mloncode

I am a python developer who has been envious of modern application dev frameworks & typescript, but never had the time to invest in another stack. This is so exciting. I suspect this might be catalyst that empowers more people to ship stuff

Hi I'm Hamel, author of this blog post. A few months ago, I hosted a course on applied-llms that went viral https://maven.com/parlance-labs/fine-tuning and became a conference organically with 30+ speakers.

I thought it would be tragedy if this material were perpetually locked behind a paywall, so I worked hard to curate all the talks and make it freely available to everyone. I hope it is beneficial to everyone.

60. Radiant.AI 61. Weights & Biases (Weave) 62. Quotient AI (some observability there)

> do not seem to be real world business cases

The first one is a real world product that lives in production that is user facing for a paid product.

The second video goes in depth about how a AI assistant was built for a real estate CRM company, also a paid product.

I don’t understand the assertion that it’s not “real world” or not “business”

Here are additional articles about these

https://help.rechat.com/guides/lucy

https://www.prnewswire.com/news-releases/honeycomb-launches-...

Hi, Hamel here. I'm one of the co-authors. I'm an independent consultant and not all clients allow me to talk about their work.

However, I have two that do, which I've discussed in the article. These are two production use cases that I have supported (which again, are explicitly mentioned in the article):

1. https://www.honeycomb.io/blog/introducing-query-assistant

2. https://www.youtube.com/watch?v=B_DMMlDuJB0

Other co-authors have worked on significant bodies of work:

Bryan Bischoff lead the creation of Magic in Hex: https://www.latent.space/p/bryan-bischof

Jason Liu created the most popular OSS libraries for structured data called instructor https://github.com/jxnl/instructor, and works with some of the leading companies in the space like Limitless and Raycast (https://jxnl.co/services/#current-and-past-clients)

Eugene Yan works with LLMs extensively at Amazon and uses that to inform his writing: https://eugeneyan.com/writing/ (However he isn't allowed to share specifics about Amazon)

I believe you might find these worth looking at.

Hi, Hamel here. I'm one of the co-authors. I'm an independent consultant and not all clients allow me to talk about their work.

However, I have two that do, which I've discussed in the article. These are two production use cases that I have supported (which again, are explicitly mentioned in the article):

1. https://www.honeycomb.io/blog/introducing-query-assistant

2. https://www.youtube.com/watch?v=B_DMMlDuJB0

Other co-authors have worked on significant bodies of work:

Bryan Bischoff lead the creation of Magic in Hex: https://www.latent.space/p/bryan-bischof

Jason Liu created the most popular OSS libraries for structured data called instructor https://github.com/jxnl/instructor, and works with some of the leading companies in the space like Limitless and Raycast (https://jxnl.co/services/#current-and-past-clients)

Eugene Yan works with LLMs extensively at Amazon and uses that to inform his writing: https://eugeneyan.com/writing/ (However he isn't allowed to share specifics about Amazon)

I believe you might find these worth looking at.

This is Hamel, one of the authors of the article. We published the article with OReilly here:

Part 1: https://www.oreilly.com/radar/what-we-learned-from-a-year-of... Part 2: https://www.oreilly.com/radar/what-we-learned-from-a-year-of...

We were working on this webpage to collect the entire three part article in one place (the third part isn't published yet). We didn't expect anyone to notice the site! Either way, part 3 should be out in a week or so.

Hello this is Hamel, one of the authors (among the list of other amazing authors). Happy to answer any questions as well as tag any of my colleagues to answer any questions!

(Note: this is only Part 1 of 3 of a series that has already been written and the other 2 parts will be released shortly)

Good article

I looked at my submissions page and it does not allow me to edit the submission title, sadly. It looks like that option goes away after some time?

I emailed the HN mods/help to see if they could correct this. Hopefully they will be able to help.

Hi Jeremy, Hamel here. I submitted this blog post, and definitely did not submit it in this form. One of the defining features of this blog post was the F word.

I'm also curious about what happened

I love Bytewax. Really underrated as far as Python utilities go.

This is cool, thanks for sharing. I’ll definitely check this out

Here is a 15 minute explanation (you can watch it on 2x speed in even less time) https://youtu.be/C8kDPmb_IKU

Excellent comment and question. Re: commercializing nbdev, this path didn't seem to make sense to me personally (for reasons I outlined), but they very well could make sense for others.

But I'm with you, I really like many of the ideas presented in nbdev and feel like they have legs. I don't think those ideas should be abandoned or thrown away. Posit (https://posit.co) is developing products that draw from many of the ideas in nbdev - infact we are working with them - and I think the directions they are pursuing are very promising.

However, if you have ideas on how to make tools in the space that draw on these ideas, I encourage you to do so. I will even support you to the best of my ability if you decide to.

No snark interpreted at all. Yes I am well aware of org mode and love it! I think it is fantastic. There are some key differences that I think make notebooks a bit more interesting - however it sounds like you've made a system you are happy with? I would love to see what you have! Mind sharing it?

I think a notebook is VERY different than an ephemeral REPL. I think that is worth considering when thinking about generating docs, tests and source code from a single source of truth (as opposed to separate files).

Yes, I encourage you to look at some example projects made with nbdev incase that is helpful:

- fastcore: https://github.com/fastai/fastcore - the nbs/ folder contains the source files that generate source code, docs and tests. - ghapi: https://github.com/fastai/ghapi - the notebooks that create source code, docs and tests are located in the root.

Yes Quarto is one of my favorite tools. My blog (the topic of this thread) is made with Quarto!

Yes, nbdev is a system that "compiles" notebooks to python scripts, tests and documentation all from one source of truth. Jupytext cannot do this (nor was it designed to)

That's great to hear!

Good question! Yes we did talk to Anaconda (we talked to Peter Wang), and they seemed interested in related but slightly different approaches. Some of their forays into exploratory programming are pyscript https://pyscript.com/, the also acquired python anywhere https://www.pythonanywhere.com/, which is sort of like Wordpress for Python developers!

We ended up partnering with Posit (formerly RStudio) a bit more on this at this was a bit more natural in terms of alignment of product and vision. You should definitely keep an eye on Posit (https://posit.co/) to see what they come up with in this space.

I don't think there was an opportunity to "be acquired" (not sure if that was what you were asking) because we didn't get far enough down the commercialization path to justify that.

Hello! Hamel here, author of the original post. I'm surprised to see this on the front page of HN! Anyways, feel free to AMA!

You may know fast.ai as a popular deep learning course. There is also a deep learning library with the same name (https://github.com/fastai/fastai) as well as software development tools like nbdev (https://nbdev.fast.ai/).

fast.ai has been offering education and tools for free for over 7 years, and has been approached by many companies asking for help. This program offers an avenue for business to get relevant professional services and support.