Pinging an LLM: Claude as a User Space IP Stack
In the world of networking, the "ping" is the most basic diagnostic tool—a simple request for a response to verify connectivity. Usually, this process happens in microseconds, handled by highly optimized kernel-level code. But what happens when you replace the entire network stack with a Large Language Model (LLM)?
Adam Dunkels, the creator of the lwIP and uIP stacks, recently conducted a thought experiment to see if Claude could act as a user space IP stack. The goal was not efficiency, but exploration: could an LLM read raw IP packets byte-by-byte, reason through the protocol logic, and construct a valid binary response using only its internal reasoning capabilities?
The Experiment: Markdown as Code
The core premise of the experiment is the idea that Markdown can serve as a set of instructions (code) and the LLM acts as the processor executing that code. To achieve this, Dunkels created a command called ping-respond.md. This document provides Claude with a strict operational manual for handling a single ICMP echo request.
The Operational Workflow
Claude was instructed to follow a six-step process to handle the network traffic:
- Read: Execute a bash command to read one raw packet from a TUN device (via a Python helper).
- Parse IPv4 Header: Manually decode the hex string to identify the version, IHL, TTL, and protocol (ensuring it is ICMP).
- Parse ICMP Header: Identify the echo request type and extract the identifier and sequence number.
- Construct Reply: Manually swap source and destination IPs, update the TTL, and—most crucially—calculate the IP and ICMP checksums using 16-bit one's complement arithmetic.
- Write: Send the resulting hex string back to the TUN device.
- Report: Summarize the transaction.
Crucially, Claude was forbidden from using Python, bc, or any external calculator tools. All arithmetic had to be performed within the model's reasoning process, with the work shown for debugging purposes.
The Results: A Very Slow Pong
Using Claude 3.5 Haiku, the experiment was successful. The model correctly parsed the incoming hex string, performed the binary arithmetic required for the checksums, and returned a properly formatted ICMP echo reply.
However, the performance was predictably abysmal. The round-trip time (RTT) for a single ping was approximately 42,593 milliseconds—over 42 seconds. While this is an eternity in networking terms, Dunkels notes it is still faster than the legendary RFC 1149, which describes transmitting IP packets via avian carriers (carrier pigeons).
Community Reaction and Technical Debate
The experiment sparked a variety of reactions from the Hacker News community, ranging from fascination to frustration over resource waste.
The "Stochastic Parrot" vs. Logic Engine
Some observers noted that this experiment challenges the notion that LLMs are merely "dumb auto-completers." The ability to perform multi-step binary arithmetic and adhere to a strict protocol specification suggests a level of logical reasoning that goes beyond simple pattern matching.
Efficiency and Practicality
Conversely, many commenters pointed out the obvious inefficiency of the approach. One user mentioned a colleague who tried to use LLMs as an Intrusion Detection System (IDS), urging them to use BPF (Berkeley Packet Filter) instead:
"I begged him to use BPF and stop wasting sprint cycles trying to reinvent a shittier slower wheel."
Other suggestions included using "agent skills"—allowing the LLM to write and execute a specialized script to handle the packet—rather than performing the logic in the reasoning tokens. This would move the LLM from the role of the processor to the role of the compiler.
The Cost of "Fun"
The experiment also touched on the ethics of token usage. Some users expressed annoyance at the use of high-powered cloud models for "ridiculous" experiments, suggesting that a local model would have been more appropriate to avoid impacting global rate limits.
Conclusion
While using an LLM as a network stack is practically useless for production, it serves as a powerful demonstration of the flexibility of modern models. It proves that an LLM can simulate a low-level system process if given a sufficiently detailed specification. It transforms the LLM from a chatbot into a virtual machine, albeit one with a latency that makes carrier pigeons look like fiber optics.