat model. Read about hallucinations. Read about safety.
Then build something. Reading without building is theater.
A walkthrough of the state machine, audio pipeline, and fallback design I use for Chasyr.
When RAG actually beats fine-tuning, when it doesn't, and how to tell which one you need.
The exact prompts and CI workflows my team runs on every PR. Copy-paste, MIT licensed.
The boilerplate I clone for every new SaaS bet. Auth, billing, RLS, AI hooks pre-wired.
How I restructured a 7-person team around AI tooling. Velocity numbers, cultural pitfalls, what worked.
A guest lecture at COMSATS on how mid-career engineers can move into architecture roles.
Panel at AusFinTech 2026 - the legal, technical, and ethical scaffolding for AI that talks to customers.
A handful of titles - short list, opinionated commentary, no affiliate nonsense.
My exact dev stack - IDE, terminal, AI agents, productivity hacks. Updated quarterly.
Remote-friendly companies, visa sponsors, OSS scholarships - things I wish I had a decade ago.