Write Twitter / X, Mastodon, or Threads-style posts in my voice.

Watch me code this up on YouTube! I'm intending to start this project at 9 am PT on Nov 29, 2023, and I have set myself a deadline of 12 noon PT: 3 hours total.

You can try the end result at write-my-posts.gaborcselle.com. You can have GPT write posts in your voice by following the "How to Run" section below.

1. Content: 25% of the total time of the livestream was watching the OpenAI finetune console. That is not amazing content.
2. Viewership: We had a total of 16 viewers overall and no chats, confirming that this is not great content.
3. Data procurement: The approach of using my downloadable Twitter archive did work, that was surprisingly easy to reformat into training / test data. (Grr Twitter for not providing a free Twitter read API.)
4. Finetune on small amounts of data: The overall approach of finetuning GPT-3.5 to match my Twitter tone did work, even with a small sample of 200 tweets train / 40 tweets validation we got to a pretty good point in matching my tone, even if the content made no sense.
5. Finetune on large amounts of data: I then trained a model on all my tweets that weren't replies (~4000 train/~500 validation) and it's a little bit better but not phenomenally so. The posts it produces are a bit more cohesive than the smaller model.

Write Twitter / X / Mastodon / Threads posts for me, in my voice.

Download all my previous tweets, and finetune GPT-3.5-turbo on it. Then build a simple web app to generate posts. I'll use topics as the prompt. The topics will be extracted from my tweets using tweetnlp.

We did a crappy version of AI generated posts with our now-defunct social platform Pebble. We used few-shot prompting, and our users complained that the posts didn't sound like them at all. It let to an event we called "Ideageddon" - the largest user revolt we ever had. You can read more about the user revolt at blog post.

I'm curious if a more sophisticated approach to post generation by finetuning GPT-3.5-turbo might result in better posts. That's why I'm building this.

I was also inspired by this video by Pieter Levels who advocates for livestreaming yourself coding as that makes you dramatically more productive. Thus the YouTube stram.

I looked a bit into this.

1. My original idea was to use the X/Twitter API to pull my tweets. But Free tier of the Twitter API will no longer let you pull tweets. You have to pay $100/month for that. So that’s out.
2. I looked into using my Pebble archive that I downloaded in the final days of Pebble. But most of you don't have that. So that’s out.
3. I found a workaround: X/Twitter allows you to download your archive. They make this inconvenient by making you wait for it. Once you get it, it contains a beautiful archive of your tweets. I'll use that, and you'll be able to use it too. It contains about 10.9k tweets I've written.

At Pebble we used few-shot prompting … it didn't work well (see above).

This OpenAI Dev Day video explains when to use few-shot vs. RAG vs. finetune. (There’s an IMHO funny joke at 32:53.)

You need to give GPT somehting to write about. For each of my tweets, my plan is to use tweetnlp to do NER (named entity recognition) and then use the entity tags as the prompt.

GPT-3.5-turbo, since OpenAI allows easy fine-tuning.

We'll base the finetuning code on this notebook - note that this notebook has a bug in it, I sent a PR to OpenAI to fix it which is here and I'll be using my fork of the repo until that's merged.

1. Use tweetnlp to extract entities from my tweets
2. 80/20 split on the posts, 80% for training, 20% for validation
3. Finetune GPT-3.5-turbo on the training set
4. Plug the finetuned model into the Next.js app

I'm intending to start this project at 9 am PT on Nov 29, and I have set myself a deadline of 12 noon PT: 3 hours total.

• app/ - contains the Next.js app to interactively generate posts, deployed to Vercel at https://write-my-posts.gaborcselle.com/
• train/ - Python code to fine-tune GPT-3.5-turbo on my tweets

(This is a work in progress, I'll complete this after the livestream.) If you want to try this yourself:

1. Request your Twitter archive from https://twitter.com/settings/your_twitter_data
2. Install train/requirements.txt
3. cp train/config.example.py train/config.py and fill in your OpenAI API key
4. cp env.local.example env.local and fill in your OpenAI API key
5. Copy your Twitter archive's tweets.js into train/data/tweets.js
6. Run train/prep_finetune_data.py to generate train and validation files for your finetune of GPT-3.5-turbo
7. Run train/run_finetune.py to finetune GPT-3.5-turbo
8. After finetune completed, plug your model ID into app/api/chat/route.ts
9. npm install or equivalent to install the Next.js app
10. npm run dev to run the Next.js app

The Next.js example was forked from an example app written by Vercel at vercel/ai