AI TOOLS |
A beginner’s guide to Hugging Face |
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If ChatGPT, Claude and Gemini are the AI tools you use, Hugging Face is where you go to see what else is out there. |
Yes, the name is a bit… odd. |
Hugging Face is a huge platform where developers, researchers and companies share AI models, datasets and demos. People often describe it as the GitHub of AI. |
But why would the average vibe coder care? |
Imagine you tell Claude: |
“I want my app to remove the background from uploaded photos.” |
Claude can help you build the feature. But you may still need an AI model that actually does the background removal. |
That’s where Hugging Face comes in. |
1. Find AI models |
Head to Models and you can search for AI built to do specific jobs, including: |
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Think: “I need AI that does X. Does a model already exist?” |
Click a model and you’ll usually see its Model Card — basically its information page. |
It tells you what the model does, who made it, how to use it and, importantly, what license it has. |
Pro-tip: If the page looks like gibberish, paste the link into Claude and ask: “Explain this model to me like I’m a beginner. Could I use it in my project?” |
2. Try AI before using it |
If Models feels too technical, start with Spaces. |
Spaces are AI apps and demos you can try directly in your browser. |
You’ll find tools for things like: |
Background removal Image upscaling Audio transcription Image generation Voice generation Document analysis
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You’ll also see some common suggestions at the top. |
All of this is especially useful when vibe coding. |
Before asking Claude to add a background-removal model to your app, for example, you can test a few options and see which one actually works best. |
Try this: Search Spaces for something like background removal or audio transcription and test a few results. |
3. Compare models |
You’ll also find leaderboards and benchmark results comparing different AI models. |
You do not need to become an AI benchmark expert, but this is where you want to go to see whether a model is actually useful. It’s kind of like checking reviews. |
Especially if you hear about it somewhere else, search for it on Hugging Face, check its Model Card and see how it performs. |
Then ask Claude to explain the numbers if necessary. But, at the end of the day, you may need to try it yourself. |
4. Peek at AI datasets |
Hugging Face also has a Datasets section. |
Datasets are basically collections of text, images, audio and other information used to train or test AI. |
You probably won’t spend much time here as a casual user. |
But if you’ve ever wondered: |
“What are these AI models actually learning from?”, this is where you can peek behind the curtain. |
5. Run AI on your own computer |
Some Hugging Face models can be downloaded and run locally, meaning on your own computer instead of somebody else’s servers. |
Why bother? |
It can mean more privacy, more control and potentially fewer API costs. |
The catch: some models require a powerful computer. Apps like Ollama and LM Studio are making local AI easier, but you absolutely do not need to mess with this when you’re starting. |
Do you need Hugging Face Pro? |
Probably not. |
If you’re just browsing models, trying Spaces and figuring out what Hugging Face even is, stick with the free account. |
The paid plan makes more sense once you’re actually building with Hugging Face regularly. |
Where should you start? |
Start with Spaces. |
Search for an AI task you already understand and try a few demos. |
Then search for the same thing under Models and open a Model Card. |
That’s enough. |
You don’t need to learn Python or understand the entire Hugging Face ecosystem. |
The main thing to remember is: Claude can help you build the app. Hugging Face can help you find the AI that powers part of it. |
Screenshot this |
Models = the AI doing the job Search for AI that can transcribe, translate, generate images or do something oddly specific. Spaces = try it first AI apps and demos you can use right in your browser. Model Cards = read before using See what the model does, its limitations and whether its license works for your project. Leaderboards = compare models See how different models perform instead of relying entirely on hype. Datasets = what AI learns from Collections of information used to train and test models. Local models = AI on your own computer More control and privacy, but a little more technical. Bottom line: Claude helps you build it. Hugging Face helps you find the AI that can power it.
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If your vibe-coding tool keeps botching one stubborn part of the build- and you have no desire to learn actual code - Models can help fill the gap before you spend 40 prompts begging Claude to fix it. - HL |
You can get our free bundle of 250 prompts to make money below! |
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