Can We Put the 16GB “Pro” Myth to Rest?

The author listed quite a few apps then:<p>&gt; A couple apps you won’t see on this list are Chrome and Slack<p>He explained we should:<p>&gt; in my opinion you should boycott them until the developers learn how to write them to play nicer with memory<p>I stopped reading. Selection bias is selection bias, no matter how you try to talk it out.

This is either clickbait or outright absurdity apologist nonsense.<p>I currently have 19GB of swapfiles sitting in &#x2F;var&#x2F;vm&#x2F; on a maxed out 2015 MBP.<p>The new MacBook Pro is not professional and it is underpowered. Accept it already.

Try running a few docker containers to support development work. A few DBs, messaging servers, Lucene indices, etc. Try prototyping applications using map-reduce for huge datasets.<p>&quot;Pro&quot; doesn&#x27;t always refer to the artistic line of professionals. Many developers see this as a huge problem, and there are a lot of us.<p>Our options now include the 2005-era looking systems at system76.com, running Linux. I love Linux for servers, but I really don&#x27;t like the GUIs, or acting as my own systems integrator. I want a true modern &quot;Pro&quot; macOS system, and that doesn&#x27;t exist right now.

Let&#x27;s just ignore very popular apps I don&#x27;t like that happen to use a lot of memory so I can make a point.

Not saying we have to, and the memory constraint might eventually make it infeasible, but my team&#x27;s developer process is a lot easier when we can run our stack (12 web services, db, message bus, reporting service) at once.<p>There&#x27;s definitely some optimization opportunities to address, but since the only immediate bottleneck that&#x27;s causing is in local dev, our resources are better spent buying beefier dev rigs and focusing on delivering value.

My machines are currently 16GB each (I have two MBPs). I imagine increasing the amount of RAM I use in my next machine upgrade.<p>Anyone working on large scale data problems or doing advanced analytics (Spark, R, Python, etc.) would appreciate having more memory to allocate to their problems.<p>The analysis in this blog underestimates the complexity of various compute environments.

Mine: 14.6GB used, 18GB swap used.<p>Twitter 2.99GB (old, ad-free client) Java 1.22GB Photoshop 1GB Transmit, Spotify, Sublime, etc - 700MB each. Chrome core 2.8GB and then <i>79x</i> (!) Google Chrome Helper, each anything up to 2GB. Obviously not listing the loads of things in the 200-700MB range.<p>Currently have Firefox, Safari and VirtualBox closed, though they are often open for testing.

similar point here: <a href="https:&#x2F;&#x2F;blog.rinatussenov.com&#x2F;leave-apple-alone-538d7619ce9e#.2pcdg1po1" rel="nofollow">https:&#x2F;&#x2F;blog.rinatussenov.com&#x2F;leave-apple-alone-538d7619ce9e...</a>

I tend to use AWS Windows or Linux machines for work and use the Mac basically for Web browsing, RDP client and occasional Microsoft Office. This has removed my need for a desktop or notebook with more 16MB, effectively lenghting the useful life of my hardware. If I feel I need more RAM (up to 2TB), I just shutdown the AWS instance, change the instance type and start it again. Actually, with this setup, I very rarely have the urge to use my laptop, I have a desktop in the 3 places I usually work from so I&#x27;m freed to drag a laptop with me unless I travel.

None of the memory consumptions are realistic, I use most of the listed apps. E.g. Xcode using 300 MB? Do you have one file open?

Disk cache buffers. It&#x27;s what&#x27;s left after all your applications are resident, and often impacts IO performance.