Well it erupted. (…and I sincerely hope that you didn’t quit your job to make custom GPTs for a living.)
A few people figured out how to trick the custom GPTs that are starting to roll out to the app store into revealing their prompt instructions.
The result is hands-down the most comprehensive and high quality repository of prompts that I have ever found:
https://github.com/linexjlin/GPTs
For additional context, many of these are “professionally-created”, bringing in thousands of dollars a month in revenue sharing to their creators. I’ve shared elsewhere that I think it’s a terrible idea to try to be selling AI products right now, this is case-in-point. It has a nice parallel with the “AI as electricity” riff - once you know how to do the magic trick, it’s pretty hard to keep other people from copying you! (and very quickly everyone will expect it to be essentially free.)
In this video, we go behind the scenes of the creation of the revolutionary new GPT, Human Instruct Turbo. This is unedited, unplanned, completely raw footage of creating a custom OpenAI GPT from start to finish. Watch me fumble so you don’t have to.
In this video, I delve into the world of AI transcription, specifically focusing on MacWhisper, a leading tool for AI-driven voice-to-text transcription on Mac. We explore how to enhance MacWhisper with a custom glossary for accurate transcription of unique words and proper nouns, and share tips from the OpenAI Cookbook to refine your MacWhisper settings.
I also demonstrate real-life application by adding custom product names to our vocabulary, troubleshoot common transcription mistakes using find-and-replace, and explore the benefits of using larger AI models for improved accuracy.
Whether for professional or personal use, MacWhisper adapts to your specific language needs, offering privacy-focused and highly accurate transcriptions.
There were a few interesting ideas on customizing generative AI for eCommerce. Unfortunately I think the OctoAI product still has a long way to go, I frankly was not impressed with the onboarding experience after getting amped up by this webinar.
But one quote stuck with me all day. It has nothing to do with AI:
Find something that you’re really passionate about, find something that you can become the best in the world at, and find something that you can make money doing.
I think this is fantastic advice for anyone, not just entrepreneurs. A trifecta of how to decide what to work on. (58:39 in the video)
Dalle3 made this diagram. I don't know what the symbol in the middle is, but I think you get the point.
The pace of AI development over the last few months has been simply exhausting. Exhilarating, but exhausting.
Just look at this chart. This is just the open source LLMs.
I don’t check this leaderboard very often, but the Mistral models that were winning two weeks ago aren’t even in the top 20.
Here’s some cool stuff I found since I ate dinner an hour ago. (Sorry, I literally don’t know what else to do…there’s too much cool stuff.)
WikiChat on GitHub: WikiChat enhances the factuality of large language models by retrieving data from Wikipedia.
LLMCompiler on GitHub: LLMCompiler is a framework for efficient parallel function calling with both open-source and close-source large language models.
Flowise on GitHub: Flowise offers a drag & drop user interface to build customized flows for large language models.
1/n Was December 8th, 2023, the day when we've come to realize that AGI technology has been democratized? That it cannot be confined to the few and the GPU-rich? Let me explain to you what happened yesterday. pic.twitter.com/syLBuCVqG6
The issue with basic RAG strategies (chunking, top-k), is that they’re fine with plain .txt essays, but they do terribly over complex documents - w/ embedded objects like tables, diagrams 📊, and hierarchical sections 🪆
For those that follow, you’ll know I’m currently obsessed with AI, voice to text transcription, and the intersection of AI and voice-to to text transcription.
But what did happen this week is that MacWhisper found a way to 3x the speed of their transcription model. And that boost in speed is enough to make it better than Otter or HappyScribe for my use case.
So yesterday I unsubscribed from HappyScribe.
MacWhisper transcribes locally on your machine. You can trade accuracy for speed, and it has a free tier. I’ve paid for it because I want to support Jordi, and because I want to run batches of audio files through it.
The quality isn’t quite as good as HappyScribe, but since its local I can quickly get ChatGPT (or Jordi’s MacGPT) to fix it up. The added time to fix the differential errors is less than the time it takes to upload to HappyScribe, wait for the transcription, and download the file.
It is very easy to output nonsense. I have been able to achieve a high throughput of “SEO-optimized nonsense”, but I have been having a hard time getting technical content that isn’t more work to edit than it would have been to just write myself (or write step by step by “holding the AI’s hand”).
I also got “greedy” and was trying to build a system that would go “all the way” from ideas/keywords to fully written articles. This was too ambitious.
So now I’m taking a more modular approach. First building up the foundational concepts and research and structure, which will later serve as the training data for AI-assisted writing. My modular approach also involves a human-in-the-loop at every stage - because nothing is more annoying than propagating errors with AI.
But my eye is on scalability, so I’m making sure that each stage of the process is able to run in parallel, concurrently. In other words, the non-human steps should take the same amount of time for one or one thousand.
My big “it’s alive” moment today was getting GPTResearcher from Tavily to run concurrently:
🧟♂️ It's alive!
This did about 15 reports in about 3 minutes. I haven’t pushed to see when I hit my OpenAI API limit.
As breadcrumbs, here are the “resource reports” that I generated: https://polyspectra.com/tags/resource-reports/. These are not very engaging, nor are they meant to be, but they will serve as the foundation for the next step…
In addition to the human oversight at each step, this modular approach also let’s me mix and match the best tools for the job. Tavily is great for research, but the writing style is pretty rigid, and I don’t feel like re-writing it’s guts. So use it just for the step that it excels at.
There are three reasons why big companies are so obsessed with big data.
One, there are very few people inside the company that actually have a clue about what’s really important. (Or at least a low density of people who have a clue.)
Two, the people who do have a clue unfortunately have to justify every activity and expense to people who don’t have a clue. (And the people who don’t have a clue are usually the ones who are in charge of the budget.)
Three, big data makes it easier to draw spurious correlations. At least the people who don’t have a clue, and maybe the ones that do have a clue but just don’t understand statistics - they have no idea that the projection that they’re looking at, the extrapolation that justifies the decision, has no basis in reality.
