Google Colab Promotion for fun and profit

Despite all the recent interest in LLMs and diffusers, there’s no getting around it: most AI work is still done by individuals and small teams, making small and specialised models. And they’re mostly doing it in Google Colabs.

Colabs have an unusual double benefit — they’re open-source, because ultimately they’re a Jupyter notebook. But they’re also subsidised, in that Google gives you access to free GPUs. The idea is to encourage you to upgrade to bigger GPUs and start paying for them, but if you don’t do that, you’ll find that the hardware constraint is actually quite interesting in itself — it’s a challenge to get as much as possible done with a resource limit that’s something like the level of a normal desktop computer.

Make a brilliant Colab, and the world’s your oyster — people will follow your Twitter, people will want to hire you, you’ll get offers to co-found companies, you’ll get universal respect and praise, girls will fancy you, and they’ll build a giant statue of you in your national Capital. Some of these are not entirely guaranteed.

In fact, none of them are guaranteed because, once you’ve made your kick-ass Colab, most of the time all that work disappears into a black hole. Outside of your usual collaborators, if you even have any, nobody cares — or, the people who might care will never see it. All that work, and it gets two likes, one of them from your Gran.

Here’s how to do better, and get one step closer to that statue. And if it helps make AI a little bit more open and distributed, that might not be a bad thing either.

The Basics: make it viewable

You’d be surprised how many people fall at the first hurdle: they post a Colab to the internet that only they can open, and then don’t know why nobody uses it. Don’t do that.

• Click on the “Share” button in the upper right corner of your notebook.
• Change the sharing settings to “Anyone with the link can view.”
• Optionally, you can also allow users to comment on the notebook if you want to encourage collaboration. Theoretically, you could also let them edit it. Don’t do that.

Source: Image by the author

Socials

Undoubtedly the first port of call should be to post on social media. There are better and worse ways to do that though, particularly:

PRO-TIP: Use ColabRating

ColabRating.com is a free tool to help with sharing Colabs. You post your Colab to Colabrating, set an image, description, and basic metadata, and then share the Colabrating link, which now has all those properties embedded.

So, this:

Google Colaboratory

Becomes this.

Alpaca-Lora (GPT)

This is well worth having, because the default OpenGraph for a Colab, as you can see, doesn’t look like much of anything in particular. You then have to spend all the characters of a tweet just saying what the Colab is. This way, the link does that for you, and the tweet can be used to tell people why they care about the Colab and should open it.

There are some other benefits; Colabrating posts also have proper Schema markup, so they’re more searchable, and the site has a small but very tailored audience of its own, has it’s own search and filtering system, and is a place to aggregate all comments on, and upvotes for, your Colab.

Here’s a screenshot of ColabRating.

Source: Image by the author

Twitter

Twitter is undoubtedly the best of the mainstream social networks for posting Colabs. Consider the following tips to increase engagement and visibility:

1. Craft a compelling tweet: Write a concise yet captivating description of your notebook, highlighting its key features or applications. Remember, you have a 280-character limit per tweet.
2. Use relevant hashtags: Include popular and relevant hashtags related to your notebook’s topic or field, such as #DataScience, #MachineLearning, #Python, #GoogleColab or #ColabRating. This will make your tweet more discoverable to users who follow these hashtags.
3. Mention related accounts: If your notebook involves a particular tool, library, or dataset, consider mentioning relevant Twitter accounts (e.g., @TensorFlow, @scikit_learn, or @kaggle). This can help attract the attention of those accounts and their followers.
4. Post at optimal times: To maximize engagement, tweet during times when your target audience is most likely to be online. Research suggests that the best times to post on Twitter are between 9 am and 3 pm, with peak engagement occurring around noon; adjust this for the timezone of your target audience.
5. Engage with your audience: Respond to comments, questions, or feedback on your tweet, and thank those who share or retweet your notebook. Engaging with your audience helps build relationships and fosters further discussion.
6. Pin the tweet: If you want to keep your notebook easily accessible to your followers, pin the tweet to your profile. This ensures that it remains at the top of your Twitter page for visitors to see.
7. Share updates or additional content: If your notebook evolves or you create related content, share updates or new insights via follow-up tweets. This keeps your audience engaged and encourages them to revisit your notebook.

By following these tips, you can maximize the visibility and impact of your Google Colab notebook on Twitter, reaching a broader audience and fostering engagement with your work.

LinkedIn

After Twitter, this is the next best social media site to post to.
All seven points above (including hashtags and mentions) apply here too, and in fact you can post identical content to your tweet and it can do quite well.
Remember though, since LinkedIn has a 1300 character limit, you also have the option of telling an entire story about your Colab; how you decided to make it, the problems you were trying to solve, an who or what inspired you to make it.
In fact, if it’s a really good story, consider:

Medium

Right here on Medium, there’s space for a proper story, good content can be evergreen, and it ranks well in search engines.

Online Communities

Subreddits

Here’s a list of subreddits where you can post and share your Google Colab notebooks, depending on the topic and content. Make sure to read the rules and guidelines of each subreddit before posting to ensure your submission is appropriate and relevant to the community.

1. /r/learnprogramming — A subreddit for learning and teaching programming concepts.
2. /r/Python — A subreddit focused on the Python programming language.
3. /r/datascience — A community for data science enthusiasts to share resources and knowledge.
4. /r/MachineLearning — A subreddit for machine learning research, news, and discussions.
5. /r/learnmachinelearning — A subreddit for beginners and experts to learn and discuss machine learning topics.
6. /r/artificial — A subreddit about artificial intelligence and related topics.
7. /r/deeplearning — A community focused on deep learning research and applications.
8. /r/compsci — A subreddit for theoretical and applied computer science topics.
9. /r/programming — A general programming subreddit, focusing on news and discussions.
10. /r/AskProgramming — A subreddit to ask programming-related questions.
11. /r/learnpython — A subreddit dedicated to learning and teaching Python.
12. /r/dataisbeautiful — A community focused on sharing data visualizations; if your notebook contains interesting visualizations, this could be a relevant subreddit.
13. /r/datasets — A place to share and discuss datasets; if your notebook works with a specific dataset or produces a new one, consider posting here.

Remember to tailor your post title and description to the specific subreddit audience to maximize engagement and ensure your notebook is well-received.

Discord servers

There are numerous Discord servers where you can share your Google Colab notebooks, depending on the topic and content. Here are some popular Discord servers related to programming, data science, and machine learning:

1. MLH (Major League Hacking) Community: A large community of programmers and hackers, with channels dedicated to various programming languages and topics. Invite link: https://discord.com/invite/MLH
2. AI & Deep Learning: Focused on artificial intelligence, deep learning, and machine learning, this server hosts discussions and sharing of resources. Invite link: https://discord.com/invite/BGUYrPJ
3. The Coding Den: A programming community that supports various languages and topics, with dedicated channels for help, discussions, and resource sharing. Invite link: https://discord.com/invite/code
4. Python: A Discord server specifically focused on the Python programming language, with channels for beginners, experts, and resource sharing. Invite link: https://discord.com/invite/python
5. Data Science: A community for data science enthusiasts to discuss, learn, and share resources related to data science and its sub-fields. Invite link: https://discord.com/invite/datascience
6. Programmer’s Hangout: A friendly community for programmers to share knowledge, collaborate, and help each other in various programming languages and topics. Invite link: https://discord.com/invite/programming
7. AI & Data Science: A server dedicated to artificial intelligence, data science, and machine learning, with channels for learning, discussions, and project sharing. Invite link: https://discord.com/invite/5J5EgYT

Hacker News

On Hacker News (https://news.ycombinator.com/), you can submit your Google Colab notebook by posting it as a new link. Follow these steps:

1. Visit the Hacker News website (https://news.ycombinator.com/).
2. If you haven’t already, create an account or sign in.
3. Click on the “submit” link at the top of the page.
4. In the “title” field, provide a descriptive and attention-grabbing title that reflects the content of your notebook.
5. In the “url” field, paste the ColabRating or shareable Colab link.
6. Click the “Submit” button to post your notebook.

Keep in mind that while Hacker News doesn’t have dedicated subcategories or threads like Reddit, it does have a tagging system. Be sure to use appropriate tags when submitting your post to increase its visibility and relevance to the community.

After submitting your post, engage with the community by responding to any comments or questions about your work. This will help build your reputation, foster relationships, and promote further discussion around your notebook.

Stack Overflow

Stack Overflow is primarily a platform for asking and answering technical questions related to programming and development. It is not designed for sharing projects like Google Colab notebooks directly. However, you can still leverage Stack Overflow to drive visibility to your notebook by following these guidelines:

1. Ask or answer a relevant question: If you come across a question that your Google Colab notebook addresses or solves, you can share the link to your notebook as part of a detailed, helpful answer. Make sure your answer addresses the specific question, and that your notebook is directly relevant to the topic.
2. Be cautious when sharing your notebook: Stack Overflow has strict rules against self-promotion and spam. Ensure that sharing your notebook is appropriate and adds value to the conversation. Do not post your notebook link in irrelevant questions, as this may result in downvotes or penalties.
3. Be descriptive and thorough: When sharing your notebook as part of an answer, provide context and explain how your notebook can help solve the problem or answer the question. A brief description of the notebook’s contents and its relevance to the question will help others understand its value.
4. Engage with the community: Respond to any comments or questions about your notebook or answer, and thank those who provide feedback or upvote your post. Engaging with the community helps build your reputation and encourages further discussion around your notebook.

By following these guidelines, you can contribute meaningfully to Stack Overflow while subtly promoting your Google Colab notebook. Remember that the primary purpose of Stack Overflow is to help others solve programming problems, so always prioritize providing helpful answers and resources over self-promotion.

Conclusion

There are loads of ways of promoting your Colab, and who knows what opportunities can come along when you do. It only takes one like-minded person to join forces with you and life can become very different in many positive and interesting ways. And it only takes a few million like-minded people before you can get that statue we mentioned in the intro. I’d pick a target somewhere between those two numbers, but it’s your call.

You’ll also find that showing people your stuff is fun in it’s own right — someone always has a new perspective you’d never considered.

Just remember — Don’t let all that fun and glory deflect you from continuing to write code.

Disclosure — I am part of the team that built ColabRating.