Nice intro to word and sentence embeddings from co:here – What Are Word and Sentence Embeddings?

Introduction to pynytimes – The New York Times is one of the most trusted news source around the world. All their article metadata is easily available using their API, which is publicly available to everyone (though only for non-commercial use). All this data can be queried using a REST API, however setting it up can be quite time-consuming. This library solves that problem, now you can easily and quickly query the API without having to worry about the specific implementation.

The techniques behind ChatGPT: RLHF, IFT, CoT, Read teaming, and more

• A few weeks ago, ChatGPT emerged and launched the public discourse into a set of obscure acronyms: RLHF, SFT, IFT, CoT, and more, all attributed to the success of ChatGPT. What are these obscure acronyms and why are they so important? We surveyed all the important papers on these topics to categorize these works, summarize takeaways from what has been done, and share what remains to be shown.

SBIRs

• 9:15 Standup
• 10:00 Q3 Slides meeting with Loren
• 1:00 Bi-weekly
• Get any responses back on paper (HA!) and get ready to send out

GPT Agents

• Set up schema. I’m thinking four tables: 1) Experiment (name, date, user, run number), 2) Experiment params 3) Text (text, embedding, projection, cluster ID) 4) Cluster (experiment, cluster_number, cluster_name, include/exclude) – done
• Add automation fields and buttons – done
• For development, load result text automatically – done
• Hooking up DB to App. Got a lot done. Experiments, runs, and text are stored using test data.