81–90 of 356 posts
I have ollama responding to SMS spam texts. I told it to feign interest in whatever the spammer is selling/buying. Each number gets its own persona, like a millennial gymbro or 19th century British gentleman. http://files.widloski.com/image10%20(1).png http://files.widloski.com/image11.png
This is fantastic. How have your hooked up a mobile number to the llm?
Not sure it qualifies, but I've started building an Android app that wraps bergamot[0] (the firefox translation models) to have on-device translation without reliance on google. Bergamot is already used inside firefox, but I wanted translation also outside the browser. [0]: bergamot https://github.com/browsermt/bergamot-translator
I programmed my own version of Tic Tac Toe in Godot, using a Llama 3B as the AI opponent. Not for work flow, but figuring out how to beat it is entertaining during moments of boredom.
U.S. FIRST STRIKE WINNER: NONE
USSR FIRST STRIKE WINNER: NONE
NATO / WARSAW PACT WINNER: NONE
FAR EAST STRATEGY WINNER: NONE
US USSR ESCALATION WINNER: NONE
MIDDLE EAST WAR WINNER: NONE
USSR CHINA ATTACK WINNER: NONE
INDIA PAKISTAN WAR WINNER: NONE
MEDITERRANEAN WAR WINNER: NONE
HONGKONG VARIANT WINNER: NONE
Strange game. The only winning move is not to play
I'm playing with the idea of identifying logical fallacies stated by live broadcasters.
I have ollama responding to SMS spam texts. I told it to feign interest in whatever the spammer is selling/buying. Each number gets its own persona, like a millennial gymbro or 19th century British gentleman. http://files.widloski.com/image10%20(1).png http://files.widloski.com/image11.png
Cool! Do you consider the risk of unintentional (and until some moment, an unknown) subscription to some paid SMS service and how do you mitigate it?
My husband and me made a stock market analysis thing that gets it right about 55% of the time, so better than a coin toss. The problem is that it keeps making unethical suggestions, so we're not using it to trade stock. Does anyone have any idea what we can do with that?
I have a tiny device that listens to conversations between two people or more and constantly tries to declare a "winner"
I'm playing with the idea of identifying logical fallacies stated by live broadcasters.
That's fantastic and I'd love to help
JetBrains' local single-line autocomplete model is 0.1B (w/ 1536-token context, ~170 lines of code): https://blog.jetbrains.com/blog/2024/04/04/full-line-code-co... For context, GPT-2-small is 0.124B params (w/ 1024-token context).
I wonder how big that model is in RAM/disk. I use LLMs for FFMPEG all the time, and I was thinking about training a model on just the FFMPEG CLI arguments. If it was small enough, it could be a package for FFMPEG. e.g. `ffmpeg llm "Convert this MP4 into the latest royalty-free codecs in an MKV."`
I have ollama responding to SMS spam texts. I told it to feign interest in whatever the spammer is selling/buying. Each number gets its own persona, like a millennial gymbro or 19th century British gentleman. http://files.widloski.com/image10%20(1).png http://files.widloski.com/image11.png