Started a new job today and got a mac as a dev machine. I won't do technical onboarding until later in the week, so I haven't seen what the dev tools are like, but today I was driving myself crazy just trying to do basic things like copy, paste, screenshot, change windows.

At the last job, we had ubuntu machines, so I was able to use gnome extensions to mostly replicate the same general layout, menus, and shortcut keys as Windows. Primarily, this allowed me to keep the same "muscle memory". Since the ubuntu gnome desktop is nothing special from a UX point of view, there didn't seem to be a downside. But I understand that the Mac experience is very curated, so I'm thinking I should lean into learning it.

So my questions are: what are your mac pro tips and things that speed up your work? And for others who have made this transition, what did you learn to do the "mac way" and what did you tweak?

I have a computer that is not quite powerful enough to run my flight simulators, but which is still quite capable. I tried to sell it for close to what I bought it for, after using it maybe 50 hours, but the stink of "used" was on it, so I only got low ball offers for the system as a whole. Selling the individual components would be better but take substantially more effort. Instead, after finding an absurdly good 64 GB RAM deal ($150 for DDR4, in early December, crazy), I decided to use it to educate myself on some work-adjacent science simulation capabilities, putting it at home to avoid the feeling like I'm doing work (and also so I can install nonsense on it if I want).

I settled on Pop! OS, after finding out it has the best NVIDIA GPU support of the .deb Linux family, and installed 22.04 on it last month. After a standard "oops I messed something up on a new-to-me Linux distro, might as well wipe it," I reset the bios to see if it fixed things, then loaded 24.04 on a live USB and ran the update at POST.

24.04 made some very big changes to Pop! OS, which I won't list, other than one that puzzles me. After installing, I ran Geekbench 6 to benchmark it, and I found out my system CPU performance was about 33% down from the prior benchmark. I rationalized this as being due to no XMP being on, and tried to enter BIOS on boot...but Pop24 refused to enter BIOS, and my motherboard didn't even POST? But it would load into Pop24 without issue? So I was stuck without a way to tune my system. I eventually removed the SSD, hard wiped it on a separate device, and reinstalled Pop22, whereafter I was able to enter BIOS and enable XMP. Performance was restored, and even better than ever.

My question...why is Pop24 different? I tried to disable fastboot. I tried to have it use systemctl to reboot into settings. I tried everything I could find online. The best guess I have is something to do with UEFI? But I have no clue. I'm not really a computer guy, I just futz around, and I don't know what I'm doing.

Just a passing thought, came up in conversation. I'm not talking about warehouses-full, nor even "retirement savings" quantities, but like, all the RAM you and your friends and family could possibly need for the next 3-4 years.

Pros, cons? Too late? Too volatile? Too ___?

I am really not sure if this topic belongs in ~tech or ~society or ~talk but I trust the moderators to re-assign accordingly.

So, this is the layout of the "development" team of my companies.

there are 4 "development" teams which reports to the development manager who also occasionally codes.
There is one team, that's the one I am on. 7 people, 6 males.
there is another team, 4 people, 3 males.
there is another team, 5 people, 4 males.
The last team, I don't really consider "development" team. its a team of 4 females. What they are best suited for is QA in the sense of manually testing the product to ensure the experience is sufficient for push to PROD, But because of budget restrictions, they are being forced to learn code and testing suites so they can be the people to develop our testing structure. They are great people and excellent Manual QAers but they really are not developers.

All our tech managers and team leads are men with the exception of the team lead for QA (obviously).

And just to be clear, the culture is friendly and respectful and no complaints. It's just the gender ratio is pathetic.

So our tech gender ratio is really 17 people and 3 women which is 17%.
If you want to consider the QA team a dev team to bump up the numbers, you get 21 with 7, that's still only 33%.

At a recent company meeting, they were talking about how diverse our workforce is and blah blah blah (I tune out most of that stuff as we are fully remote and I spend most of my time coding), but then they showed a slide that claimed our gender ratio for tech roles was like 50% or something.....

I message a colleague at work, being like "where on earth did they get that number??", he was like ":shrug: maybe they are counting the people who use the product we are making?"

To clarify that, the product we work on is rarely used by external customers. Instead we have employees who know how to use our product and correspond on our behalf with external customers. So all these employees are doing is using a webapp the real tech employees develop.

So long story short, my company pulled a number out of nowhere to claim we have gender equity in the tech roles and now I dont know how to trust any stats a company puts out about how equal the gender roles are in their "tech" departments.

Looking to get a tablet for my birthday but I'm so disconnected I don't know what specs to look for, where to get one or a decent price range to expect. I do need something on the cheaper side, but am ok with something good if refurbished. Only ever had a tablet once and it was a "free*" one from Verizon over a decade ago. I'm also open to other device suggestions.

Wants:

1. Not an iPad
2. To be able to use it with an attachable keyboard as a light laptop replacement for the couch.
3. To be able to use it to play mobile games similarly while on the couch.
4. To set up in the kitchen when cooking with recipes or a video.
5. To work for playing/running D&D or Pathfinder (Foundry VTT is the biggest memory user.

I am wanting to be able to disconnect from my phone and all the work apps and social media and such while still playing farmrpg on a lazy night watching a panel show on TV.

Or watch something on the tablet while knitting or something.

Finally embarking on a side-project that I will be doing with Django.

One thing that I am having to consider is how to do encryption.

Looking at the explanations of different levels of encryption here, I think data at rest is really all I need to do (although, I will probably use cloudflare tunnels which will also ensure data in transit but I just won't be implementing it myself is all).

Now, doing data at rest, doing some research, django-cryptography comes up a lot but that hasn't been updated in forever, to point where an open issue on its repo points to a new library (django-cryptograph-5) that was made specifically cause the devs of django-cryptography seem to have abandoned it, but that same thing could happen to the new off-shoot.

I can't tell if this means that I am looking on the wrong webpages for knowledge of how to do about this or when working in the python open-source ecosystem, there's no list of trustworthy reliable publishers of a library for data at rest encryption? like how Django REST Framework is so established, they even have sponsors now.

The last couple of years have been a wild ride. The biggest parts of the conversation around AI for most of that time have been dominated by absurd levels of hype. To go along with the cringe levels of hype, a lot of people have felt the pain of dealing with the results of rushed and forced AI implementation.

As a result the pushback against AI is loud and passionate. A lot of people are pissed, for good reasons.

Because of that it would be understandable for people casually watching from a distance to get the impression that AI is mostly an investor fueled shitshow with very little real value.

The first part of the sentiment is true, it's definitely a shitshow. Big companies are FOMOing hard, everyone is shoehorning AI into everything they can in hopes of capturing some of that hype money. It feels like crypto, or Web 3.0. The result is a mess and we're nowhere near peak mess yet.

Meanwhile in software engineering the conversation is extremely polarized. There is a large, but shrinking, contingent of people who are absolutely sure that AI is something like a scam. It only looks like a valid tool and in reality it creates more problems than it solves. And until recently that was largely true. The reason that contingent is shrinking, though, is that the latest generation of SOTA models are an undeniable step change. Every day countless developers try using AI for something that it's actually good at and they have the, as yet nameless but novel, realization that "holy shit this changes everything". It's just like every other revolutionary tech tool, you have to know how to use it, and when not to use it.

The reason I bring up software engineering is that code is deterministic. You can objectively measure the results. The incredible language fluency of LLMs can't gloss over code issues. It either identified the bug or it didn't. It either wrote a thorough, valid test or it didn't. It's either good code or it isn't. And here's the thing: It is. Not automatically, or in all cases, and definitely not without careful management and scaffolding. But used well it is undeniably a game changing tool.

But it's not just game changing in software. As in software if it's used badly, or for the wrong things, it's more trouble than it's worth. But used well it's remarkable. I'll give you an example:

A friend was recently using AI to help create the necessary documents for a state government certification process for his business. If you've ever worked with government you've already imagined the mountain of forms, policies and other documentation that were required. I got involved because he ran into some issues getting the AI to deliver.

Going through his session the thing that blew my mind was how little prompting it took to get most of the way there. He essentially said "I need help with X application process for X certification" and then he pasted in a block of relevant requirements from the state. The LLM agent then immediately knew what to do, which documents would be required and which regulations were relevant. It then proceeded to run him through a short Q and A to get the necessary specifics for his business and then it just did it. The entire stack of required documentation was done in under an hour versus the days it would have taken him to do it himself. It didn't require detailed instructions or .md files or MCP servers or artifacts, it just did it.

And he's familiar with this process, he has the expertise to look at the resulting documents and say "yeah this is exactly what the state is looking for". It's not surprising that the model had a lot of government documentation in its training data, it shouldn't even really be mind blowing at this point how effective it was, but it blew my mind anyway. Probably because not having to deal with boring, repetitive paperwork is a miraculous thing from my perspective.

This kind of win is now available in a lot of areas of work and business. It's not hype, it's objectively verifiable utility.

This is not to say that it's not still a mess. I could write an overly long essay on the dangers of AI in software, business and to society at large. We thought social media was bad, that the digital revolution happened too fast for society to adapt... AI is a whole new category of problematic. One that's happening far faster than anything else has. There's no precedent.

But my public service message is this: Don't let the passionate hatred of AI give you the wrong idea: There is real value there. I don't mean this is a FOMO way, you don't have to "use AI or get left behind". The truth is that 6 months from now the combination of new generations of models and improved tooling, scaffolding and workflows will likely make the current iteration of AI look quaint by comparison. There's no rush to figure out a technology that's advancing and changing this quickly because most of what you learn right now will be about solving problems that will be solved by default in the near future.

That being said, AI is the biggest technological leap since the beginning of the public, consumer facing, internet. And I was there for that. Like the internet it will prove to be both good and bad, corporate consolidation will make the bad worse. And, like the internet, the people who are saying it's not revolutionary are going to look silly in the context of history.

I say this from the perspective of someone who has spent the past year casually (and in recent months intensively) learning how to use AI in practical ways, with quantifiable results, both in my own projects and to help other people solve problems in various domains. If I were to distill my career into one concept, it would be: solving problems. So I feel like I'm in a position to speak about problem solving technology with expertise. If you have a use for LLM powered AI, you'll be surprised how useful it is.