For quickly skimming the list, I think it would be useful to add a short, standardized summary for each project. These summaries should be easy to generate automatically with any LLM of your choice.
I have a similar tool for organizing the “Who Is Hiring” threads [1] that uses GPT to provide a quick overview. It’s still running on an old model version from 2023, yet it has been working surprisingly well for over two years now with basically zero maintenance.
As a kind of "AI first" take on this, I created a small Jupyter notebook that uses the Nthesis api which shapes, tags, etc. based on LLM instructions. It also has built in RAG which makes it possible to chat with the documents and has some interesting UMAP based visualizations.
I built a site that transforms the monthly Who is hiring posts into a structured table [1]. It also allows for semantic sorting to find similar jobs to a given one. It originally used GPT-3, which already worked quite well.
I think that a huge part of Tailwind‘s success is not so much the underlying concept but a combination of several aspects: You get a kind of generic design system (margins, paddings, matching colors, etc.) that are restrictive enough to help make things look good and consistent but flexible enough to not result in a every-site-looks-the-same situation (at least not to the extent many other CSS frameworks did); Tailwind sits at a low level of abstraction above raw CSS that is just enough to make some CSS concepts a little bit easier to use (flex, grid, etc.); the documentation is quite good and getting started is relatively easy; …
Given these (and probably some more) aspects, Tailwind makes it easy to get some good results quickly which helps to convince users that their whole approach is the right way to go. Add very good marketing and early traction and you are where we are now.
So I agree with the article that Tailwind‘s conceptual approach is not the holy grail it‘s often said to be, but it maybe also is not even the reason for its success compared to many auxiliary things it did/does right.
Sample size one, but I actually found that the iOS ecosystem works surprisingly well for having a finished app. I have one, created over six years ago with almost no updates over the past years. I intentionally built it in a way that does not require any kind of maintenance: It works offline, i.e., does not require a backend; it's a one-time payment through the App Store (no subscription -> no expectation of continuous updates); the free and paid versions only differ in how many items you can put in the database, i.e., everyone can try the full feature set before buying -> reduces complaints / refunds; etc.
While these are deliberate design decisions, a nice benefit of the iOS ecosystem is that the app has kept working on every new iOS version without me updating anything in the code. Things like installation flow, payment processing, etc. are definitely components that would fall apart a lot sooner when not offered by the platform itself.
Sure, platform lock-in and all that stuff, but so far the app just keeps generating happy users every month (and some pocket money).
Even better, I have a hacked-together-in-a-weekend-for-a-class app for windows on the Microsoft store that was uploaded using a school account I can no longer access eight years ago.
I have received literally daily warnings that the app will be delisted if I don't fix a laundry list of issues including security vulnerabilities, outdated libraries, new EULAs, Icons in different sizes, etc.
Despite all of that the app is still there and able to be downloaded (just checked).
Just to present a balanced view for our international audience: a significant number of locals sincerely enjoy Oktoberfest. Within my circles in the Munich tech/startup scene, a visit to Oktoberfest with the team is often considered one of the year’s highlights in terms of team building and socializing. People attend with friends, family, etc. The infamous images of inebriated tourists merely represent the most attention-grabbing outliers, overshadowing the majority who simply have a splendid time there.
Munich does have its share of issues, rent being a notable example. However, most of these problems are likely a result of the general lack of serious troubles, relatively speaking, which in turn boosts the city's attractiveness.
Indirectly, in a way. A few months ago, I built a small site [1] that parses and structures the "Who is hiring" posts using GPT. It was/is just a personal project to deepen my actual experience in building things with LLMs [2]. After the launch [3], I was actually contacted a few times for LLM-related projects. I am not looking for a full-time job at the moment, but it led to a small consulting/contracting engagement.
PS: The site is still up. Some people think it's a nice alternative interface for quickly skimming the "Who is hiring" posts.
For many use cases like summarization or information extraction, you can get deterministic and mostly non-creative results by adjusting the parameters (temperature, top-p, etc.). This is only possible via the API, though. And it work's most reliably when providing the whole input which should be worked on ("open book" as another commenter called it). I run a task like this for Hacker Jobs [1] and am quite happy with the results so far (there is also an article detailing how it works [2]). If you ask for facts that you hope are somehow remembered by the model itself, it is a different story.
Yes, sorry, you're right of course. I wanted to say that you need to use the more developer-oriented tooling (API, Playground) if you want to have the parameter options.
Thank you very much! I will definitely try out your suggestions! However, at least with GPT-3.5 and the amount of data I have to deal with in this case, my main concern is the quality of the extractions. With about 500 posts per month, the cost is manageable. But for larger datasets, saving tokens is definitely important.
For quickly skimming the list, I think it would be useful to add a short, standardized summary for each project. These summaries should be easy to generate automatically with any LLM of your choice.
I have a similar tool for organizing the “Who Is Hiring” threads [1] that uses GPT to provide a quick overview. It’s still running on an old model version from 2023, yet it has been working surprisingly well for over two years now with basically zero maintenance.
[1] https://www.hacker-jobs.com/