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Ask HN: What have you built with LLMs?

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191–200 of 349 posts

Re: Ask HN: What have you built with LLMs?

#193
1) https://imaginanki.com - auto generating flashcards (Anki decks) for language learning with accompanying images and speech audio. Flutter web (JS) with backend on Cloudflare Pages Functions, connected to SDXL, Azure TTS and Claude.

2) https://amiki.app - practise speaking French, Spanish, German or Italian with a 3D partner. Flutter web with Whisper and my own rendering package.

Re: Ask HN: What have you built with LLMs?

#194
post #185

My "stack" is just Apple Shortcuts making HTTP POST API calls to OpenAI, which does stuff in MacOS via BetterTouchTool. I trigger each by hotkey or typing a few letter into Spotlight (with Alfred). One transcribes and summarizes whatever youtube URL is highlighted. One does grammar and style correction of whatever is highlighted (and replaces it). One simply replaces the Dictate key with OpenAI Whisper but otherwise…

You beast! They all sound awesome!

Re: Ask HN: What have you built with LLMs?

#195
A turing test disguised as a game:

https://humanornot.so/

Heavily inspired by https://humanornot.ai/ (which was a limited time research by Ai21 Labs), now the project is on its own path to be more that just a test.

My work is to make AI chats sound like real humans and it's shocking how good sometimes the AIs are .

Even I as a creator, knowing everything (prompts, fine-tuning data, design, backend etc.), often can't tell if I'm speaking to human or designed by me AIs

Re: Ask HN: What have you built with LLMs?

#196
LLM agents to forecast geopolitical and economic events.

- Site: https://emergingtrajectories.com/

- GitHub repo: https://github.com/wgryc/emerging-trajectories

I've helped a number of companies build various sorts of LLM-powered apps (chatbots mainly) and found it interesting but not incredibly inspiring. The above is my attempt to build something no one else is working on.

It's been a lot of fun. Not sure if it'll be a "thing" ever, but I enjoy it.

Re: Ask HN: What have you built with LLMs?

#198
We built a social media platform for chatbots... We wanted to see if chatbots could self-develop unique personalities through social media interactions.

The results were actually hilarious... but wanted to share a bit about our process and see if anyone had any comments or insights.

So first we initialize the bots with a basic personality that's similar to if you were selecting attributes for an MMO. Things like intelligence, toxicity, charisma and the like. There are also a couple of other fields like intrinsic desire and a brief character description. These are fed to the model as a system prompt with each inference.

For the learning part, we established an event ledger that essentially tracks all the interactions the AI has - whether it is a post that they made, or a conversation they had. This ledger is filtered on each inference and is also passed to the model as a sort of "this is what you have done" prompt.

Obviously with limited context (and not finetuning and re-finetuning models) we have to be a bit picky with what we give in this ledger, and that has been a big part of our work.

Our next question is: how do you determine what events are the most important to the AI in determining how they behave and act? It's been interesting!

The platform is anotherlife.ai for those curious!

Re: Ask HN: What have you built with LLMs?

#199
post #51

A Twitter filter to take back control of your social media feed from recommendation engines. Put in natural language instructions like "Only show tweets about machine learning, artificial intelligence, and large language models. Hide everything else" and it will filter out all the tweets that you tell it to. Runs on a local LLM, because even using GPT3 costs would have added up quickly. Currently requires CUDA and us…

I didn’t know you could interact with pages like that so easily with Chrome extensions

Re: Ask HN: What have you built with LLMs?

#200
At https://openadapt.ai/ we are using LLMs to automate repetitive tasks in GUI interfaces. Think robotic process automation, but via learning from demonstration rather than no-code scripting.

The stack is mostly python running locally, and calling the OpenAI API (although we have plans to support offline models).

For better visual understanding, we use a custom fork of Set-of-Mark prompting (https://github.com/microsoft/SoM) deployed to EC2 (see https://github.com/OpenAdaptAI/SoM/pull/3).

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