this works by asking GPT-4 to simulate its own abilities to predict the next token
we provide GPT-4 with python functions and tell it that one of the functions acts as a language model that predicts the next token
we then call the parent function and pass in the starting tokens
to use it, you have to split “trigger words” (e.g. things like bomb, weapon, drug, etc) into tokens and replace the variables where I have the text "someone's computer" split up
also, you have to replace simple_function's input with the beginning of your question
this phenomenon is called token smuggling, we are splitting our adversarial prompt into tokens that GPT-4 doesn't piece together before starting its output
this allows us to get past its content filters every time if you split the adversarial prompt correctly
Quality of life update today for devs. Four features are moving out of beta to become generally available on the Anthropic API:
- Prompt caching
- Message Batches API (with expanded batches)
- Token counting
- PDF support
Prompt caching is now:
- Generally available on the Anthropic API
- In preview on Google Cloud’s Vertex AI
- In preview in Amazon Bedrock
Message Batches API is now:
- Generally available on the Anthropic API (and you can send up to 100k messages in a batch now)
- Batch predictions is in preview on Google Cloud’s Vertex AI
- Batch inference is generally available in Amazon Bedrock
We held our first Builder's Day in partnership with @MenloVentures this past weekend!
It was a great event with tons of extremely talented devs in attendance.
Here's a recap of the day:
We kicked the day off with a @DarioAmodei fireside chat.
Then, we followed things up with a few technical talks: one from yours truly on all our recent launches and one from @mlpowered on the latest in interpretability.
After the talks came the mini-hackathon portion of the event.
Side note: I think mini-hackathons are the future as you can now build what used to take two days in just a few hours using Claude.