callmeed (u/callmeed)

callmeed commented on Ask HN: What are the algorithms used by predictions markets like Polymarket? · Posted by u/callmeed

tripplyons · 10 months ago

Not that sure about Polymarket, but Kalshi uses a limit order book and has incentives for market makers to provide liquidity. The prices are set by what people are willing to buy and sell for, not by an algorithm.

callmeed · 10 months ago

Ok, that's helpful but I'd argue even a limit order book has an algorithm (albeit simple)

callmeed commented on Ask HN: Any good essays/books/advice about software sales? · Posted by u/nikasakana

callmeed · a year ago

Predictable Revenue is considered canon for B2B/enterprise sales https://www.amazon.com/Predictable-Revenue-Business-Practice...

But not sure how applicable to agencies it is so YMMV.

callmeed commented on Ask HN: What's the best charting library for customer-facing dashboards? · Posted by u/rogansage

ushercakes · a year ago

https://www.chartjs.org/

It's pretty easy to configure and understand. It's the 80/20 principal of charts, it is 80% of D3 functionality for 20% the effort.

callmeed · a year ago

+1 I use chartjs for the web version of fatgpt.ai. It easy to setup and you can get charts going very quickly.

If I needed more dense data viz (ex a datadog-like system) I'd probably go with something D3 based

callmeed commented on Launch HN: Nango (YC W23) – Source-available unified API · Posted by u/rguldener

callmeed · a year ago

It's interesting, seems like a popular space lately (even within YC). Off the top of my head, there's merge.dev, Terra, Kombo, Workato.

Aside from the obvious question of "how are you different/better?" I'm most curious to know why you're going so broad initially. You've got everything from legal to devtools to gaming. Seems like the opposite of a wedge/beachhead approach. Why?

callmeed commented on Improvements to the fine-tuning API and expanding our custom models program openai.com/blog/introduci... · Posted by u/Josely

minimaxir · a year ago

> As a result, Indeed was able to improve cost and latency by reducing the number of tokens in prompt by 80%.

There's some exact words shenanigans here. Indeed may have reduced the number of tokens in the prompt by 80%, but they didn't reduce the cost by 80%: the prompt cost of inferring from a fine-tuned GPT-3.5-turbo ($3.00 / 1M tokens) is 6x the prompt cost of inferring from the base GPT-3.5-turbo ($0.50 / 1M tokens). If prompt tokens are cut to 20% of normal, then that implies the overall cost of the prompt tokens is 120% relative to their normal prompt: a cost increase! That's not even getting into the 4x cost of the completion tokens for a finetuned model.

Of course, Indeed likely has an enterprise contract reducing costs further.

callmeed · a year ago

I was somewhat involved in this project. Can't get into details but there were other factors/efforts not mentioned which allowed us to scale this while reducing cost per recommendation. As someone mentioned, I do believe we benefited from a price drop over time.

Regarding the monthly scale mentioned in article–we are way beyond that now.

A lot of really smart people worked on this and it was fun to watch unfold.

callmeed commented on Ask HN: What have you built with LLMs? · Posted by u/break_the_bank

gremlinsinc · 2 years ago

our large language model is large so you don't have to be.

callmeed · 2 years ago

Transformers that transform your body

callmeed commented on Ask HN: What have you built with LLMs? · Posted by u/break_the_bank

throwup238 · 2 years ago

> 1. Analyze calories/macronutrients from a text description or photo

Step 1: Is it a hot dog or not hot dog? https://www.youtube.com/watch?v=ACmydtFDTGs

I'm glad someone is keeping the dream alive!

callmeed · 2 years ago

Jokes aside, GPT-4 Vision is surprisingly good at noticing facts from food images. For example:

- In my chipotle bowl, it can tell if I had brown rice vs white rice

- In my In-n-out, it can tell if I got it protein style

It struggles with accurate weights/volumes but I'm excited about where this is going.

callmeed commented on Ask HN: What have you built with LLMs? · Posted by u/break_the_bank

callmeed · 2 years ago

I'm building a weight-loss app that leverages LLM to do 2 things:

1. Analyze calories/macronutrients from a text description or photo

2. Provide onboarding/feedback/conversations like you'd get from a nutritionist

https://www.fatgpt.ai/

My stack is Ruby on Rails, PostgreSQL, OpenAI APIs. I chose Rails because I'm very fast in it, but I've found the combination of Rails+Sidekiq+ActionCable is really nice for building conversational experiences on the web. If I stick with this, I'll probably need a native iOS app though.

Vendor stack is: GitHub, Heroku (compute), Neon (DB), Loops.so (email), PostHog (analytics), Honeybadger (errors), and Linear.