Ask HN: What have you built with LLMs?

Also a Chrome extension [0]! The concept is to use the browser's context menu to run commands on the LLM, so it stays out of your way most of the time but feels like a somewhat native experience.

The stack is: 1. TypeScript/Node/tRPC/Postgres/Redis/OpenAI on the backend 2. SolidJS/Crxjs/tRPC on the front end 3. Astro for the docs/marketing site

And deployment is currently through render.com for the databases and servers, and manually via a zip file to the Chrome webstore for the extension itself.

[0] https://smudge.ai

My project team in university built a meme generator that uses GPT and Dall-E to generate image macros using Impact font. It was pretty entertaining.

Summarisation for calls, emails. Lots of extraction tasks & closed domain chatbots.

Deployment is usually FastAPI for business logic, Langchain or MS/Guidance library, LLM hosted via. HF-TGI server

For my expense sharing app [1], I added receipt scanning [2] in a few minutes and a few lines of code by using GPT 4 with Vision. I am aware that LLMs often are a solution looking for a problem, but there are some situations where a bit of magic is just great :)

It is a Next.js application, calling OpenAI’s API using a plain API route.

[1] https://spliit.app

[2] https://spliit.app/blog/announcing-receipt-scanning-using-ai

I've built a sales bot that would go over a predefined sales scenario like a real human would, being able to jump between steps and work with any complications real conversation would throw at it. It would appear fully human to whoever converted with it. Unfortunately, it was never deployed in production due to business reasons.

I've created just-tell-me [1] that summarizes youtube videos with ChatGPT. It's built with Deno, uses TypeScript and is deployed with Deno Deploy. It's open source, you can run it from CLI as well [2]

[1] https://just-tell-me.deno.dev/

[2] https://github.com/franekmagiera/just-tell-me

Absurd news article generator using local LLMs. I wanted to create a static website from the articles, but ultimately didn't think anyone would give a damn. In the same vein I create a person + CV generator, and a group chat between simulated crazy people.

I made a private Discord bot for me and my friends to talk to, that also generates images using SD 1.5 LCM.

The self-hosted backend uses the ComfyUI Python API directly for images, and the LLM part uses oobabooga's web API.

I wrote a script that takes in my credit card statement line by line and categorized the transactions into a custom set of categories that I cared about as well as generating a human readable description of the transaction.

Built a tool to summarize certification and licensing costs associated with jobs that require State credentialing.

I am building textool [1] an app that lets you create endpoints using GPT4. The idea is to make it so you can create "actions" for GPT4 assistants easily.

  - Nextjs
  - Deno Deploy for hosting the apis 
  - Supabase - postgres / auth
  - Shadcn

I want to use the t3 app stack [2] for v2.

It's really MVP, but I want to see if anyone is interested at all before I work on v2: creating gpts that come with databases!

  [1] https://textool.dev
  [2] https://create.t3.gg/

I built the copilot for flux.ai, which allows LLM-driven interaction with circuit schematics and datasheets.

The stack is react / cloud run / job queue / LLMs (several) / vector db.

I built https://tailgate.dev/ a few months ago. It can help with deployment of simple, client-facing generative web apps. There are a few simple demos on the home page!

> I worked on a chrome extension a few weeks ago that skips sponsorship sections in YouTube videos by reading through the transcript

You might want to connect that to SponsorBlock

https://sponsor.ajay.app/

I know chat is lame and overdone but here's my open source local AI chat app for macOS :). I wanted something simple enough for the non-technical people in my life who were using ChatGPT. For better or worse, those people are mostly not using chat AI much anymore. Seems like the initial awe wore off.

https://github.com/psugihara/FreeChat

I'm also working on a little text adventure game that I hope to release soon.

I have built a webapp for translating srt files: https://www.subsgpt.com

GPT-4 excels as a translator, but it often encounters issues with content warnings and formatting errors when translating entire subtitle files via ChatGPT. The solution is straightforward: divide the subtitle file into sections, focusing solely on translating the text and disregarding the timestamps. While it's feasible to have ChatGPT maintain the correct format, I've observed a decline in translation quality when attempting this in a single pass. My preferred approach is a two-phase method: first, translate the text, and then, if necessary, request ChatGPT to adjust the formatting.

The webapp splits the srt file into batches of 20 phrases and translates each batch. It also allows for manual correction of the final translation.

Ah and it's also serverless: you input your OpenAI token & select the model of your choice and the webapp makes the requests to OpenAI directly.

We've built a prompting, synthetic data generation, and training library called DataDreamer: https://github.com/datadreamer-dev/DataDreamer

I'm not sure if this is the category of "build" that you had in mind, but I used 3.5 to make a pay-as-you-go chat interface for the OpenAI API: https://benwheatley.github.io/YetAnotherChatUI/

I’m working on some tools to help GMs of tabletop games make content for their players.

Little demo is up at npcquick.app.

Doesn’t look like much rn, but there’s no openai involved. Currently it doesn’t even use a gpu.

An automatic video editor.

It should be cheap enough to deploy that it can be applied to relatively low-value content like video meeting recordings, so it can’t spend a lot of expensive GPU time analyzing video frames.

It also needs to be easily customizable for various content verticals and visual styling like branding and graphics overlays.

And everything is meant to be open sourced, so that’s fun!

I wrote about it on my employer’s blog here:

https://www.daily.co/blog/automatic-short-form-video-highlig...

I use sponsor block and it's really good, I like that it's community-driven but sometimes it's not available for videos so your solution sounds great.

I consult to a law firm as their founder-in-residence. For fun, I trained Llama 2 on all the non-client data of the firm so that people could ask it questions like "Who are the lawyers in Montreal who litigate American securities laws, what are their email addresses and what time is it where they are?" It's a njs app running on linode.

It's extremely simple, but people seem to find it useful.

I've made a couple games, though I am still having a hard time finding the soul of the game in the LLM and haven't released them; there's a historical roleplay game (that I plan to release soon), a storytelling game (the player tells stories to the LLM), a wander-a-world-aimlessly-and-chat game, and I never get further than 50% through the way of murder mystery games, though murder mysteries seem like an excellent structure.

I've built some abstract content development tools, generally focused on building larger content somewhat top-down (defining vibes, then details).

I'm working on a general project helper using the GPT-Vision, voice, and regular GPT. You setup the camera above your workspace, work on paper, and chat with the LLM while you do it. I think there's a lot of potential, but the voice stuff is quite hard to deal with... there's just a ton of stuff happening in parallel, and I find it very hard to code something reliable.

The stack I use is all in the browser, generally Next.js, Preact Signals, and my own code to call into GPT, Whisper, etc. I like having everything available for inspection, and I generally keep all the working bits visible somewhere. (This can be overwhelming when other people see it.)

But I haven't gotten over the deployment hump... the cost and complexity is a challenge. I've used Openrouter.ai recently in a project, and I think if I leaned on that more completely I'd find the release process easier.

I don't like selling. I wanted a way to practice cold calling in a realistic way. I set up a phone number you can call and talk to an AI that simulates sales calls.

I ended up using it for more general purpose things because being able to have a hands-free phone call with an AI turned out to be pretty useful.

It's offline now, but here's the code with all the stack and deployment info: https://github.com/kevingduck/ChatGPT-phone/

Edit: forgot to mention this was all running off a $35 raspberry pi.

We've made a lot of data tooling things based on LLMs, and are in the process of rebranding and launching our main product.

1. sketch (in notebook, ai for pandas) https://github.com/approximatelabs/sketch

2. datadm (open source, "chat with data", with support for the open source LLMs (https://github.com/approximatelabs/datadm)

3. Our main product: julyp. https://julyp.com/ (currently under very active rebrand and cleanup) -- but a "chat with data" style app, with a lot of specialized features. I'm also streaming me using it (and sometimes building it) every weekday on twitch to solve misc data problems (https://www.twitch.tv/bluecoconut)

For your next question, about the stack and deploy: We're using all sorts of different stacks and tooling. We made our own tooling at one point (https://github.com/approximatelabs/lambdaprompt/), but have more recently switched to just using the raw requests ourselves and writing out the logic ourselves in the product. For our main product, the code just lives in our next app, and deploys on vercel.

I am building a no code solution. Use case is simple: Write complete programs/software for the browser from natural language input.

https://domsy.io

Currently running on my little digital ocean droplet. Stack is javascript/python.

An AI agent to answer questions about any github/gitlab repository. www.useadrenaline.com

It does the work of understanding questions in the context of a repo, code snippet, or any programming question in general, and pulls in extra context from the internet with self thought + web searches.