HN user
email: colin [at] lastepoch [dot] co
But improving each kid’s skill and confidence was the real mission. Instead of my desk job, I’d be asking Clayton how we could make Corey¹ use his body for rebounding. Or how Monte’s soccer skills could be best leveraged. Or how Evan, our best player, could become an on-court leader.
I wonder how software development would be like if we had coaches like this.
Everyone now have access to a tool that makes them nearly as powerful as only the most creative builders were before.
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.
China is getting closer to tech independence by the day, I imagine they are happy to sell their tech to anyone who is willing. Not saying this is good or desirable from a European perspective, but quite likely.
This is from April last year. I haven’t seen this angle of the Greenland saga mentioned a lot recently. Is it a coincidence that Trump started to talk about Greenland again right after he and Musk became friendly again?
Totally agree.
Also, do you really understand what the numbers in that spreadsheet mean if you have not been participating in pulling them together?
I'm really glad to hear that! Two things I've thought about that would probably make the process even smoother:
- 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.
Let me know how it goes! I'm not always very happy with how the cards turn out, will see what I can do with some LLM prompt tuning.
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.
I was reading a lot of technical books and kept highlighting things I wanted to remember — but I rarely went back to review them. The notes just sat there, on my Kindle or in the reading app.
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
I found this: https://developer.apple.com/forums/thread/762784
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’m sure the algorithm can play a huge role in the effectiveness of learning but for me the difficult part was always creating the cards and actually opening the app to practice.
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.
Your app looks cool! I've tried a few other apps doing something things, Clearspace is the one I'm using now. Will give yours a try!
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?
Working on a tool to remember things I read.
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.
What do you have in mind? I’ve thought about affirmations, that could maybe fit this approach as well.
Not yet ;)
R1 and O1 points towards a world where training models will be a small bucket of the overall compute. That doesn't mean the total amount of AI compute will stop accelerating, just that interconnected mega clusters is not the only or most efficient way to run a majority of future workloads. That should be negative news for the company that is currently the only one that is capable of making chips for these clusters, and positive news for the players that can run inference on a single chip, as they will be able to grab more parts of the compute pie.
Great article.
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?
If you haven’t already: Start to store question and answer pairs and reuse the answer if the same question is asked multiple times.
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.
Assuming we had millions of 3D-scans of different kinds of animals and their full DNA sequence. Wouldn't it be interesting to try to use generative ML methods to generate a dinosaur DNA based on a 3D model?
The TV Meter needs to hear the TV clearly so it can work, and people watching TV will need to see the screen while they’re watching TV.
And for some reason it's important that you can see the device even though the camera is turned off?
I also work with non-speech audio and I'm curious: Do you use pure DFT:s as inputs to your models or do you use mel-energies or MFCC:s? What kind of models do you use? Since there is not that much variation in the sound of a chainsaw I suppose either a regular fully connected or convolutional neural network?
Love what you are doing and I would love to see a technical blog post about how you work with audio!