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
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.
Claude 3.5 Haiku is now available on the Anthropic API, Amazon Bedrock, and Google Cloud's Vertex AI.
Claude 3.5 Haiku is our fastest and most intelligent cost-efficient model to date. Here's what makes it special:
3.5 Haiku surpasses all previous Claude models (except the new 3.5 Sonnet) on coding and agentic tasks, while being significantly more affordable -- a fraction of the cost of Sonnet and Opus.
This combo of speed+intelligence makes 3.5 Haiku a particularly good choice for long context tasks where the model needs to quickly ingest lots of info (e.g. a codebase/financial docs/etc) and provide high-quality outputs.
We're rolling out visual PDF support across claude dot ai and the Anthropic API.
Let me explain:
Up until today, when you attached a PDF in claude dot ai, we would use a text extraction service to grab the text and send that to Claude in the prompt.
Now, Claude can actually see the PDF visually alongside the text.
This allows Claude to more accurately understand complex documents, such as those laden with charts or graphics that aren't representable in text.
For example, I can now ask Claude questions about this PDF full of anatomy diagrams.
The new Claude 3.5 Sonnet is one of the best models I've ever used. We listened to the feedback on the old 3.5 Sonnet and worked to improve the new model in a number of ways.
Here are some of my favorite improvements:
Self-correction and reasoning
Tau bench is an agent benchmark that evaluates a model’s ability to interact with simulated users and APIs in customer service scenarios - the new 3.5 Sonnet is SOTA.
Personally I've noticed the the model gets stuck in loops less often than before.
Code
The new 3.5 Sonnet is really good at coding. It reached 49% on SWE-Bench Verified with access to only two tools and with no complicated scaffolding.
This is a nearly 16% jump over the old 3.5 Sonnet.