# MakeMoneyWithAI: A Curated List of Open Source AI Projects, Reviewed

> garylab/MakeMoneyWithAI is a README-only curated list of AI repositories, from ollama to ComfyUI, each annotated with a monetization angle. The list is real; the revenue claims attached to each entry are not substantiated anywhere in the repository.

**garylab/MakeMoneyWithAI** — A list of open-source AI projects you can use to generate income easily.

- Repository: https://github.com/garylab/MakeMoneyWithAI
- Stars: 1,067 · Forks: 160
- Language: Python
- License: not declared
- Published: 2026-09-10 · Updated: 2026-09-10 · Language: en
- Canonical page: https://hysenlabs.com/projects/garylab-makemoneywithai

## What MakeMoneyWithAI Actually Is

This is a README, a Python script, and a CSV. The top level of the repository contains README.md, fetch_projects.py, repos.csv, extra-repos.txt, excluded-repos.txt, and a .github directory. There is no application, no library, no server, and no configuration to deploy. The README describes the project as a curated list of AI tools and projects that help you turn open-source into income, and then lists entries such as ollama, transformers, n8n, langchain, dify, ComfyUI, and supabase, each with a link and a one-paragraph description of how someone might monetize it.

The audience is people who are looking for a starting point rather than a finished tool. If you already know you want to build a retrieval-augmented chatbot, this list will not teach you how. It will point you at ragflow or langchain and suggest that semantic search and knowledge workflows are sellable. That is the entire value proposition: an inventory, sorted roughly by popularity, with a business framing attached to each row.

## How the List Is Generated and Maintained

The presence of fetch_projects.py, repos.csv, extra-repos.txt, and excluded-repos.txt indicates an automated pipeline rather than hand editing. The script presumably pulls repository data, writes it to repos.csv, and the README is built from that data. The two text files act as manual overrides: extra-repos.txt for entries the scraper would miss, excluded-repos.txt for entries that should be filtered out. The repository layout supports this reading; the README does not document the exact invocation of the script or the format expected in either text file.

That is a real gap. If you want to fork the list and maintain your own version, you are reading the script to learn its interface. The last push to the repository was on 2026-09-09, which is recent enough that the list is not stale, but there is no release history and no changelog, so the only signal about activity is the commit date itself.

The star counts embedded in the README are the other structural detail worth noting. Each entry carries a figure such as 236.6k for LibreCode or 153.4k for ollama. These numbers are generated data, and some of them look implausible relative to the projects they describe. They are not evidence of quality, and the README offers no methodology for how they were collected.

## Installing and Running the List Generator

There is nothing to install in the conventional sense. The repository is a data collection, and the only executable component is fetch_projects.py. The README does not document its arguments or its output behavior, so read the source before you run it.

```bash
git clone https://github.com/garylab/MakeMoneyWithAI.git
cd MakeMoneyWithAI
python fetch_projects.py
```

After running the script, the expected artifact is an updated repos.csv, which is the file the README's entries are derived from. If the script requires network access to query repository metadata, that is implied by its purpose but not stated in the README. Read the source before executing it in any environment where outbound requests matter.

For the actual AI tools in the list, the install path is different for each one. The README gives no install instructions for any entry. To use ollama, for example, you would follow that project's own documentation, not this one. To use n8n, you would follow n8n's documentation. The list tells you a project exists; the project's own repository tells you how to run it.

## The Monetization Claims Are Annotations, Not Evidence

Every entry in the README pairs a project with a sentence about how it could generate income. AutoGPT is described as usable to build commercially scalable SaaS, automated services, marketplace products, and monetizable workflow solutions. Deep-Live-Cam is described as monetizable through real-time face-swap and one-image deepfakes for entertainment and virtual try-ons. JavaGuide, a Java interview guide, is framed as a route to corporate training, paid courses, developer upskilling, and recruitment services.

These are plausible business ideas attached to real projects. They are not tested outcomes, and the repository contains no case studies, no revenue figures, no user reports, and no methodology for how the monetization angles were chosen. The README does not claim otherwise, but the framing invites the reader to treat the annotations as validated paths. They are not. A list entry saying a tool can be monetized is a statement about what the tool does, not about whether anyone has made money doing it.

The Deep-Live-Cam entry is the clearest example of why this matters. Face-swap software carries legal and platform-policy risk in many jurisdictions, particularly around consent and likeness rights. The README mentions licensing and white-label solutions without addressing any of that.

## Where This List Falls Short

The repository has no license file at the top level. The license is listed as unknown in the repository metadata. That matters if you intend to reuse the README text or the CSV data in your own project, because there is no stated permission to do so. The individual projects linked from the list have their own licenses, and those vary widely, from permissive to copyleft to non-commercial. Nothing in MakeMoneyWithAI flags license differences between entries, which is a significant omission for anyone planning commercial use.

The descriptions are also inconsistent in depth. Some entries are a single clause; others run to a full sentence with multiple clauses. There is no indication of which projects are actively maintained, which have security advisories, or which have been abandoned. The list is sorted by star count, which is a popularity signal and nothing more.

Finally, the list mixes categories without much structure. A Java interview guide, a face-swap tool, a Postgres backend, and a diffusion GUI all appear in the same numbered sequence. If you are looking for a specific kind of tool, you are scanning the whole list.

## Alternatives to a Curated Monetization List

The closest alternative is a general awesome-list, such as the various awesome-* repositories that catalog AI tools by category without a business framing. The difference in approach is that those lists organize by technical domain (inference, agents, vector stores) and leave the commercial question to the reader. MakeMoneyWithAI does the opposite: it organizes by monetization narrative and leaves the technical categorization implicit. If you want to find the right tool for a technical problem, a domain-organized list is faster. If you want a menu of things people claim are sellable, this list is the more direct route.

A second alternative is to skip the list entirely and evaluate specific projects directly. The list itself points to ollama, n8n, dify, and langchain, all of which have their own documentation, communities, and issue trackers. Those primary sources will tell you far more about whether a tool fits your use case than a one-paragraph annotation will.

## Maintenance Cost and License Implications

Maintaining a fork of this repository means maintaining the scraper. The last push was on 2026-09-09, and there are no releases, so there is no versioned artifact to pin. If you depend on the list's contents, you are depending on a moving README and a script whose interface is undocumented. Budget time to read fetch_projects.py and understand the format of extra-repos.txt and excluded-repos.txt before you rely on either.

On licensing: the repository itself has no license file, which means the default position is that no rights are granted beyond what the hosting platform's terms allow. The projects it links to have their own licenses, and those are what govern your use of them. Some of the tools in the list, particularly in the image and face-swap categories, have licenses that restrict commercial use. The list does not tell you which. Check each project's LICENSE file before you build anything commercial on top of it. This is a factual observation about the repository contents, not legal advice.

## Conclusion

This repository is worth reading if you already know which AI stack you want to build on and need a quick inventory of well-known open source options with a business angle attached. It is not a business plan, not a tutorial, and not a source of verified earnings data. Before adopting anything from it, verify the license of the specific project you pick, since the list repository itself carries no license file, and check that project's own repository for install steps, since MakeMoneyWithAI does not provide any. Start with fetch_projects.py if you want to see how the list is generated.

## FAQ

### Can I actually make money with AI using the projects in MakeMoneyWithAI?

The repository lists projects and attaches a monetization angle to each, but it contains no revenue data, case studies, or evidence that anyone has earned money from them. The annotations describe what a tool could be used for, not what it has produced.

### Can I make $1,000 a day using AI with this list?

Nothing in the repository supports a specific income figure. The README gives descriptions of tools and suggested business uses, with no earnings claims or benchmarks of any kind.

### Which AI in MakeMoneyWithAI is best for earning money?

The list does not rank projects by earning potential. It orders entries by star count, which is a popularity measure, and the README offers no comparison of which tool produces better commercial results.

### Can I get paid to teach AI based on what MakeMoneyWithAI lists?

The JavaGuide entry in the README mentions corporate training and paid courses as monetization routes, but the repository provides no curriculum, materials, or guidance for teaching. It is a link with an annotation.

## Sources

- [garylab/MakeMoneyWithAI on GitHub](https://github.com/garylab/MakeMoneyWithAI)
- [Issues](https://github.com/garylab/MakeMoneyWithAI/issues)
- [README](https://github.com/garylab/MakeMoneyWithAI/blob/main/README.md)

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Hysen Labs editorial analysis, written from the project's own repository and release notes. Cite the canonical page: https://hysenlabs.com/projects/garylab-makemoneywithai