matylla

I'm excited to introduce intern.work, a dedicated job board and learning hub created solely for internships.

While looking for interns to join my company, I realized there are very, very few places for internships alone so I decided to create on myself. I also noticed that most regular platforms emphasize big-ticket positions, such as "Remote Software Engineers", but overlook a crucial stepping stone for many in their career which are internships.

Why intern.work?

1. By dedicating a platform exclusively for internships, I aim to ensure that both companies and interns find exactly what they're looking for without the noise of other job types. 2. Not just a job board, but a holistic hub (hopefully) that guides prospective interns on making the most of their internships, from preparing for interviews to maximizing their time at the position. 3. Community Driven: A space for interns to share experiences, seek advice, and connect with potential mentors or peers in the industry (coming soon)

I would love to get feedback and feature suggestions.

Visit intern.work for more.

It's a perceptually lossless optimization and recompression.

We use saliency detection (trained on an eye-tracker) which tells us where the human vision system would look in the images and optimise those fragments (heatmaps) using our own comparison metrics.

If you're interested in the details shoot me an email to przemek [at] optidash [dot] ai

A few days ago I've lanunched Optidash - ML-enhanced image optimization and processing API. Optidash builds on top of another product of mine (Pixaven) and runs entirely on Mac Pros (as pioneered by imgix).

While Optidash supports all major image formats for optimization and processing, I am mainly focused on JPEGs. All open-source JPEG optimizers share pretty much the same algo - create N copies of master image at different quality settings and, using various metrics (ssim/dssim/psnr), pick the variant with the "best" quality to size ratio.

Optidash takes a different approach. We use saliency detection to identify the most important area(s) of a master image. That basically tell us how the human eye would see the image and where it would look most likely. Once the saliency heatmap is computed, we crop that fragment and pass it to our Core ML model trained to predict optimal encoding settings. That approach also comes with a performance benefit - only the most salient areas are passed to the model (far less pixel data to process) and it also ensures we don't saturate pretty limited GPU memory we have available on Mac Pros (we use 2nd gen so D700, 6GB VRAM).

Estimating output Q value is one thing but we are also training additional models to help us determine optimal quantization table for a given salient region.

As I am still evaluating the above approach and general API design, I'd love to get some feedback.