CoffeePython

Thanks for the response! Sounds interesting. I’ll have to look more into it. I love the idea of building software as a charity

Ottofield ( https://www.ottofield.io/ )- Conversational Scheduling for Home Service businesses.

The idea came from being in the trades in my previous career. Companies in the trades spend a ton of money on advertising but fail to capitalize on a lot of their incoming leads. We use humans to answer incoming text, fb messenger, and webchat questions for people like electricians, hvac techs, plumbers, etc.

Long term idea is that as we get more data on how these conversations typically occur, we can build some ML systems to lower the cost of answering the questions.

It's still MVP stage right now. Got a few customers and planning on ramping up marketing in January.

This is cool. I'm curious about what your main marketing channels are. There is another project this reminds me of another project called Simple Analytics.

How are the developers compensated? Are you all doing this for free? Love the project but interested in the practicalities behind it.

I've been using Clubhouse for a new solo project as well. I love it. It's so much faster than Jira. It's refreshing using something that loads so fast and has a UI that is nice to look at.

+1 for Notion. It’s the first note taking app that I actually enjoy using. I love the UI/UX and that it has a synced IOS app.

I think this is a misplaced worry. If you’re a high quality engineer than you shouldn’t compete on price. I do some freelance work but and have found it to be extremely lucrative. (Not in the mobile space though FWIW. Mostly NLP projects) You have to find a niche and provide a ton of value. Being an IOS developer that just delivers features is one way to sell yourself. A better way be positioning yourself is as an IOS expert that knows intricacies which will save the customer $10,000’s in costs via lower maintenance costs, faster dev time, etc

Even with one of the most common software businesses, software consulting, you could reach this amount with 10 people in a fairly straightforward fashion.

10 software engineers at say a salary of $100,000 a year. Consulting rate at 3x would be $3M a year.

I find it strange that the part of the conversation where AI has the potential to save thousands of hours of human labor doesn’t show up more often in these types of threads.

That being said, we need to definitely be thinking about lowering the cost (environmental and monetary) of training these models. I’m glad research is being done within this domain.

I’d love to see a study on what the human labor cost potential vs. training environmental costs would be for certain large models.

This is a great first step. College athletes are extremely exploited. The amount of value they bring the universities vs. benefits they currently receive is laughable. It's wild to read about coaches who make millions of dollars per year coaching these teams complain about the athletes being entitled.

Interesting to see deep learning techniques entering the realms of non-cs focused academia. I recently heard of another example from the Planet Money podcast[1], that used deep learning to perform an elephant census.

One of my favorite things about ML is using it to reduce human labor hours on tasks like these.

[1]https://www.npr.org/2019/08/09/749938354/episode-932-deep-le...

I used to be in the trades. I read hacker news when I started getting into programming and now I work full time as a software engineer. So.. yes non-tech folks read hacker news!

Yeah I had to read this over 3 or 4 times before I understood exactly what the author was saying. Very unintuitive way of putting it.

I’m interested in this as well. I’m work for different clients frequently and it seems like there is always so much time wasted on knowledge transfer when the client already has a system for recording these types of things.

Interestingly it seems some teams from within a company use the tools more effectively. I think it’s largely about building a culture around sharing information. Confluence is the tool I’ve seen most commonly used. I don’t have strong opinions one way or the other about it, but would like to see what tools effective teams are using

None. We sign NDA's for specific clients. Other than that, we don't have a non-compete or similar clause prohibiting side work. In fact, it's encouraged. We talk about our side projects openly. Also, if you want to have some company backing on your project, you can get paid 50% of your effective hourly rate for every hour you work on your side project if you agree to split potential profits with the company. Kind of an in-house funding.

Some feedback:

You should put an example of a use case for some of the task bars in your website photos. "Task Progress" doesn't really convey any type of value to me. Maybe a specific example like "Gym sessions attended" would help show new users what the app can be used for.

Yeah same here! The daily mixes that Spotify makes and groups based on my different listening habits has been pretty great. I get a ton of value out of it.

>In contrast, take music recommendations. If I give you a series of suggested songs, can you detect a pattern?

It seems to me that there has been a lot of success in recommendation engines (i.e. Spotify, the "Netflix Algorithm", etc). Maybe not so much on a smaller scale, but large companies are using recommendation engines all the time to keep user's engaged.

Other than that, I agree with the rest of the post. The eagerness to throw ML at any and everything can be kind of exhausting. When used right, it can be super powerful but sometimes a simpler method that achieves 80% of the results may be enough for certain use cases.