The author created several GPTs, including a Dejargonizer that decodes technical terms, a JavaScript Code Interpreter that runs JavaScript code, a Dependency Chat that identifies project dependencies, and a fun Animal Chefs GPT that generates recipes with animal-themed stories.
. The author experiments with the model, asking it to add a walrus to a prompt, and is surprised to find that the model can maintain consistency between images with a slightly altered prompt using a "seed" number. The author also delves into the underlying prompt engineering of DALL-E 3, revealing policies and guidelines that govern the model's image generation, including diversity and inclusivity guidelines.
* **New Feature:** `openai-to-sqlite` tool now allows enriching data in a SQLite database using OpenAI's GPT3.5 model.
* **Sentiment Analysis Example:**
+ Update a `sentiment` column in a `messages` table using `chatgpt()` function.
* **Sentiment Analysis Example:**
+ Update a `sentiment` column in a `messages` table using `chatgpt()` function.
The author has also automated their weeknotes by using an Observable notebook, which generates the "releases this week" and "TILs this week" sections.
The notebook fetches TILs from the author's Datasette, grabs releases from GitHub, and assembles a markdown string for the new post.
* `llm` CLI tool for running prompts against large language models
* Automation of weeknotes using an Observable notebook
* Notebook generates "releases this week" and "TILs this week" sections
* Tool stores prompts and responses in a SQLite database
The notebook fetches TILs from the author's Datasette, grabs releases from GitHub, and assembles a markdown string for the new post.
* `llm` CLI tool for running prompts against large language models
* Automation of weeknotes using an Observable notebook
* Notebook generates "releases this week" and "TILs this week" sections
* Tool stores prompts and responses in a SQLite database
