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Discover▾

Not sure where to start? This week’s hottest software, models and stacks in three minutes.

AI Apps▾

AI software people use: from assistants like ChatGPT to tools for coding, video and workflows, picked by purpose.

For Agents▾

Software for AI: tools that let your agent (Claude Code, Codex, Cursor…) browse, read docs, run code and remember.

Models▾

Choose models, estimate cost, self-host: prices, rankings, benchmarks, free tiers and compute.

Solutions▾

A goal or an error message? Ready-made stacks, steps and fixes by scenario.

Learn▾

A systematic course from zero to the frontier, with an algorithm library, must-read papers and tutorials.

Frontier▾

Daily AI news, weekly highlights, the latest papers and how AI is changing every discipline.

// Stage 9 · Safety, Evaluation & Frontier

Tracking the Frontier: Reading & Reproducing Papers

The three-pass reading method, tracking arXiv and conferences, reproduction and writing.

What you will learn ↓ Code & exercises ↓

about 6 hours3 algorithms0 paperslesson text in progress

01Goals

Do these first: Transformers & Attention

02Algorithms to master

Each card has the original paper, prerequisites and resources.

Transformer & Self-Attention An attention-only, parallelizable sequence model — the backbone of all LLMs. 2017core Group Relative Policy Optimization (GRPO) Samples a group of answers per prompt and uses in-group relative scores instead of a critic. 2024core Flow Matching Learns a velocity field that flows noise into data. 2022core

03Courses & videos

04Hands-on: project, code & exercises

Free, official or author-published pages. Most run in the browser or on a free Colab GPU.

PROJECT

Pick a paper from AIMap’s “Latest papers”, write a 500-word explainer and reproduce one small experiment from it.

  1. 1
    Hugging Face Daily Papers ↗Tools

    Spend 15 minutes a day on trending papers and write a three-sentence summary of one.

  2. 2
    arxiv.org ↗Tools

    Follow the arXiv listing and practise triaging by title and abstract.

  3. 3
    github.com ↗Reference code

    Pick a Transformer-improvement paper and do a small reproduction on nanoGPT.

05Self-test

Answer in your head, then open to check.

What do you read on the first pass?

Title, abstract, figures and conclusions.

What do you check first when a reproduction fails?

Whether the data and evaluation settings match.

How do you judge a paper’s credibility?

Open code, thorough experiments, and peer review or independent reproduction.

On these routes: Algorithm Researchers · Domain Scientists

Data updated: AI algorithms & curriculum(UTC+8)