amitness/learning

A log of things I'm learning

What this is

learning is a personal study log — a curated, running list of books, courses, videos, and articles that the author (amitness) is working through to build strong software engineering skills. It's not a code library or app; it's a public curriculum tracker.

How it's organized

The README splits everything into two big buckets:

  1. Generic Skills — "domain-agnostic, stack-independent" abilities that apply to any software role. These are grouped into 12 categories:

    • System Design (e.g., Designing Data-Intensive Applications, A/B testing courses, REST API design)
    • Maths (statistics, probability, linear algebra — mostly Python-based courses)
    • Data Structures & Algorithms (e.g., Grokking Algorithms, Neetcode courses)
    • Data Modeling (SQL, NoSQL, database design)
    • Design Sense (UI/UX fundamentals, Figma, visual communication)
    • Linux & Command Line (shell basics, bash scripting, environment variables)
    • Version Control (Git, GitHub collaboration)
    • Testing & Profiling (unit testing, mocking, memory profiling in Python)
    • Technical Writing (Google's technical writing courses, writing for developers)
    • Career (resume writing, coding career handbook)
    • Programming (Python idioms, OOP, packages, dates/times)
    • Development Environment (Chrome DevTools, AI coding agents, VSCode)

  2. Specialized Skills — currently just one area: Generative AI, which itself splits into:

    • Natural Language Processing (Stanford/CMU courses, chatbot building, sentiment analysis, machine translation)
    • LLM Theory (building LLMs from scratch, tokenizers, positional embeddings, RLHF, transformer internals)

How progress is tracked

Each resource row has a Format (Book, Udacity, Datacamp, YouTube, etc.), the resource name with a link, a Length (page count, hours, or lesson count), and a Progress column using these symbols:

  • ✅ — completed
  • ⬜ — not started
  • ⌛️ — in progress (partially done)
  • ⏳ — in progress (started, e.g., "1/7" lessons done)

Some rows show explicit fractions like "310/489 pgs" or "36/36" to indicate exact progress.

Current status

  • Updated: once a month
  • Current focus: Generative AI

Most resources in the Generative AI section are marked complete, while many in Generic Skills (especially Data Modeling and some System Design items) are still pending.

What it reveals about the author's approach

The README links out to a blog post about "mastering adjacent disciplines" and a James Clear article on continuous improvement, suggesting the author deliberately studies topics around their core work, not just directly on it. The mix of free university lectures (MIT, Stanford, CMU), paid platforms (Datacamp, Udacity, Neetcode), and books shows a multi-source, self-directed learning strategy.

In short: this is a transparent, well-organized record of one engineer's ongoing education — useful as a reference for anyone looking for high-quality learning resources in the same areas.

Related

  • Project
    yihong0618/xiaogpt

    A tool that connects Xiaomi AI Speakers to LLMs like ChatGPT and Gemini, allowing the speaker to answer questions using advanced AI via custom TTS engines.

  • Project
    nndl/llm-beginner

    A comprehensive, hands-on tutorial series for beginners to learn LLMs and AI agents through six progressive implementation tasks, from building a mini-GPT to creating a coding agent.

  • Project
    dataease/SQLBot

    An intelligent data querying system based on LLMs and RAG that enables conversational data analysis (ChatBI) by converting natural language into SQL and visual charts.

  • Project
    zai-org/GLM-4

    A series of open-source LLMs featuring 9B and 32B parameter models with specialized variants for general chat, deep reasoning, and complex rumination tasks.