Hilarious name for an open source project released by a government lab.
Readit Newslearndeeply commented on Show HN: Wiretap – Transparent WireGuard proxy server without root github.com/sandialabs/wir... · Posted by u/sleepyink
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learndeeply commented on Sequoia on SBF: A Future Trillionaire twitter.com/philbak1/stat... · Posted by u/MangoCoffee
> For me, it was simply a gut feeling. I’ve been talking to founders and doing deep dives into technology companies for decades. It’s been my entire professional life as a writer. And because of that experience, there must be a pattern-matching algorithm churning away somewhere in my subconscious. I don’t know how I know, I just do. SBF is a winner.
This is a fantastic insight into journalists. They need to fit their story into a narrative. If you match their stereotype, then they'll spin whatever tale they can imagine to convince themselves that it's really true. The level of self-deception that's needed is difficult to comprehend for me.
Is it common to have the same number of employees as there are companies funded at a VC? Is each one assigned a company?
learndeeply commented on Show HN: A GitHub business card generator github-business-card.verc... · Posted by u/scastiel
Brilliant way to get thousands of tech people to look at your business card.
Edit: This was updated, it used to point to his real Github username. Not that there's anything wrong with that! It's clever marketing.
Aside from the project itself, the production value of this video is crazy good.
learndeeply commented on Tinygrad: A simple and powerful neural network framework tinygrad.org/... · Posted by u/masterofsome
How is it compared to JAX? After TensorFlow and PyTorch, JAX seems very simple, basically an accelerated numpy with just a few additional useful features like automatic differentiation, vectorization and jit-compilation. In terms of API I don't see how you can go any simpler.
JAX is a DSL on top of XLA, instead of writing Python. Example: a JAX for loop looks like this:
def summ(i, v): return i + v
x = jax.lax.fori_loop(0, 100, summ, 5)
x = 5
for i in range(0, 100):
x += 1
Previous discussion: https://news.ycombinator.com/item?id=22374825
Wonder if anyone's turned Instagram's version of Blurhash into a library?
learndeeply commented on Tinygrad: A simple and powerful neural network framework tinygrad.org/... · Posted by u/masterofsome
I believe neural networks are over hyped sometimes.
They are not always the best tool for the job. There are lots of other ML techniques such as SVM, naive Bayes, k-nearest neighbor, decision tree, logistic regression, random forest etc. nobody is using because they lack the hype factor.
If something lacks some keywords like neural network, deep learning, reinforced learning, than it is deemed not cool.
I can't think of anything that neural nets can't beat, except small tabular data with boosted decision trees. Can you give some examples?
learndeeply commented on Tinygrad: A simple and powerful neural network framework tinygrad.org/... · Posted by u/masterofsome
The code is very easy to read. Doesn't seem like there's data/model parallelism support for training, which will be important for real-world use.