The author created a system using the open-source large language model, Ollama, to automatically respond to SMS spam messages. Instead of simply blocking the spam, the system engages the spammers in extended, nonsensical, and often humorous conversations generated by the LLM, wasting their time and resources. The goal is to make SMS spam less profitable by increasing the cost of sending messages, ultimately discouraging spammers. The author details the setup process, which involves running Ollama locally, forwarding SMS messages to a server, and using a Python script to interface with the LLM and send replies.
Evan Widloski has developed a system to automatically engage and playfully frustrate SMS spammers using a large language model (LLM) named Ollama, hosted locally on his machine. He describes his motivation stemming from annoyance with frequent spam text messages and a desire to waste the spammers' time and resources, potentially discouraging their operations.
His technical implementation involves forwarding incoming spam SMS messages to a Python script. This script utilizes the Twilio API to identify the originating phone number. This number is then checked against a blocklist to prevent responses to legitimate messages. If the number is not blocked, the script formats the received spam message and feeds it into the Ollama LLM. The LLM is prompted to generate a nonsensical or absurd reply, designed to keep the spammer engaged in a pointless conversation.
Widloski details specific prompting strategies to guide the LLM's responses, such as instructing it to impersonate a confused persona, ask irrelevant questions, or fabricate outlandish scenarios. The generated response is then sent back to the spammer via the Twilio API.
He highlights the cost-effectiveness of running the LLM locally, minimizing expenses associated with cloud-based LLM services. Furthermore, he showcases examples of successful interactions with spammers, illustrating how the LLM-generated responses effectively lead the spammers on, often through multiple exchanges. Widloski also acknowledges potential drawbacks and ethical considerations, such as the possibility of the LLM inadvertently revealing personal information or generating offensive content. He emphasizes that his project is a personal experiment and stresses the importance of responsible use of LLMs. The post concludes with a reflection on the effectiveness of the approach and future possibilities, including refining the prompting strategy and incorporating more advanced language models.
Summary of Comments ( 21 )
https://news.ycombinator.com/item?id=42796496
HN users generally praised the project for its creativity and humor. Several commenters shared their own experiences with SMS spam, expressing frustration and a desire for effective countermeasures. Some discussed the ethical implications of engaging with spammers, even with an LLM, and the potential for abuse or unintended consequences. Technical discussion centered around the cost-effectiveness of running such a system, with some suggesting optimizations or alternative approaches like using a less resource-intensive LLM. Others expressed interest in expanding the project to handle different types of spam or integrating it with existing spam-filtering tools. A few users also pointed out potential legal issues, like violating telephone consumer protection laws, depending on the nature of the responses generated by the LLM.
The Hacker News post titled "Show HN: Trolling SMS spammers with Ollama" generated a moderate amount of discussion, with a handful of commenters engaging with the original poster's project of using a local large language model (LLM) to engage with and frustrate SMS spammers.
Several commenters expressed amusement and appreciation for the project's concept. One user praised the creative use of an LLM for this purpose, finding the idea of tying up spammers' resources with a bot entertaining and potentially helpful in reducing their effectiveness. Another commenter expressed a similar sentiment, enjoying the "trolling" aspect and appreciating the potential for wasting spammers' time and money.
A couple of users raised practical questions and concerns. One individual inquired about the cost-effectiveness of running the LLM locally for this purpose, wondering if the expense of compute resources would outweigh the benefits. Another commenter raised a point about potential legal implications or risks associated with engaging with spammers, though no specific legal issues were identified.
The original poster actively engaged with the comments, responding to questions and clarifying certain aspects of the project. They addressed the cost concern by explaining their utilization of a relatively small and efficient LLM, and also noted that the compute costs were currently negligible for their usage pattern.
While there wasn't extensive debate or deeply analytical discussion, the comments generally reflected positive interest in the project, with users finding the idea clever and potentially useful. The main points of conversation revolved around the amusement factor, the practicality and cost of the approach, and potential risks involved. The lack of a large number of comments suggests that while the project intrigued some users, it didn't spark widespread or highly controversial discussion.