MakeMoneyWithAI: a curated list that maps open-source AI projects to revenue, with a README that admits it is a list
A list of open-source AI projects you can use to generate income easily.
At a glance
- What is it?
- garylab/MakeMoneyWithAI is a Python-language curated list of open-source AI projects, each annotated with a sentence describing how it might be turned into income. The README is the entire product, and reading it as a research tool rather than a business plan is the only honest way to use it.
- Who is it for?
- Adopt MakeMoneyWithAI as a reading list if you already know which layer of the AI stack you work in and want a single page that names projects like ollama, n8n, ComfyUI, and ragflow alongside a one-line revenue angle for each.
- Can I use it commercially?
- Not without permission. GitHub finds no licence file in the repository, and without a licence all rights are reserved by default: you may read the code but not reuse it. Check the README, or ask the authors, before using it.
- Is it still maintained?
- Yes. The repository last received commits 3 days ago.
- What is it written in?
- Mainly Python, according to GitHub's language statistics.
Answers come from the project's GitHub data, last synced on September 15, 2026, and from our analysis. They are not legal advice.
DEEP OPEN-SOURCE ANALYSIS
What the list actually ships, and who it is written for
The repository is a README. There is no package to install, no CLI, no configuration file, and no release has been retrieved. The primary language is listed as Python, but nothing in the supplied material shows Python code being executed; the deliverable is a numbered markdown list of links. Each entry follows the same shape: a project name, a link to its GitHub repository, a star figure, and a single sentence describing a monetization angle. Entry 6, for example, points at ollama and describes deploying private LLMs locally or in enterprise for low-cost inference, API integration, scaling, billing, analytics, and fine-tuning. Entry 14 points at ComfyUI and describes turning a modular diffusion GUI into revenue via custom pipelines and SaaS imaging tools.
The audience is implied rather than stated. Someone who wants a starting point for picking an open-source AI project to build a product on, and who would rather scan twenty-four annotated links than browse GitHub topics. That reader gets a shortlist and a vocabulary of revenue models (SaaS, licensing, consulting, subscriptions, white-labeling, API sales) attached to each project. What that reader does not get is any evidence that the attached model works, any cost estimate, any discussion of the license terms of the listed projects, or any ranking beyond what appears to be star count. The list is a map of the territory, drawn at low resolution.
The annotation style is the real content, and it is inconsistent
Read the entries closely and the descriptions fall into two groups. The first group names a mechanism. Entry 13, graphify, says it turns a codebase with docs, SQL schemas, configs, and PDFs into a queryable knowledge graph via a /graphify skill for Claude Code, Cursor, Codex, and Gemini CLI, with local deterministic AST parsing, every edge explained, no vector store. Entry 21, browser-use, names browser automation, web scraping, and AI agents. Entry 12, open-webui, names an interface for OpenAI, Ollama, and local models. These entries tell you what the software does before they tell you what it might earn.
The second group names only a revenue category. Entry 5, JavaGuide, is described as a Java and backend interview guide enabling monetization via corporate training, paid courses, developer upskilling, recruitment services, system design consulting, and AI product acceleration strategies. Entry 20, Front-End-Checklist, is described as enabling faster, higher-quality web builds that reduce costs and increase client revenue through repeatable delivery models. Entry 3, AutoGPT, is described with autonomous agents, task automation, and plugin integrations for commercially scalable SaaS and marketplace products. Those sentences could be attached to almost any popular repository without changing meaning. The list would be more useful if every entry read like the graphify entry. The inconsistency is not a fatal flaw, but it means the reader has to triage: trust the entries that describe a mechanism, discount the ones that describe a market.
The star figures do not match the repositories they point to
This is the most concrete problem in the supplied material, and it is worth stating plainly. The README lists LibreCode at 236.6k stars, superpowers at 233.5k, AutoGPT at 178.8k, stable-diffusion-webui at 157.0k, and JavaGuide at 155.8k. AutoGPT and stable-diffusion-webui are well-known repositories, and the numbers shown for them are far above what those projects carry. The pattern suggests the figures were either generated, mis-transcribed, or drawn from a different source than the links. Whatever the cause, a reader who uses star count to decide which entry to open first is being misled by the list itself.
The correct response is not to discard the list but to stop reading the numbers. The links are the durable part. If you want to know how active a project is, open the repository and look at the commit history and the issue tracker directly. The list gives you the name and the URL; the star figure is decoration that happens to be wrong. This is also a signal about how the list was assembled: if the numbers were not checked, the one-line revenue descriptions probably were not checked either. Treat every sentence in the README as a pointer to verify, not a finding to cite.
How you would actually use it: no install, just a clone or a browser
There is nothing to run. The practical workflow is to read the README on GitHub, or clone the repository if you want a local copy:
git clone https://github.com/garylab/MakeMoneyWithAI
That is the full setup. No dependency file is shown in the supplied material, no entry point, no configuration keys. If you want to turn the list into something queryable, you would have to write that yourself; the repository does not offer a parser, a JSON export, or a schema. The README is prose and links.
Because the list is the product, the maintenance question is about the README rather than about code. The last push recorded is 2026-09-09, so the repository is not archived and has been touched recently, but no releases have been retrieved, which is normal for a list. The upgrade cost of a list is the cost of re-reading it: entries go stale when a linked project changes license, stops receiving commits, or pivots. Nothing in the repository appears to track those changes automatically. If you depend on this list for anything recurring, you are depending on a human editing a markdown file, and there is no changelog in the supplied material to tell you what changed between reads.
Where the list breaks down: the license field is unknown and the entries carry none
The repository's own license is listed as unknown in the supplied material. For a list of links, that is a smaller problem than it would be for code, since the list itself contains no executable artifact. But the entries it recommends are a different matter. The README does not state the license of any linked project. Several of the named projects are known in the wider ecosystem to carry licenses with commercial restrictions or dual-licensing terms, and a reader who picks an entry from this list and builds a product on it without checking the upstream license is taking a risk the list does nothing to surface.
This is the case where the list is the wrong tool. If you need to know whether you can ship a commercial product on top of a given project, the list cannot answer that, and its framing (every entry is presented as a monetization opportunity) actively implies the answer is yes. The list is useful for discovery and useless for clearance. That gap is not unique to this repository, but it is wider here because the entire value proposition is commercial use. A reader who treats the list as a menu of safe commercial building blocks is reading it against its own evidence.
A real alternative: awesome lists that describe projects instead of pricing them
The obvious comparison is the general awesome-list format, for example awesome-selfhosted or Awesome Python. The difference in approach is the annotation. An awesome list typically describes what a project is, what it does, and sometimes its license, and leaves the business decision to the reader. MakeMoneyWithAI inverts that: it leads with the revenue angle and treats the technical description as supporting detail. Entry 4 on stable-diffusion-webui lists custom models, batch processing, plugins, and exportable commercial assets, then appends services, prints, licensing, merchandise, marketplaces, subscriptions, ads, and API integrations. The technical nouns and the revenue nouns are mixed in one sentence with equal weight.
The trade-off is real in both directions. A conventional awesome list is slower to read if your question is specifically "what can I sell," because you have to infer the commercial angle yourself. MakeMoneyWithAI answers that question immediately but with no sourcing, which means the answer is a hypothesis, not a finding. A reader who wants a shortlist fast gets value from the format here. A reader who wants to compare two projects on capability will find the revenue language gets in the way, because the sentences are optimized for breadth of monetization vocabulary rather than for distinguishing one project from another.
Who should use this, who should not, and what to check first
Use it if you are scouting. You know roughly what layer you care about (local inference, workflow automation, image generation, retrieval) and you want a single page that names projects in that layer alongside a prompt about how people might charge for them. The list is fast to scan and the links are the part that survives scrutiny. Entries with concrete mechanisms, such as graphify's local AST parsing or browser-use's browser automation, are the ones worth opening first, because their descriptions tell you enough to decide whether the project is in your problem space before you click.
Do not use it as a business plan, a market survey, or a source you would put in front of a client or an investor. The star figures are unreliable, the revenue sentences are unsourced, and the licenses of the listed projects are not mentioned at all. Do not use it to decide whether a project is commercially usable; it cannot tell you that and its framing implies otherwise. And do not use it as a substitute for reading the upstream README of whichever project you pick, because the one-line description here is a paraphrase, not documentation.
What to verify first, in order: open the linked repository and read its license file; check the date of the most recent commit and whether issues are being answered; and confirm that the capability named in the list entry appears in the project's own documentation. If all three hold, the entry is worth your time. If any of them fails, the list has given you a name and nothing else, which is still more than you had before you opened it.
Editorial conclusion
Adopt MakeMoneyWithAI as a reading list if you already know which layer of the AI stack you work in and want a single page that names projects like ollama, n8n, ComfyUI, and ragflow alongside a one-line revenue angle for each. Do not adopt it as a business plan, a due-diligence source, or anything you would cite to a client: the annotations are unsourced, the star figures in the README do not match the repositories they point to, and the license field for the list itself is unknown. Before you act on any entry, open the linked repository and confirm three things yourself: the actual license file, whether the project is still receiving commits, and whether the monetization sentence in the list corresponds to a capability the project documents. Start with the entries whose descriptions name a concrete mechanism (graphify's local AST parsing, browser-use's browser automation) rather than the ones that only name a revenue category.
Community notes