PixLab is a Machine Learning SaaS platform that offers Computer Vision and Media Processing APIs either via a straightforward HTTP RESTful API or offline SDK via the SOD Embedded CV library. - Source: dev.to / 10 months ago

In this tutorial, we are building a Next.js application - PicFil, that filters image uploads with PixLab's NSFW API. Users can upload an image or enter a link to the image they want to check. - Source: dev.to / 11 months ago

When asked to select a Facial Recognition Engine for the application, choose PixLab Insight. The engine will be used for mapping each enrolled user’s face in real-time into a mathematical feature vector, known as a biometric hash, which is in turn stored in a sand-boxed binary index. This is used to protect the data of users when they are authenticated with FACEIO. - Source: dev.to / about 1 year ago

In this blog post, I will show you a live demo and how you can implement this in your Nextjs/React app using PixLab's powerful computer vision APIs. - Source: dev.to / over 1 year ago

PixLab (https://pixlab.io) & FACEIO (https://faceio.net) | Full-or-part-time | Remote | Computer Vision / Full stack Engineers | PixLab, a leading provider of Machine Vision, Face Recognition & Media Processing APIs is looking for: * Embedded C & Computer Vision engineer(s) to work on the SOD (https://sod.pixlab.io), embedded computer vision library. * Senior Python engineer with proficiency in PyTorch to work on... - Source: Hacker News / over 1 year ago

In five pages of text, we never get to learn what a Vector is (in this context), why we’d want to store one in pgsql, or why it costs so much to store them compared to anything else you’d store there. For an example of how you can communicate with domain experts, while still giving everyone else some form of clue as to what this hell you’re talking about, check out the link to the product that this thing claims to... - Source: Hacker News / 6 months ago

Pgvecto.rs adopted a design akin to FreshDiskANN, resembling the Log-Structured Merge (LSM) tree concept. This architecture comprises three components: the writing segment, the growing segment, and the sealed segment. New vectors are initially written to the writing segment. A background process then asynchronously transforms them into the immutable growing segment. Subsequently, the growing segment undergoes a... - Source: dev.to / about 1 year ago