blackjack-basic-strategy VS n8n

Roboflow Universe. Image source: Roboflow Universe

FWIW you can use roboflow models on-device as well. detect.roboflow.com is just a hosted version of our inference server (if you run the docker somewhere you can swap out that URL for localhost or wherever your self-hosted one is running). Behind the scenes it’s an http interface for our inference[1] Python package which you can run natively if your app is in Python as well.

Pi inference is pretty slow (probably ~1 fps without an accelerator). Usually folks are using CUDA acceleration with a Jetson for these types of projects if they want to run faster locally.

Some benefits are that there are over 100k pre-trained models others have already published to Roboflow Universe[2] you can start from, supports many of the latest SOTA models (with an extensive library[3] of custom training notebooks), and tight integration with the dataset/annotation tools that are at the core of Roboflow for creating custom models, and good support for common downstream tasks via supervision[4].

[1] https://github.com/roboflow/inference

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

[3] https://github.com/roboflow/notebooks

[4] https://github.com/roboflow/supervision

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/

So obviously there is a lot of information in the documentation. We want to focus only on the options used in n8n lefthook.yml file.

Check Release Notes: Review n8n’s release notes on GitHub (https://github.com/n8n-io/n8n/releases) for breaking changes or new features that may affect your workflows.

We walk through how to connect n8n, an open-source automation tool, with Upsun’s Git-based deployment flow. The result: event-driven automation triggered by deploy hooks or webhooks, with zero new backend code to maintain.

Wow, that's just about the dumbest licensing clause I've ever seen in my life:

> Content of branches other than the main branch (i.e. "master") are not licensed

How the fuck do pull requests work in that setup? Or presumably tags aren't licensed?! Holy shit

Anyway, seems to be some rando made up license https://github.com/n8n-io/n8n/blob/master/LICENSE.md#sustain...

To set up such a system, developers can take advantage of tools like n8n – a modular tool to automate workflows, combined with the newest AI tools like GPT-4. Here’s a more detailed explanation.

Hi y'all. Love the idea and congratulations on your launch. I've used [n8n](https://github.com/n8n-io/n8n) for similar use cases in the past. Any differences in Sim Studio that you'd like to call out?

n8n – The workflow automation platform

For those interested in elevating their automation capabilities, n8n offers an array of integrations worth exploring. Visit n8n.io if you're intrigued.