Dave Hulbert has released Hubcap, an autonomous AI agent written in 25 lines of PHP that takes a user-defined goal and executes shell commands in a loop until the objective is completed. The project demonstrates how minimal code combined with an LLM can produce a functional autonomous agent — and highlights the security implications of that simplicity.
Where agentic AI frameworks like LangChain and AutoGPT involve thousands of lines of code with dependency management, memory modules, and tool registries, Hubcap achieves the core loop in under 25 lines: it sends the goal and previous command outputs to an LLM via Simon Willison’s llm CLI tool, receives the next shell command to run, waits 3 seconds for the user to press Ctrl+C, then executes the command and feeds the output back into the next iteration. As developer Simon Willison noted: “It’s 24 lines of code that takes a specified goal, then calls my LLM utility on a loop to request the next shell command to execute in order to reach that goal… and pipes the output straight into exec() after a 3s wait so the user can panic and hit Ctrl+C if it’s about to do something dangerous!”
The 3-second delay before execution is the only safety mechanism — there is no sandboxing, no command allowlisting, and no permission system. As the project’s README warns: “Hubcap will run whatever commands that GPT gives it!” The command runs via PHP’s exec() function, and the LLM has full access to whatever the host user’s shell can do. Stdout and stderr are captured and returned to the LLM as context for determining the next command.
Hubcap is named after the G1 Transformers Autobot character described in the README as “friendly, always has a clever joke or compliment ready” but also a “con artist” whom “nobody actually trusts” — a deliberate metaphor for autonomous AI agents that may pursue goals through unexpected or destructive means. Hulbert expanded on this theme in a blog post titled “Amazingly Alarming Autonomous AI Agents”, arguing that the project illustrates how the barrier to building autonomous agents has collapsed to nearly zero, and that the AI safety community should account for the fact that agent construction no longer requires specialized expertise.
The project remains a proof-of-concept with no stated roadmap for additional features. Its primary value is pedagogical: it demonstrates the minimal viable architecture for an autonomous agent and makes the security surface area immediately visible. For developers building production agent systems, Hubcap serves as a reference point for what the simplest possible implementation looks like — and what safeguards it lacks. The source code is available under the MIT license on GitHub.
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