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
Today we're introducing Skills in claude dot ai, Claude Code, and the API.
Skills let you package specialized knowledge into reusable capabilities that Claude loads on demand as agents tackle more complex tasks.
Here's how they work and why they matter for the future of agents:
At a high level, the best analogy I've heard for Skills is something like Neo learning Kung Fu in seconds in the Matrix.
We're "loading in" specialized knowledge to our general agents at runtime.
At their core, Skills are simple. They're just a folder with a file.
The file starts with a name and description, then contains instructions, code, and resources. This simplicity means anyone can now specialize Claude without building custom agents. SKILL.md
We’re running a “Built with Claude Sonnet 4.5” challenge.
We want to see the coolest things you can build with 4.5 in the next week.
Four winners will receive one year of Claude Max 20x and $1k in Claude API credits.
We will select four winners:
“Keep Coding” Award - most technically impressive implementation
“Keep Researching” Award - most compelling exploration of a topic
“Keep Learning” Award – best educational application
“Keep Creating” Award – most artistic use-
To enter, quote post the first tweet of this thread through October 7 with what you built with Claude 4.5:
- How you built it (prompts, agents, MCP servers, workflows)
- Screenshots or demos
- Must be your own work, built with Claude Sonnet 4.5 (Claude.ai, Claude app, Claude Code, Claude Code SDK)
- We will select winners based on ingenuity, creativity, and community response.
We’ve rolled out another update to Claude Code to help customize your workflows: Hooks.
Hooks are user-defined shell commands that execute at various points in Claude Code’s agent loop.
They give you deterministic control over Claude Code’s behavior to ensure certain actions always happen at certain times.
You can create hooks for:
- Notifications (e.g. via Slack) on prompt completions
- Logging and observability
- Custom permissions and approvals
- Running lints after every write
We've simplified local MCP usage by creating something new we call Desktop Extensions (.dxt files).
These package your local server, handle dependencies, and provide secure configuration so you can one-click share and install local servers on Claude Desktop and other apps.
dxt's are zip archives containing the local MCP server as well as a manifest.json, which describes everything Claude Desktop and other apps supporting desktop extensions need to know.
Multi-agents systems are the next frontier of AI applications. At Anthropic, we found that multi-agents beat single agents by up to 90%+ on some complex tasks.
We wrote a blog post detailing practical tips for building multi-agent systems based on our own experiences:
Let's start with some context:
This post is based on our learnings from developing claude dot ai's Research feature. We define a multi-agent system to be multiple agents (LLMs autonomously using tools in a loop) working together.
The architecture looks something like this:
A lead agent analyzes your query and spawns specialized subagents that search in parallel.
Each subagent gets its own context window and can pursue independent research paths, then reports findings back to the lead agent.