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unslothai
hyperlearn
Jupyter Notebook2018–2023
unslothai / hyperlearn

hyperlearn

2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.

AI & Machine LearningSystems & InfrastructureLearning & ReferenceScience & EngineeringHardware & EmbeddedNetworking
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3Distinct people
5Mentions
1Explicit recommenders
2.5KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

Good point - the main issue is we encountered this exact issue with our old package Hyperlearn (https://github.com/danielhanchen/hyperlearn). I OSSed all the code to the…

danielhanchen used a recommendation phrase. One indexed comment matches. This is a phrase match, not a verdict.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 1:49:46 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year5 dated mentions

Observed mentions: 2018-08-30 19:08:58 UTC to 2023-12-02 05:02:28 UTC.

01232018-01-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 2 mentions; 2 submissions, 0 comments20182019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments20202021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 comments2023

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20182018-01-01 00:00:00 UTC2019-01-01 00:00:00 UTC220
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC000
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC000
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC000
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC000
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC303

What Hacker News says

5 sources
danielhanchen·Dec 2, 2023Recommendation phrase
Good point - the main issue is we encountered this exact issue with our old package Hyperlearn (https://github.com/danielhanchen/hyperlearn). I OSSed all the code to the community - I'm actually an extremely open person and I love contributing to the OSS community. The issue was the package got gobbled up by other startups and big tech companies with no credit - I didn't want any cash from it, but it stung and hurt really bad hearing other startups and companies claim it was them who made it faster, whilst it was actually my work. It hurt really bad - as an OSS person, I don't want money, but just some recognition for the work. I also used to accept and help everyone with coding up their startup's software, but I never got paid or even any thanks - sadly I didn't expect the world to be such a hostile place. So after a sad awakening, I de
Read the rest
cided with my brother instead of OSSing everything, we would first OSS something which is still very good - 5X faster training is already very reasonable. I'm all open to other suggestions on how we should approach this though! There are no evil intentions - in fact I insisted we OSS EVERYTHING even the 30x faster algos, but after a level headed discussion with my brother - we still have to pay life expenses no? If you have other ways we can go about this - I'm all ears!! We're literally making stuff up as we go along!
In Show HN: 80% faster, 50% less memory, 0% loss of accuracy Llama finetuning
danielhanchen·Dec 2, 2023
I agree fully - what do you suggest then? OSS the entire code base and using AGPL3? I tried that with https://github.com/danielhanchen/hyperlearn to no avail - we couldn't even monetize it at all, so I just OSSed everything. I listed all the research articles and methods in Hyperlearn which in the end were gobbled up by other packages. We still have to cover life expenses and stuff sadly as a startup. Do you have any suggestions how we could go about this? We thought maybe an actual training / inference platform, and not even OSSing any code, but we decided against this, so we OSSed some code. Any suggestions are welcome!
In Show HN: 80% faster, 50% less memory, 0% loss of accuracy Llama finetuning
danielhanchen·Dec 2, 2023
Sorry about that - I'm super new to pricing and stuff so it might seem off since I'm literally making the plans with my bro as we go along. If you don't believe the timings, I was the author of Hyperlearn https://github.com/danielhanchen/hyperlearn which makes ML faster - I also listed the papers which cite the algos. I also used to work at NVIDIA making TSNE 2000x faster on GPUs and some other algos like Randomized SVD, sparse matrix multiplies etc. If you have any suggestions on a more appropriate pricing strategy - I'm all ears!! I really don't know much about pricing and the open core model, so I'm making stuff up literally.
In Show HN: 80% faster, 50% less memory, 0% loss of accuracy Llama finetuning

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