htmlbutcher VS bors-ng

In 2006/2007 I built HTMLBucher, a C++ desktop application to slice PSD/image website designs made by designers to HTML.

In these years sites where made with tables, so for slicing a design I had to cut the images and fit then into borderless HTML tables.

I took 2 years to build it, and in this timeframe people started building sematic websites with CSS, and abandoning table-based designs.

I managed to sell 100 copies, for some reason 90% to India.

After some years without selling nothing, I open sourced it: https://github.com/RangelReale/htmlbutcher.

The good thing is that I REALLY learned C++ with this project, and this knowledge was the basis for my current company where I made a digitalsignage

You can further protect main branch by using a rollup approach (see Bors for an example) where the pre-merge tests are run against the combination of those PRs currently proposed for merging, which limits the possibility that two PRs look OK but together break something.

Completely independent implementations of the same underlying concept, one of which was written pretty much just for the rust project, and the other was written as a more general tool and service.

Merge commit (via bors) because I hate squash merges.

Maybe the author does not know that there's https://bors.tech which is a publicly available Bors instance. There's absolutely no operational cost involved except for the initial setup.

Or images. On pages like https://bors.tech and https://foodpartners.us the "bloat" is all images. The images are bigger than they were in the 1990's because they look better, even if you don't think they communicate more "information" people may still want them.

According to the README, it bisects. It doesn't matter much for me, though - the batches in our case were never big.

https://github.com/bors-ng/bors-ng/blob/master/README.md#how...

Sounds like they settled on the bors model of a batching merge queue. Gitlab's merge trains instead speculatively run all of the merges at once which gives more precise data at a higher cost and longer time, if your number of parallel runners is limited.