Connecting to Salesforce’s REST endpoint is fairly straightforward from a Ruby on Rails application. However, there are few steps you have to take. Before starting, ensure that you have full administrator privileges

WireGuard is a security-focused virtual private network (VPN) known for its simplicity and ease of use. It uses proven cryptography protocols and algorithms to protect data. Originally developed for the Linux kernel, it is now deployable on Windows, macOS, BSD, iOS, and Android.

A color palette is a combination of colors used by UI designers when designing an interface. Color palettes form the visual foundation of your brand, help to maintain consistency, and make your user interface aesthetically pleasing and enjoyable to use.

When we started using Clickhouse, the concepts of MergeTree and SummingMergeTree was very confusing. Not only should one understand what each mean, it is extremely crucial to know when to use what as using a wrong one for the other would either result in Resolution/Data loss or would incur huge amount of storage space.

Materialized Views are not something very new if you are familiar with Database views and have worked with Time series/Analytical database. However, when used in Clickhouse, they are blazing fast. So fast, that not many databases in the similar domain can even come closer to it.

GCP is right up there in the top three race to cloud infrastructure only behind Amazon Web Services and Microsoft Azure. It has almost everything you need from classical VM instances, Serverless, Hosted Kubernetes, Docker container registries, ML, Vision - almost everything!

We had an interesting requirement to authenticate IoT devices by our cloud service for managing those devices, provisioning them, rotate keys, etc.

With the development community leaning a lot towards front-end, and framework like Rails mostly moving to mostly serve back-end, it is important to keep this separation clean and straightforward in a Production environment as well.

We have seen how to build a custom object detector using Faster RCNN with PyTorch. That model performed pretty decently with the IOU of 71%, this can further be improved with little tweaks in training and inference.

With so much advancement in technology and computing power, we are at ease in dealing with many of the supervised learning problems. This learning method requires tons of labeled data and we train our model on this data over and over to learn the pattern. This is effective and extremely performant. But we won’t be having the luxury of labeled data always. In many real-time cases, we will always have unlabeled data and need lots of time and effort to label this manually.

If you are familiar with object detection then it’s quite certain that you know a bit about YOLO. It’s an efficient and faster object detection algorithm and the first choice for real-time object detection tasks.

Object Detection is always a hot topic in computer vision and is applied in many areas such as security, surveillance, autonomous vehicle systems, and machine inspection. Widely used object detector algorithms are either region-based detection algorithms (Faster R-CNN, R-FCN, FPN) or single-shot detection algorithms(SSD and YOLO).