blackjack-basic-strategy VS Tailwind CSS

It’s an easy to use inference server for computer vision models.

The end result is a Docker container that serves a standardized API as a microservice that your application uses to get predictions from computer vision models (though there is also a native Python interface).

It’s backed by a bunch of component pieces:

* a server (so you don’t have to reimplement things like image processing & prediction visualization on every project)

* standardized APIs for computer vision tasks (so switching out the model weights and architecture can be done independently of your application code)

* model architecture implementations (which implement the tensor parsing glue between images & predictions) for supervised models that you've fine-tuned to perform custom tasks

* foundation model implementations (like CLIP & SAM) that tend to chain well with fine-tuned models

* reusable utils to make adding support for new models easier

* a model registry (so your code can be independent from your model weights & you don't have to re-build and re-deploy every time you want to iterate on your model weights)

* data management integrations (so you can collect more images of edge cases to improve your dataset & model the more it sees in the wild)

* ecosystem (there are tens of thousands of fine-tuned models shared by users that you can use off the shelf via Roboflow Universe[1])

Additionally, since it's focused specifically on computer vision, it has specific CV-focused features (like direct camera stream input) and makes some different tradeoffs than other more general ML solutions (namely, optimized for small-fast models that run at the edge & need support for running on many different devices like NVIDIA Jetsons and Raspberry Pis in addition to beefy cloud servers).

[1] https://universe.roboflow.com

* Most of the time I find Roboflow extremely handy, I used it to merge datasets, augmentate, read tutorials and that kind of thing. Basically you just create your dataset with roboflow and focus on other aspects.

For computer vision, there are 100k+ open source classification, object detection, and segmentation datasets available on Roboflow Universe: https://universe.roboflow.com

Solid website and app overall for learning more about computer vision, discovering datasets, and keeping up with advancements in the field: * https://roboflow.com/learn * https://universe.roboflow.com (datasets) | https://blog.roboflow.com/computer-vision-datasets-and-apis/ * https://blog.roboflow.com

If you're lacking training images, you can also use [Roboflow Universe](https://universe.roboflow.com) to obtain them (over 100 million labeled images available)

Roboflow | Multiple Roles | Full-time (Remote) | https://roboflow.com/careers

Roboflow is the fastest way to use computer vision in production. We help developers give their software the sense of sight. Our end-to-end platform[1] provides tooling for image collection, annotation, dataset exploration and curation, training, and deployment.

Over 100k engineers (including engineers from 2/3 Fortune 100 companies) build with Roboflow. And we now host the largest collection[2] of open source computer vision datasets and pre-trained models[3].

We have several openings available, but are primarily looking for strong technical generalists who want to help us democratize computer vision and like to wear many hats and have an outsized impact. (We especially love hiring past and future founders.)

We're hiring 3 full-stack engineers this quarter and we're also looking for an infrastructure engineer with Elasticsearch experience.

[1]: https://docs.roboflow.com

[2]: https://blog.roboflow.com/computer-vision-datasets-and-apis/

[3]: https://universe.roboflow.com

Based on what I do/use when I prepare models: A good framework for creating and improving this dataset faster is to use Roboflow Universe and search “flowers” and “bouquets of flowers” in the search bar (it’s like Google Images for CV Datasets). You can search images by subject, or metadata, and clone them directly into a free public workspace (they house up to 10k images without charge). * https://universe.roboflow.com/ * https://universe.roboflow.com/search?q=flowers * https://universe.roboflow.com/search?q=bouqets

Lots of ideas will come to mind if you look and search through open source datasets: https://universe.roboflow.com/

Open source datasets: https://universe.roboflow.com/ Model training: https://docs.roboflow.com/train Model deployment: https://docs.roboflow.com/inference/hosted-api

Finally, for our front end, we’re going to be pairing Next.js with the great combination of TailwindCSS and shadcn/ui so we can focus on building the functionality of the app and let them handle making it look awesome!

You can use any frontend framework you want — react-based tooling, however, has a natural advantage as it models everything as a function of state, which can map 1:1 with the concept in Burr. In the demo app we use react, react-query, and tailwind, but we’ll be skipping over this largely (it is not central to the purpose of the post).

Tailwind CSS: A utility-first CSS framework for rapidly building custom designs.

First, you need to make sure that you have a working Tailwind CSS project…

Tailwind CSS

With better CSS approaches like TailwindCSS and Vanilla Extract (which we're heavily using) it's much easier to maintain the UI and make sure it doesn't change unexpectedly. No more conflicting CSS classes, much less CSS specificity issues and much less CSS code in general.

This app was built with Svelte Kit, Tailwind CSS, and many other technologies. For a full rundown, please visit the GitHub repository

Unlike Tailwind, which has over 77,000 stars on GitHub, Mojo CSS has about 200 stars on GitHub. But the Mojo CSS documentation is fairly good and you can find most of the information you’ll need there.

JavaScript React Flowbite Tailwind Firebase - Auth, Database, and Hosting Vite

- Performance is a feature.

Another common interpretation of brutalism is aesthetic, reacting to overly complicated user interfaces by creating simpler, more direct ones. Tailwind CSS (https://tailwindcss.com), one of today's most popular CSS libraries, promotes this approach in its component examples. There's also a neat library I've seen recently called "Neobrutalism Components" for React that I like (https://neobrutalism-components.vercel.app), providing components with a similar look and feel to Gumroad. This might more accurately be called 'Neo-Brutalism,' as noted in the comments.

A more engineering-centric interpretation of Brutalism focuses on form, structure, and efficiency, drawing significantly from brutalist architecture principles. Apart from the user interface itself, most mobile, desktop, and web applications are extremely bloated and often perform worse than sites from 10 years ago did. While one HTML file might be "less brutalist" than the original HN site, it is substantially more brutalist than any HN mobile app in existence, and offers nearly identical functionality.

A broader interpretation of brutalism, which could be termed 'Meta-Brutalism,' is embodied in the overall experience on this site through UX flows. Yes, in the strictest sense, the original HN site is more Brutalist in many ways, but it only shows 30 articles at a time and does not function as a PWA. For this site, the experience of reading 10 stories is arguably less brutalist, but for quickly browsing through several pages and skimming articles (which is how I read HN) it is a lot faster, and in my opinion, more Brutalist.

My primary inspiration was addressing software and tool bloat in UIs rather than strictly adhering to every principle set forth by David Bryant Copeland. I don't find it convincing that this site "isn't brutalist" compared to really any other experience apart from the Main HN site, and I would argue the overall experience is more brutalist in its performance and scrolling behavior.

As a side note: I generally don't like Brutalist architecture that much although I believe it is unfairly maligned. I visited the Salk Institute once and enjoyed it though (https://www.archdaily.com/61288/ad-classics-salk-institute-l...).