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Trolling SMS spammers with Ollama

Hacker News

Trolling SMS spammers with Ollama

I've been working on a side project to generate responses to spam with various funny LLM personas, such as a millenial gym bro and a 19th century British gentleman. By request, I've made a write-up on my website which has some humorous screenshots and made the code available on Github for others to try out [0]. A brief outline of the system: - Android app listens for incoming SMS events and forwards them over MQTT to a server running Ollama which generates responses - Conversations are whitelisted and manually assigned a persona. The LLM has access to the last N messages of the conversation for additional context. [0]: https://github.com/evidlo/sms_llm I'm aware that replying can encourage/allow the sender to send more spam. Hopefully reporting the numbers after the conversation is a reasonable compromise.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: context, code · Missing: mac, agents, macos
56%56% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: llama, ide, io · Missing: https docs, excited, just released
49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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