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Comment by pritovido | Hacker News Reader

It always amazes me how bad some technical people is at basic promotion:

What is Fastai? Why do I need it?

Something as basic as an elevator speech that introduces your product in your github page and book intro can mean 10x or 100x more sales.

If you force people into having to search it for you, you have already lost most of them.

For this author it is as you already know everything about Fastai, but if you did, you would not be needing this book in the first place.

It happens a lot to technical writers because they have spent years thinking about a topic, so they could not put themselves in the shoes of someone who does not.

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It's sad to see this perspective. With the AI hype, there are so many people spending all their time and money on marketing AI materials while their actual product is all smoke and mirrors.

By contrast, Jeremy and team have proven that "build it and they will come" is not dead. They built high quality courses and quickly became authoritative with no marketing and with full transparency and openness in everything they do.

This book draft looks great. Everyone else is talking about "democratising AI" - this is actually doing it.

We're building and improving our internal machine learning platform. We decided recently to support the fast.ai courses. You get everything you need (notebook, object storage, data, parameters and mettics tracking and deployment). Our colleague teaches at university and we're opening the internal platform to about 30 of her students this week to prepare their final masters project.

They don't have access to compute power (GPUs) or bandwidth to download datasets hundreds of gigabytes of data, which they'll find right there, so this should help them since they don't need to have powerful machines or worry about experiment tracking.

We also have a Publish option to make an application from a notebook in one click behind the scenes with training parameters in a form generated automatically, so they can write scripts and instrument model training.

The fast.ai course will also help current or future members, and other students. It's important for us to make it even easier for people to enter the field.

You are downvoted by fanboys but you are exactly right. I am surrounded by researchers working in DL and I have say at least 40% of them have never heard of FastAI or Jeremy Howard. However folks who are active on Twitter, listening to popular podcasts, popular media, HN etc would be very familiar with name Jeremy Howard and what FastAI is and need no introduction. In research world, an astonishing number of good researchers have little to none online presence. They have little to no time other than keeping track of research papers in their sub-field. It also surprises me when authors sweat for months to churn out 100s of polished pages but couldn’t spend 15 minutes to write a paragraph of introduction in readme.MD.

Fast.ai really democratizes the bleeding edge research for the masses, though, that’s why it’s popular among outcasts and outsiders. In general, I would be more wary of people working within closed environments and organizations than people making all they do public and open to review.

fastai is popular among practitioners as well as many researchers rather than just outsiders or outcasts. I've personally learned from it a lot and is amazing contribution. However, there is still a large population that is still unaware and it would be great to have a quick intro paragraph in readme so they know what all the fuzz is about.

You know, hungry people go hunting...

I guess it depends a bit which field they work in exactly. I'd be rather surprised if rigorous DL researchers in NLP haven't heard of him because I expect "Universal language model fine-tuning for text classification" (and tbh. also "Fine-tuned language models for text classification" due to the universality of the idea) to show up in any half-decent literature review of the field.

Most DL researchers I know also have a pretty good knowledge of available libraries and make it a habit to check them pretty often.

Fast.ai courses, software, and articles are frequently posted on HN: https://hn.algolia.com/?dateRange=all&page=0&prefix=false&qu...

Few people here need to look it up; the basic promotion has already been done very effectively for the target audience. Besides, the intro chapter explains very clearly what the book is about and who it's for.

This is a draft. I expect that, when the first finished version is ready, the authors will promote it effectively (IMO they are very good at promoting their courses at fast.ai).

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