Agent Architecture GPTwitter The LangChain Team
Build a personalized tweet generator by sampling from latent space using a weighted example selector and increasing weights on liked tweets.
Build a personalized tweet generator by sampling from latent space using a weighted example selector and increasing weights on liked tweets.
Summary
GPTwitter is a personalized AI‑generated social media platform that learns from a user’s like history to produce tweets that match their interests. The system samples directly from the latent space, using a weighted example selector that picks 2‑5 prior tweets based on their like weights. When a generated tweet is liked, it is added to the pool and the weights of the contributing tweets are increased, reinforcing the user’s preferences. Experiments with controlled style prompts were less effective than latent‑space sampling, leading the team to rely on temperature‑1.0 settings to boost creativity and hallucination.
The platform warns users of potential echo chambers and offers a reset button to diversify content. GPTwitter uses GPT‑3 for generation and demonstrates how hallucinations can be leveraged for creative output when factual accuracy is not critical. Future work includes expanding the reference corpus and exploring chain‑of‑thought prompts to improve consistency. The article illustrates how a simple LLM harness can create a fully personalized content feed without explicit guidance.
Key changes
- GPTwitter learns from a user’s like history to generate personalized tweets.
- It samples from the latent space using a weighted example selector that selects 2‑5 prior tweets.
- Liked tweets are added to the pool and the weights of contributing tweets are increased.
- Controlled style prompts were less effective; temperature‑1.0 settings were used to boost creativity.
- The platform warns of echo chambers and offers a reset button for diversification.
- GPT‑3 is used for generation, leveraging hallucinations for creative output.
- Future plans include expanding the reference corpus and exploring chain‑of‑thought prompts.
- The system demonstrates a simple LLM harness creating a personalized content feed.