AI, from zero to hero¶
Everything AI in one place, free. Start from nothing and learn what a model is, use AI well in your job, roll it out to a team, build real systems, pick the right tools, secure and govern them, and get hired - 98 guides in all. Then go as deep as you like: 156 research papers rewritten in plain language and 31 explainers on the models, the benchmarks, the money and the rules.
No maths degree assumed, no paywall, no signup. Press / to search all 377,000+ words.
Start from zero AI for your job Build something Read the research
- 98 guides
- 156 papers
- 31 explainers
- 1.8M words of research read
- 8x shorter
- $0 to read
Research figure measured, not estimated: every word pdftotext extracts from 120 of the 156 papers. The other 36 sit behind journal paywalls or are published as web pages with no PDF, so they are excluded - the real total is higher. See the script.
Pick your way in
Where are you starting?¶
-
Learn - from absolute zero
Paths by audience (everyone, leaders, engineers, researchers), the day-one basics - your first API call, running a model on your laptop - and the core ideas in plain English. 35 pages.
-
AI at Work - use it, then roll it out
Playbooks by role, real use cases, and the team-enablement kit: adoption plan, acceptable-use policy, training, measuring ROI. 17 pages.
-
Build - hands-on projects
Chatbots, RAG, agents with MCP, evals, fine-tuning, voice - step by step with runnable code, plus the architecture patterns behind production systems. 16 pages.
-
Tools - what to pick
Chat assistants, coding assistants, agent frameworks, vector stores, serving stacks, and which model for which job. Comparisons with dates on them. 11 pages.
-
Safety & Governance
Prompt injection, the OWASP LLM Top 10, agent security, red teaming - and the governance side: NIST AI RMF, ISO 42001, the EU AI Act, risk assessment. 13 pages.
-
Careers
The AI roles and how to get there, interview prep with real questions, and which certifications are worth it. 6 pages.
Go deeper
The research library¶
Every major paper from the Transformer to today's reasoning models, read in full and rewritten to be finished: 1.8M words of research in, 284,000 words out. Plus 31 dated explainers on everything a single paper does not cover.
-
Start with the story
Seventy-five years of AI in one page, every milestone linked to its summary.
-
Understand the landscape
Model families, benchmarks, compute and cost, policy, open questions.
-
Read in order
A staged reading order from Word2Vec to reasoning models.
The library
Papers by category¶
Full index with titles, authors, and years
Start with the classics
Twelve papers worth reading first¶
- Attention Is All You Need - the Transformer
- GPT-3 - few-shot learning at scale
- InstructGPT - how a base model becomes an assistant
- DPO - alignment without the RL loop
- DeepSeek-R1 - reasoning models, method published
- Chain-of-Thought Prompting - thinking out loud
- LoRA - cheap fine-tuning
- RAG - retrieval-augmented generation
- FlashAttention - fast exact attention
- Scaling Laws - why bigger worked
- Stable Diffusion - latent diffusion
- Model Context Protocol - connecting tools to models
New
Recently added¶
- Mamba-3: Improved Sequence Modeling using State Space Principles (2026)
- DAPO: An Open-Source LLM Reinforcement Learning System at Scale (2025)
- s1: Simple Test-Time Scaling (2025)
- Emergent Misalignment: Narrow Finetuning Can Produce Broadly Misaligned LLMs (2025)
- Reasoning Models Don't Always Say What They Think (2025)
- Circuit Tracing and On the Biology of a Large Language Model (2025)
- On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? (2021)
- Why Language Models Hallucinate (2025)
Quick links
Jump to what you need¶
- Explainers - model families, benchmarks, compute, policy, open questions
- Timeline of AI - 1950 to today, milestone by milestone
- Learning Roadmap - the staged order to read them in
- Reading Guide - what is still current, what is history
- Quick Reference - every paper in one line
- Comparisons - decision guides for real build choices
- Glossary - the vocabulary, defined once
- Coverage & Gaps - what is not here yet, and why
- Browse visually - grid view with a three-line pitch each
- By topic tag - 69 tags across the collection
- papers.json - the whole collection as machine-readable data
- Contributing - how to add a paper, and the house style
What each summary gives you¶
Every paper here was read in full, then rewritten to a fixed shape so you can move between them without relearning the format each time:
- Why this matters - the one thing that changed, stated before any notation.
- The problem - what people were stuck on, and why the obvious fix did not work.
- The core innovation - the actual idea, with diagrams instead of proofs.
- Key results - the numbers the paper reported, not the numbers people remember.
- Real-world impact - what shipped because of it.
- Limitations - what it does not do, including where the field has since moved past it.
- Related in this collection - generated cross-links, so following a thread never dead-ends.
Summaries are not a substitute for the paper. Every one links to the original, and the good ones are worth your time once the summary has told you what to look for.
Who made this¶
Built by Patrick Wiloak - ex-AWS Solutions Architect, 10 years in tech, multi-cloud certified. YouTube · LinkedIn · Source on GitHub
Learning the cloud, data, and security side too? Cloud, Data, AI and Security - Zero to Hero is the sibling site: concepts, hands-on builds, and the most comprehensive certification library on GitHub.
We build custom software and products at Nobler Works. Open-source training like this is how we give back - we are nothing without the community that supports us. If you need software built, get in touch.
Want the reps as well as the reading?
This site gives you the material. gitGood gives you the reps, and tells you whether you actually know it - including ML and AI practice paths built for exactly the material on this site.
A 7-day free trial, then $8/month or $64/year. The free tier needs no card.
Fine print¶
Summaries are original work, released under CC BY 4.0 - free to use with attribution. The papers themselves belong to their authors and are linked, never reproduced.
This is an independent educational resource. It is not affiliated with, endorsed by, or sponsored by OpenAI, Google, Anthropic, Meta, or any other organisation whose work is summarised here. All trademarks belong to their respective owners.
Found something wrong or out of date? Contributions welcome.