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marcammann

I started using the Sandbox CRD: https://github.com/kubernetes-sigs/agent-sandbox

And if I really need to kick off a box very quickly, OpenRouter Spawn seems like it'll do 95% of what I need it to do: https://github.com/OpenRouterLabs/spawn

Still missing better restrictions though.

The education space definitely relies on Google (was also big during the pandemic) and the tools are decent.

But I was definitely surprised about the mentions of Chromecast and Youtube TV, particularly YouTube TV. All of the cord cutters I talked to opted for Fubo, Hulu or DirectTV. Good to hear it sees adoption. Is there a marketshare analysis that you're aware of?

Re:React Native Open Source Update on Saturday March 02, 2019 @06:11PM (#19291709)
I don't have any recent numbers, but high(er) end clients generally go iOS first due to their own market expectations and distribution.
Re:React Native Open Source Update on Friday March 01, 2019 @09:02PM (#19287026)
To that extent, I also found that Android is usually treated as a 2nd class citizen. Which is understandable, given the marketshare, but frustrating nonetheless. It often sounds like something is easily do-able (like Accessibility) ... IF you're on iOS. We recently developed an app for a client of ours for Android, replicating their native iOS app in React Native. That was not a good experience.
We've been recommending Contentful and DatoCMS (https://www.datocms.com/) to our customers. Most pick Contentful due to the perception of being enterprise (Replacing AEM, Sitecore etc.). DatoCMS seems to enjoy a lot of warm reception by people who are coming from Squarespace/Wordpress/Drupal and want something headless but still geared towards Web.
Funny enough, I've been using "write a blog based on Gists on Github for a certain user" as a take-home test for the past 3 years when hiring JS developers.

Now I have to watch out for copy cats..

Re:Pull request acceptance of women versus men on Wednesday February 10, 2016 @02:46PM (#11075434)
Well, they used two steps. First they identified a sample set of contributors that have self identified, thus validated to some extent, their gender.

Further down they then distinguish between contributors where the gender can be inferred from looking at their name & profile picture. Splitting the group of those 35% which were identified via Google+ into two separate groups - identifiable vs. non-identifiable.