Project status: Proof of concept
AI Filter is a Chrome extension that uses a local language model to filter your social media feeds (currently, only Twitter / X) according to your instructions. For instance, you can say:
Hide all tweets, except for tweets about machine learning (ML), artificial intelligence (AI) and large language models (LLMs).
or:
- By default, show all tweets
- Do not show any tweets related to cryptocurrencies, blockchain, Bitcoin, Ethereum or related projects.
AI Filter will follow your instructions, hiding the appropriate tweets in the background as you browse Twitter.
Note that because it is a Chrome extension, it only works in the browser, not with the Twitter / X app on your phone or other device.
This project is in the proof of concept stage, which means that it requires some technical know-how to get started.
- Install vLLM using these instructions. Note that a CUDA (Nvidia) GPU is required.
- Download a language model. AI Filter has been tested with Nous Hermes 2 - Solar 10.7B but you can try other models. NH2S 10.7B just barely fits on a 24G GPU. Note that if you use another model, you must edit the prompt template (see below).
- Start vLLM (see sample command line below)
- Download the extension from the releases page and decompress it
- Go to chrome://extensions/ and enable developer mode
- Choose "Load unpacked" and select the directory containing AI Filter
- Click on the extension in Chrome's toolbar and navigate to the options page to edit the instructions, vLLM server URL, and other options
- You're all set! Visit X.com and AI Filter should automatically hide the tweets you told it to.
- Like all LLMs, AI Filter is not perfect. It will sometimes hide tweets that you want to see, and sometimes show tweets that you want to hide. If you're not getting the results you want, try tweaking your instructions or try out different models.
- If you change options (instructions, prompt, etc.) you will need to reload X for the new settings to take effect.
- You can use this example command to start vLLM (see vLLM docs for details of what the flags do):
python -m vllm.entrypoints.openai.api_server --gpu-memory-utilization 0.99 --max-model-len 4096 --enforce-eager --model /path/to/Nous-Hermes-2-SOLAR-10.7B --swap-space 32 - If you use a different model, you need to change the prompt template in the extension settings.
The LLM model card on Huggingface should give an example of what prompt template to use. When setting the prompt template,
{tweet}will be replaced with the contents of the tweet to evaluate, and{prompt_instructions}will be replaced with your instructions. - You may be able to use GGUF quantized models if you're willing to do a little hacking. See this issue for the current status of GGUF support in vLLM.
- The extension logs quite a bit of diagnostic info to the browser console. If something
isn't working the way you expect, you can check these logs for information on what might be going wrong.
If you still can't figure it out, add more logging in
resources/content.jsand rebuild from source (see below).
AI Filter is an open source project, so you can play with the source code and experiment as you like. To build the project:
- Clone the repository
- Navigate to the chrome_extension directory
- Using npm, install all dependencies
- Run
npx parcel build resources/content.js - Load the extension from the resulting
bundled/directory
Most of the time when we use LLMs like ChatGPT, we want them to generate text in response to some input prompt. AI Filter works in a similar way, but it builds a prompt that tells the LLM to follow the user's instructions, and then only output one token: YES if the tweet should be displayed, or NO if it shouldn't.
Because LLMs have some randomness in their output, AI Filter directly calculates the probability that the LLM outputs YES or NO, and then uses the NO probability to choose whether to hide tweets. The specific cutoff probability for hiding can be adjusted in the extension's options page.
- Why did you build this?
A lot of people are thinking about what it means to have unaligned AI and how that might harm humanity in the future. Most of these concerns are fanciful stories about "paperclip maximizers" or biological weapons development. These stories detract from the most harmful AI systems that are present today: Social media recommendation engines. Social media recommendation engines optimize for engagement, which often means content that enrages us, makes fun of our out-group, or presents a clever meme instead of thoughtful discussion.
AI Filter is a way to fight AI with AI: It gives the user the ability to take back some control from recommendation engines and choose for themselves what they want to see or not see.
- Won't this just lead to more "social bubbles" where people only see content that they already agree with?
Yes, probably. If you use this extension on a regular basis, you may want to consider occasionally inverting the filter's instructions by hiding what you normally see and showing what you normally hide.
- How can I help?
Engineers: Submit PRs! There are lots of improvements possible, like improving the UI, adding support for HuggingFace inference, adding support for other social networks, and cleaning up the code.
Local LLM users: Try different models and prompts, and share the results!
Other supporters: Share the project on your favorite social network!
- How do I download a language model?
Navigate to the language model's page on HuggingFace, then choose "Files and Versions". Click the three dots button next to "Train" and choose "Clone repository". Follow the instructions displayed by HuggingFace.
- Why use vLLM instead of llama.cpp's server?
vLLM provides continuous batching, access to token log probabilities, an OpenAI compatible API, and is easy to install. llama.cpp provides an OpenAI compatible server, but it currently doesn't expose logprobs, doesn't support advanced features like continuous batching, and requires a more complicated build process.
- Can I just use OpenAI's endpoint instead of a local LLM?
Yes, you could point the extension to the real OpenAI API, but back of the envelope calculates indicate that it would cost somewhere around $0.02 just to load the Twitter.com home page, and the cost would grow as more tweets were processed. You could easily spend $5 or more just casually scrolling through Twitter, which seems excessive.
- A 10.7B model is too big for me to run, what can I do?
I haven't tested other models, but I suspect a high quality small model like Phi-2 might work almost as well. You can also try a quantized model, but see the vLLM issue above regarding support for GGUF models.