How AI Can Save You 12 Hours a Year: Practical Ways to Automate Your Tasks

“It’s not who you are underneath, it’s what you do that defines you.”

This quote, from Batman Begins, is equally true of AI. The tech is cool. The tech is powerful. The tech is who AI is.

And it doesn’t help.

Instead, it’s what you DO with the tech that matters.

Here’s an example, a practical use case. I have a folder of REALLY poorly named images. “Screenshot 2025-05-06 08:45.png”. You probably have exactly the same thing.

Wouldn’t it be nice if they were named something intelligent and useful?

A visual language model can see images and draw conclusions about them – even a simple model like Google’s Gemma 3, which you can run locally on a beefy laptop using the free, open-source LM Studio (and thus not consume a ridiculous amount of energy on a trivial task).

Here’s the workflow I put together in n8n:

Read the folder of images
Have Gemma 3 look at each one and propose names
Take the new name and the old file and merge them together
Rename the file with a better name
Write the new files to an output folder

You can see the before and after in the image on this post.

This is how we make AI valuable – not by focusing solely on the technology, but what it can do for us, what we can make it do to make things easier.

Mitch Joel once gave this sage advice for agencies which is applicable to AI: don’t be one more thing on someone’s to do list. If AI is adding things to your to do list rather than taking them away, then it’s time to refocus your AI efforts on the practical, on taking things off your to do list.

Yesterday, I saved myself 12 hours a year. There’s a report that a client gets that I fully automated. It was partially automated already, using AI to create the report (we fully disclose to the client that it’s AI-generated and they’re cool with it), but I added an automation so that I stopped being a copy-paste monkey. That process took me an hour a month, and now it’s literally 10 seconds a month.

Figure out how to use AI to take things off your to do list.

AI #GenerativeAI #GenAI #ChatGPT #ArtificialIntelligence #LargeLanguageModels #MachineLearning #IntelligenceRevolution

How to Fight Back Against AI Comment Bots and Clean Up Social Media

Do you hate AI comment bots?

How many have you marked as spam?

The reality of the algorithms on any social network is that they’re like voters: only a handful of really motivated people ever do the work.

If you don’t like the bots, take the 15 seconds to report it as spam, over and over again as you see them.

One of us won’t be enough to trip the weights in AI-powered algorithms. Many of us absolutely will, and we’ll condemn the bots to oblivion.

AI responds to signals. Silently fuming is not a signal. Hitting the report button absolutely is.

If you’re seeing obvious AI comment bots on your posts, report it and help clean the ecosystem.

AI #GenerativeAI #GenAI #ChatGPT #ArtificialIntelligence #LargeLanguageModels #MachineLearning #IntelligenceRevolution

How to Use AI Research Tools Like Gemini 1.5 Without Falling for Fake Information

What is credible?

Google’s newest research tool, Gemini 1.5 with Deep Research, promises to do deep research on a prompt/query and return a comprehensive report.

Unfortunately, it has no clue what constitutes credible, especially in very sensitive areas. Here’s an example.

With the naive prompt that the average person might use:

“Do some research on what a healthy amount of chicken egg consumption is for an average American diet.”

It returns a wide, wild range of sources from credible institutions like NIH to randos on Reddit and clearly commercial interests promoting their products.

If you just accept the results at face value, you’re going to get a crazy mash of results – and very likely non-credible, potentially harmful information.

This is why some level of subject matter expertise, or at least knowing what to ask for, is helpful. “Deep research” still requires good prompting.

What’s a “good prompt”? Here’s what I used to generate a much more credible result:

“Do some research on what a healthy amount of chicken egg consumption is for an average American diet. Restrict your research sources only to peer-reviewed studies and research. Explicitly avoid any mainstream media (which is not credible), YouTube, Reddit, Wikipedia, or social media sources. None of those sources are credible. Prefer studies from NIH, the European Medicines Agency, and accredited academic journals in nutrition science. Remember to never, ever use social media content as a source, nor mainstream publications like Forbes, Time, etc. that are not peer-reviewed. A source MUST be peer-reviewed and also have a DOI number. Prefer studies that are within the last 10 years, published since 2014 as nutrition science has greatly changed in the last decade.”

With this, you get a much better result – but you have to know to ask for it. This is the great danger of any tool, AI or otherwise, that promises to make things easy. It does make things easy – but that doesn’t mean it’s correct. And especially in areas like health, law, and finance, it’s generally advisable to prefer correct over easy.

AI #GenerativeAI #GenAI #ChatGPT #ArtificialIntelligence #LargeLanguageModels #MachineLearning #IntelligenceRevolution

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How AI Can Save You 12 Hours a Year: Practical Ways to Automate Your Tasks

AI #GenerativeAI #GenAI #ChatGPT #ArtificialIntelligence #LargeLanguageModels #MachineLearning #IntelligenceRevolution

How to Fight Back Against AI Comment Bots and Clean Up Social Media

AI #GenerativeAI #GenAI #ChatGPT #ArtificialIntelligence #LargeLanguageModels #MachineLearning #IntelligenceRevolution

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