HN user

jph00

8,140 karma

Jeremy Howard -- answer.ai and fast.ai; hon professor at University of Queensland.

Posts82
Comments895
View on HN
minutes.substack.com 5mo ago

Tool Shaped Objects

jph00
3pts0
www.fast.ai 9mo ago

Let's Build the GPT Tokenizer: A Complete Guide to Tokenization in LLMs

jph00
20pts0
www.answer.ai 1y ago

How to Solve It with Code

jph00
11pts5
www.answer.ai 1y ago

/llms.txt–a proposal to provide information to help LLMs use websites

jph00
3pts0
gpucpp.answer.ai 1y ago

Gpu.cpp: Portable C++ GPU Compute Using WebGPU

jph00
5pts0
www.answer.ai 2y ago

Claudette, a new friend that makes Claude 3.5 Sonnet even nicer

jph00
22pts8
www.answer.ai 2y ago

What policy makers need to know about AI

jph00
81pts35
www.affuture.org 2y ago

Call-to-Action on SB 1047 – Frontier Artificial Intelligence Models Act

jph00
148pts103
www.answer.ai 2y ago

Efficient finetuning of Llama 3 with FSDP QDoRA

jph00
13pts3
www.answer.ai 2y ago

Fine tune a 70B language model at home

jph00
909pts206
arxiv.org 2y ago

Self-Discover: Large Language Models Self-Compose Reasoning Structures

jph00
4pts0
www.answer.ai 2y ago

Lessons from history's greatest R&D labs

jph00
153pts53
arxiv.org 2y ago

Self-Rewarding Language Models

jph00
95pts60
www.answer.ai 2y ago

A new old kind of R&D lab

jph00
289pts90
www.fast.ai 2y ago

AI and Power: The Ethical Challenges of Automation, Centralization, and Scale

jph00
2pts0
www.fast.ai 3y ago

First Two Lessons of "From Deep Learning Foundations to Stable Diffusion"

jph00
4pts0
fastai.github.io 3y ago

Practical Deep Learning for Coders

jph00
5pts0
www.fast.ai 3y ago

Deep Learning Foundations Signup and Open Source Scholarships

jph00
1pts0
www.fast.ai 3y ago

The Jupyter+Git problem is now solved

jph00
301pts163
github.com 3y ago

Unsilence: Console Interface and Library to remove silent parts of a media file

jph00
3pts0
shiny.rstudio.com 3y ago

Shiny for Python

jph00
8pts0
www.fast.ai 5y ago

Is GitHub Copilot a blessing, or a curse?

jph00
170pts193
abseil.io 6y ago

Announcing TCMalloc

jph00
1pts0
www.fast.ai 6y ago

Data Project Checklist

jph00
2pts0
www.fast.ai 6y ago

Nbdev: Use Notebooks for Everything

jph00
28pts5
www.propublica.org 6y ago

Millions of Americans’ Medical Images and Data Are Available on the Internet

jph00
86pts33
towardsdatascience.com 6y ago

Creating a Pop Music Generator with the Transformer

jph00
5pts0
www.fast.ai 7y ago

Deep Learning from the Foundations

jph00
10pts0
www.fast.ai 7y ago

A LaTeX Add-In for PowerPoint

jph00
3pts0
www.fast.ai 7y ago

Fast.ai Embracing Swift for Deep Learning

jph00
7pts0
Grok 4.5 14 days ago

It takes a lot of digging to find their cache pricing - it's $0.50, which is unusually expensive.

The vast majority of input tokens are normally cached, so this is actually the price that matters. I wonder why it's so high.

I'm a big fan of sqlite-utils, but I really don't like how Python (particularly 3.12+) changes how sqlite's transactions work -- the native behavior explained in the sqlite docs is much better IMO. I understand why Python had to change it (to be compatible with other databases) but I don't think it's a good model for sqlite.

Therefore, I created apsw-utils, a port of sqlite-utils to the amazingly-awesome apsw lib -- which is a really idiomatic sqlite lib for python. It's here: https://answerdotai.github.io/apswutils/

I've used it in lots of projects including in significant production stuff, and it's always worked great for me. IMO if you're serious about doing sqlite in python, at some point you'll probably want to check out apsw.

It's both an illogical question and comes from a place of ignorance about a topic which one would expect folks nowadays to have some basic competence in.

First, the illogical part: the statement was about "inequality" to which you asked about wealth generation. These are two separate issues, and inequality is not, logically, necessarily tied to wealth generation. So "How does creating wealth hurt others?" is, at best, a non sequitur.

The ignorance part: there is a lot of empirical research over many decades showing the negative impact of wealth inequality on societies. With Google and AI there's really no good reason to ask such ignorant questions when in a moment you could educate yourself, and then ask an informed and thoughtful question instead.

You seem to have a rather limited understanding of what kinds of concurrency exist and how those needs are best met. Whether something is a server or not is not very relevant to this discussion.

SQLite is an excellent production db for many real world workloads, as has been widely documented. It is very different to Postgres, so requires learning a whole new skill set.

One way to think about it is that SQLite can work well for the parts of your system where there is naturally strong partitioning.

I couldn't find a CPAP mask that worked for me, even after 6 months of trying across 4 different masks.

Then I read studies showing oropharyngeal exercises can help treat sleep apnea. Originally I used https://snoregym.com/ for that, but then I switched to simply chewing gum once a day (being careful to chew on both sides of my mouth roughly equally), and also chewing my food a bit longer.

It totally cured my sleep apnea.

TIL HN still doesn't support rfc3492, 23 years after it was published, and so this domain is not rendered correctly on the site. :( (It should appear as: マリウス.com )

There's always someone making this claim when negative comments about AWS come up.

They almost always come from people that don't have experience running substantive infra at scale without AWS, so they can't make an informed comparison. The complexity of doing so, for a lot of infra, turns out to be lower than using AWS. Also, you end up with transferable skills and a deeper understanding of the foundational protocols and systems. And you save a lot of money, both because you don't have to pay to manage that complexity, and the systems themselves are cheaper.

No, you are deeply misunderstanding the issue. Creating a rivalrous good that powers fight over for control, then use violence to maintain control of, creating a global feudalism, is not "existing oppression getting worse". It actually makes the risks of every human everywhere dying far higher, and even if that doesn't happen, decreases global utility by a similar percentage (99%, instead of 100%). It could actually be worse, if average human utility becomes negative.

Yeah this has always been the glaring blind spot for most of the "AI Safety" community; and most of the proposals for "improving" AI safety actually make these risks far worse and far more likely.

Nearly all my coding for the last decade or so has used literate programming. I built nbdev, which has let me write, document, and test my software using notebooks. Over the last couple of years we integrated LLMs with notebooks and nbdev to create Solveit, which everyone at our company uses for nearly all our work (even our lawyers, HR, etc).

It turns out literate programming is useful for a lot more than just programming!

This is exactly the thinking that has characterized responses to new sources of power through history, and has been consistently used to excuse hoarding of that power. In the end, enlightenment thinking has largely won out in the western world, and society has prospered as a result.

Centralizing power is dangerous and leads to power struggles and instability.

In the alternative, asymmetry is guaranteed.

When you only allow gov and big tech access to powerful AI, you create a much more dangerous and unstable world.

Yes there is. Lots of researchers are more interested in making a contribution to societal flourishing than in making incredible sums of money. That’s why there’s still lots of top AI researchers in academia.

llms.txt files have nothing to do with crawlers or big LLM companies. They are for individual client agents to use. I have my clients set up to always use them when they’re available, and since I did that they’ve been way faster and more token efficient when using sites that have llms.txt files.

So I can absolutely assure you that LLM clients are reading them, because I use that myself every day.

I don’t know how to trust the author if stuff like this is wrong.

She's not wrong.

A good way to do this calculation is with the log-ratio, a centered measure of proportional difference. It's symmetric, and widely used in economics and statistics for exactly this reason. I.e:

ln⁡(1.2/0.81) = ln⁡(1.2)-ln⁡(0.81) ≈ 0.393

That's nearly 40%, as the post says.

No, your job is to help your reader get to the end of the text. That means writing in a way that most of your audience finds compelling, readable, and not intimidating.

Not all readers are the same, so you will fail at your job for some readers.

But few readers are emotionless automatons that need nothing but dry technical content, unless it’s a topic they are very motivated to understand.

This is not due to slowness of the file system. Native ntfs tools are much faster than Unix ones in some situations. The issue is that running Unix software on windows will naturally have a performance impact. You see the same thing in reverse using Wine on Linux. Windows uses a different design for IO so requires software to be written with that design in mind.

Obviously what matters is how much of the world’s products they produce - especially products that require high energy input. I can’t imagine why you think per capita is the appropriate statistic to compare.

This has already been pointed out to you in this discussion, so it seems you are not actually engaging with the information you’re being provided with for some reason.