PetroIvaniuk/llms-tools: a curated reading list for LLM work, not a toolkit

What PetroIvaniuk/llms-tools actually is

The README opens by describing the repository as a list of ready-to-use AI tools, open sources and research projects, and notes that it also covers AI research outside LLMs, such as computer vision. That sentence sets the expectation correctly: this is an index, not a codebase. The top-level entries in the repository are LICENSE and README.md. There is no source directory, no package manifest, no build file and no test suite to inspect.

The audience follows from that shape. Someone starting in LLM engineering, or someone who needs a shortlist of courses and reference reading for a team, can use the page as a starting map. Someone looking for a library that tokenises text, calls a model or evaluates outputs will not find it here. The name is the most misleading part of the project: llms-tools sounds like a toolbox, and the contents are closer to a syllabus with links.

How the list is organised, and where it stops being neutral

The README is grouped into thematic blocks. It opens with prize context: the 2024 Nobel Prize in Physics, the 2024 Nobel Prize in Chemistry for computational protein design and protein structure prediction, and a link to Jürgen Schmidhuber's post arguing that the physics award misattributes credit. It then notes the 2025 Turing Award to Andrew Barto and Richard Sutton as pioneers of reinforcement learning.

From there the structure becomes a conventional link index. Under LLMs and chatbots there are sections for courses, video, reading, visualisation, competitions and models. The courses section is a row of badge links to DeepLearning.AI, LangChain Academy and Anthropic Academy. Video links point to Andrej Karpathy's talks, Andrew Ng, Sequoia Capital's AI Ascent recordings and a 3Blue1Brown neural network playlist. Reading links cover the Stanford AI Index, the State of AI Report, the Prompt Engineering Guide, OpenAI's cookbook and model spec, and Anthropic's prompt engineering tutorial. Competitions are Kaggle and Google challenges with deadlines printed alongside them.

The models section is the one piece of original structure: a table split into 2021-2023, 2024 and 2025-2026 columns, with rows for Google and DeepMind and cells linking to LaMDA, GLaM, PaLM, Chinchilla, Bard, PaLM-2, Gemini, Gemini 1.5, Gemma, Gemma 2 and Gemini 2.0. Treating that table as a timeline is reasonable. Treating it as a maintained inventory is not, because the last column is labelled 2025-2026 and the entries in it are the ones most likely to fall behind.

Using it: there is no install step

There is no installation procedure, because there is nothing to install. The repository contains LICENSE and README.md, and the README gives no setup commands, no package name and no entry point. Any tutorial that showed you a pip install line for this project would be inventing it.

The only practical way to consume it is to read it, either on the repository page or by cloning it for local reference. Cloning gives you the README on disk, which is useful if you want to annotate it or diff it against your own notes later.

git clone https://github.com/PetroIvaniuk/llms-tools.git

After the clone, the working directory contains LICENSE and README.md. That is the whole payload. If you were expecting a virtual environment, a requirements file or a command to run, the absence is the answer.

The README does invite contributions, and it is the only file that carries content, so a contribution means editing that file. Adding an entry means matching the existing pattern of a markdown link with a short description, placed in the right thematic block. There is no schema, no link checker and no CI configuration visible in the repository, so nothing enforces that a new link resolves or that a deadline in the competitions section is still in the future.

The competitions block is the clearest failure mode

The competitions section lists Kaggle and Google challenges with dates attached: a red-teaming challenge with an August 2025 deadline, an AI Mathematical Olympiad progress prize with a March 2025 deadline, Gemma language tuning with a January 2024 deadline, Gemini long context with a December 2024 deadline, and a Gemini API developer competition with a September 2024 deadline. Several of those dates have passed, and the README does not mark them as closed or archive them.

This is not a criticism of the author's intent, it is a property of link lists. A dated entry ages the moment the date passes, and nothing in the repository detects that. The same applies to the model table: a cell that says Gemini 2.0 Flash in a column headed 2025-2026 will read as current for a while and then quietly stop being current.

The practical consequence is that you should treat every date and every model name in this repository as a claim made at the time of the last push, which was on 2026-09-05, and verify it against the linked source before you act on it. If your use case depends on knowing which competitions are open right now, this page is the wrong tool; go to the competition platform.

Compared with awesome-llm style lists

The obvious comparison is the family of awesome-* repositories, which collect links on a topic and usually sort them by category with short annotations. PetroIvaniuk/llms-tools follows the same model, with two differences worth naming.

First, the scope is wider than the title. The README explicitly says it includes AI research from other areas such as computer vision, and the prize and Turing Award sections at the top are about machine learning history rather than tools. An awesome-llm list usually stays inside the LLM boundary. Here you get a small amount of field context before the links start.

Second, the model table is the closest thing to an editorial position. An awesome list typically presents links flatly; this one arranges models by lab and by period, which makes the progression from LaMDA and PaLM through Gemini and Gemma legible at a glance. That is a real difference in approach, and it is also the part that decays fastest. If you want a flat, category-sorted link dump that is easy to grep, a conventional awesome list is a better fit. If you want a short historical frame around the links, this layout does something the flat lists do not.

Maintenance cost and the Apache-2.0 licence

The repository is not archived, and the last push was on 2026-09-05. There are no releases, which is consistent with a README-only project: there is no artifact to tag.

Upgrade cost is close to zero in the software sense, because there is no dependency to bump. The cost sits in link rot and stale dates instead. Every entry is an outbound URL, and outbound URLs move, get paywalled or disappear. Nothing in the repository checks them. If you fork it for internal use, you are taking on that verification work yourself, and the only file you would need to edit is README.md.

Licensing is Apache-2.0, which is a permissive licence that generally allows reuse and modification with attribution and notice requirements. That applies to the repository's own content, which is the README and its arrangement. It does not extend to the third-party material the links point at: the videos, papers, courses and competition pages belong to their own owners and carry their own terms. Reading the licence file in the repository is the right first step if you plan to redistribute the list, and this is a description of what the repository states, not legal advice.

Editorial conclusion

Adopt PetroIvaniuk/llms-tools as a reading list if you are orienting yourself in LLM work and want a single page of courses, papers and model history to work through. Do not adopt it if you need a library, a CLI or anything importable: the repository holds only LICENSE and README.md, so there is nothing to install and nothing to version. Before relying on any entry, open the linked source yourself, because the README is a pointer list and does not restate the content it links to.

Frequently asked questions

Official sources

1. Issues
2. License: Apache-2.0
3. PetroIvaniuk/llms-tools on GitHub
4. README