I wonder how software development would be like if we had coaches like this.

But these new builders have a tool, they don’t suddenly have a newfound creativity.

I think with time we will stop seeing what we consider AI slop, simply because we know it’s not worth sharing. Instead great creative people will share very impressive things that simply wasn’t possible to build before.

Comments URL: https://news.ycombinator.com/item?id=46566444

Points: 29

# Comments: 7

Also, do you really understand what the numbers in that spreadsheet mean if you have not been participating in pulling them together?

- Create cards directly from camera input in the app.

- Create multiple cards from a source text, so that she can just supply a text body and the llm figures out what is important, and then the user can just discard cards they do not find useful.

Yeah, it's very barebones at the moment, if you swipe the name of a deck left on the list of decks, you should be able to delete it, and I haven't added the ability to delete individual cards yet. Shuffling is not possible, adding it to the backlog.

So I started building something simple: a tool that lets me turn highlights into flashcards with as little friction as possible.

Just select text on your iPhone, share it with the app, and it creates a flashcard using AI — a Q&A pair and a short summary. You can browse cards in the app, or show them on your Home Screen, Lock Screen, or watchface of your Apple Watch.

This is my first iOS app, and building it has been a great learning experience. I’m using Supabase for the backend which have been mostly great.

Check it out: https://komihag.com

Looks like share extensions are not generally available inside the Books app, which is why my app is not showing up.

Thank you for making me aware of the issue!

I've built Komihåg [1] to try and combat this: Select any text on your iOS device and a flashcard is automatically created for you, and the app is then showing you the cards on the Home Screen / Lock Screen / Apple Watch Face.

I haven't gotten to implement any sophisticated scheduling algorithm yet but will definitely do that eventually.

[1] : https://komihag.com

I'm in a similar situation as you (developer having to do marketing) but have not gotten as far, so far I've only posted on a few subreddits and here on HN. Have you found any nice learning resources?

You select some text with your phone and share it with my app, then the shared text is reformulated to a flashcard (with the help of a llm).

You can then browse your flashcards in the app, but I’m also working on ways to show the cards to you with less friction: Like on the phone lock screen or on the face of your watch.

https://komihag.com

Just select any text on your iOS device and share the selection with the app, this will transform the shared text into a Q&A flashcard using LLMs. You can then scroll through your decks of cards in the app, or create a widget that displays your cards continuously.

I got the idea while reading machine learning books. Sure I could create Kindle bookmarks or copy paste interesting parts, but I noticed that I never really took the time to revisit them, so adding an iOS widget with the things I want to learn and that inevitably reaches my eyes many times per day seemed like a good idea.

The app is build with SwiftUI, using Supabase for authentication and storage.

The app is pretty bare bones at the moment, some ideas I have for improvement are: - Scheduling for the widget. E.g. show new card every 5 minutes. - Lookscreen widget / Watch widget. These cannot be interactive (I think), so I would have to reformulate the Q&A pair into a combined statement.

Let me know if you think the concept is interesting enough to continue working on.

Comments URL: https://news.ycombinator.com/item?id=43205163

Points: 4

# Comments: 5

>Now, you still want to train the best model you can by cleverly leveraging as much compute as you can and as many trillion tokens of high quality training data as possible, but that's just the beginning of the story in this new world; now, you could easily use incredibly huge amounts of compute just to do inference from these models at a very high level of confidence or when trying to solve extremely tough problems that require "genius level" reasoning to avoid all the potential pitfalls that would lead a regular LLM astray.

I think this is the most interesting part. We always knew a huge fraction of the compute would be on inference rather than training, but it feels like the newest developments is pushing this even further towards inference.

Combine that with the fact that you can run the full R1 (680B) distributed on 3 consumer computers [1].

If most of NVIDIAs moat is in being able to efficiently interconnect thousands of GPUs, what happens when that is only important to a small fraction of the overall AI compute?

[1]: https://x.com/awnihannun/status/1883276535643455790

You could also compute embeddings for the questions (don’t have to be OpenAI embeddings), and reuse the answer if the question is sufficiently similar to a prevously asked question.