llms-tools is two files: a link list with awards at the top and expired deadlines below

The repository is one README file next to a licence

The top-level listing contains exactly two entries, LICENSE and README.md. There is no source directory, no build configuration, no continuous integration file, no issue templates and no documentation folder. The recorded primary language is unknown, which is what a repository with no code looks like to a language detector. There are no GitHub releases at all, and no homepage field is set, so there is nothing outside the README to check a claim against. The licence is Apache-2.0 and the default branch is main, last pushed on 2026-09-05. What the file contains is a set of headings and outbound links: it opens by saying the repository holds a list of ready-to-use AI tools, open sources and research projects, notes that the list reaches beyond language models into areas such as computer vision, and invites contributions in a single line. Everything else is curation, which means the only failure mode available to it is a link that stops resolving.

Three course links are written with no text beside them

The Courses section under the language model heading has three entries, and all three are written as empty labels: a link with no title pointing at learn.deeplearning.ai, another with no title pointing at the LangChain academy collections page, and a third with no title pointing at an Anthropic skilljar host. The same pattern repeats further down, where the video tutorial section carries a link with no text pointing at a YouTube identifier, which is how a thumbnail is usually embedded. In a Markdown renderer these appear as three blank spaces, or as bare clickable areas with no indication of what they offer. This is not a style variation: every other heading in the file spells out what the link is, from the Prompt Engineering Guide to the BentoML LLM Inference Handbook. A reader arriving at those three anchors has no way to tell which one is a video course, which is a collection of notebooks and which is an interactive tutorial, without following each and reading the destination.

Two Nobel Prizes and a Turing Award come before any tool

The first two sections of the file are not about software at all. They record the Nobel Prize in Physics 2024 to John J. Hopfield and Geoffrey E. Hinton, quoted from the press release as being for foundational discoveries and inventions that enable machine learning with artificial neural networks. The next records the Nobel Prize in Chemistry 2024, one half to David Baker for computational protein design and the other half jointly to Demis Hassabis and John M. Jumper for protein structure prediction. Immediately after that sits a link to a post by Jurgen Schmidhuber arguing that the physics prize rewards plagiarism and incorrect attribution in computer science, so a dispute about credit is preserved inside a tools list rather than left out. Then the Turing Award in 2025 to Andrew Barto and Richard Sutton as pioneers of reinforcement learning. Four honours and one controversy, placed above the first course link, which tells you the file's centre of gravity is the history of the field rather than its current tooling.

All five competitions listed have deadlines that have already passed

The Competition section carries five entries with dates attached, and every date is in the past. The Gemini API Developer Competition offers cash prizes of one million dollars with a deadline of September 2024. Building Gemma model variants for a specific language or a unique cultural aspect closed in January 2024. Demonstrating use cases for Gemini's long context window closed in December 2024. Solving national-level math challenges with models in the AI Mathematical Olympiad Progress Prize 2 closed in March 2025. Finding previously undetected vulnerabilities and harmful behaviours in a red-teaming challenge against an open weight model, ranging from deceptive alignment to reward-hacking exploits, closed in August 2025. Against a last push of 2026-09-05, the newest of these closed more than a year ago. The section reads as a record of contests that have run rather than as a place to find something to enter, and it carries no successor entry.

The model table leaves its first column header blank

Models is the one place the file uses a grid instead of links, and the grid is hand built. The header row reads as an empty first cell followed by three year buckets, 2021-2023, 2024 and 2025-2026, with an alignment row underneath. The rows are vendor cells holding one or more model links separated by line breaks. Google and DeepMind share a row, so the vendor column has no heading of its own and the reader has to infer that the leading cell is the maker rather than a category. The 2021-2023 bucket carries LaMDA, GLaM, PaLM, Chinchilla, Bard and PaLM-2. The 2024 bucket carries Gemini 1.5, Gemini 1.5 Flash, Gemma, Gemma 2 and Gemini 2.0. The 2025-2026 bucket carries Gemini 2.0 Flash Thinking, Gemma 3, Gemini 2.5, Gemini 2.5 Flash and Gemma 3n, and then the visible text of that cell stops partway through an entry beginning Gemma 3.

A dated Model Spec snapshot sits next to the live one

The Reading section is the most carefully dated part of the file, and it holds two links that carry their own staleness warning. OpenAI's entries pair the current model specification with a second link labelled as the old one, pointing at a static file whose path ends in model-spec-2024-05-08.html. Keeping both is the right call, but only if a reader knows which is which, and the label carries a date rather than a version number, so nothing says whether the live specification has moved since. The same section pairs Nathan Benaich and Alex Chalmers's State of AI Report for 2024 and 2025, and the 2025 link ends in an edit path rather than a view path, which is what a link to an editable document looks like. Stanford's AI Index is annotated as running since 2017, Menlo Ventures appears with its 2024 enterprise report only, and the AI 2027 forecasting site is listed under a publication that has not happened yet.

The description promises tools and open source, the headings list courses and talks

Read the opening three lines against the actual headings and a gap appears. The file says it contains a list of ready-to-use AI tools, open sources and research projects, and that it reaches into other areas such as computer vision. What the language model section actually contains is Courses, Video, Reading, Visualization, Competition and Models. Every one of those is instructional material, a chart, a contest or a reference list. There is no heading for tools you can run, no heading for open source projects you can clone, and no heading for research papers outside the Nobel and Turing entries near the top. So the two claims a reader arrives with, ready to use and open source, are the two the structure does not deliver, and the categories that are delivered are the ones a newcomer has least trouble finding elsewhere.

Welcome to contribute, with no file that says how

The opening ends with a two-word invitation, Welcome to contribute, and nothing else about contribution. There is no CONTRIBUTING file in the two-entry top-level listing, no pull request template, no code of conduct and no issue template directory. For a list like this the missing process is not fatal, because the natural contribution is a single link. It does mean that whether entries are added in alphabetical order, whether each link carries a one-line reason, and whether a section that has gone quiet is retired or kept, are all conventions that exist only in the maintainer's head. The formatting shows the same looseness. Lines are joined by hard breaks made from trailing backslashes, and some lines in a block carry one while the last line of the same block does not. The opening sentence about other research areas ends with etc. immediately followed by its backslash, with no space between.

Editorial conclusion

llms-tools is worth cloning as a starting index if you want the Stanford AI Index, the State of AI reports, the OpenAI cookbook and the DeepMind model line gathered in one place, and it costs nothing but the time to check each link. It will not serve as a tool catalogue, a comparison, or a tracked dependency list, because it has no code, no versions, no releases and no homepage, and its competition section is only a record of contests that have closed. Before you rely on any of it, open the two versioned links that carry real staleness risk, the archived OpenAI Model Spec snapshot dated 2024-05-08 and the deadline list, and confirm the three course links render for you at all, since they carry no visible text.

Frequently asked questions

Official sources

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