> Some of these features can be turned off. Others can’t. Or if they can, it means also turning off useful long-standing features like automatic thread categorization.
This, I absolutely hate it. And like the author said, it must be intentional, so that someone at Google can show the usage numbers and get a promotion.
I recently installed an app to manually activate the fans on my MacBook Pro M1 Pro as I've never been able to trigger them over the past 4+ years. Just to check whether the fans even work (they do).
My father is a journalist and learned to type by himself on typewriters using his index fingers. He writes a lot and he is really fast. I don't have any issues with that.
You hit the nail on the head here. Saying and doing are two very different things. It's also especially tempting to find an excuse to use some shiny new thing that everyone is always talking about. Both for personal learning and curiosity but also for future job prospects. The reality is that it's easier to get a k8s job if you have k8s experience.
I totally agree, but that's not what happens in reality: the average devops knows k8s and will slap it onto anything they see (if only so they can put in on their resume). The average manager hears about k8s, gets convinced they need and hires beforementioned devops to build it.
> Making Kubernetes good is inherently impossible, a project in putting (admittedly high quality) lipstick on a pig.
So well put, my good sir, this describes exactly my feelings with k8s. It always starts off all good with just managing a couple of containers to run your web app. Then before you know it, the devops folks have decided that they need to put a gazillion other services and an entire software-defined networking layer on top of it.
After spending a lot of time "optimizing" or "hardening" the cluster, cloud spend has doubled or tripled. Incidents have also doubled or tripled, as has downtime. Debugging effort has doubled or tripled as well.
I ended up saying goodbye to those devops folks, nuking the cluster, booted up a single VM with debian, enabled the firewall and used Kamal to deploy the app with docker. Despite having only a single VM rather than a cluster, things have never been more stable and reliable from an infrastructure point of view. Costs have plummeted as well, it's so much cheaper to run. It's also so much easier and more fun to debug.
And yes, a single VM really is fine, you can get REALLY big VMs which is fine for most business applications like we run. Most business applications only have hundreds to thousands of users. The cloud provider (Google in our case) manages hardware failures. In case we need to upgrade with downtime, we spin up a second VM next to it, provision it, and update the IP address in Cloudflare. Not even any need for a load balancer.
I don't really have the hardware to try it out, but I'm curious to see how Qwen3.5 stacks up against Gemma 4 in a comparison like this. Especially this model that was fine tuned to be good at tool calling that has more than 500k downloads as of this moment:
https://huggingface.co/Jackrong/Qwen3.5-27B-Claude-4.6-Opus-...
> they're almost always people who already had some pull toward software
I think this is probably true, and basically how I got into software myself.
I always dabbled in writing software and things for the web, but for some reason I never thought studying computer science would be any fun and that a career as a software developer sounded boring. But then I got an actual full time office job and oh boy, did my perspective on things change fast.
That first job did not have anything to do with writing software at all. But I saw people struggle with things that seemed to me trivial to automate, such as making annotations on paper bank statements and entering them into the system line-by-line. The bookkeeping system did support electronic bank statements, but lacked features to match certain descriptions to certain cost places. In the end it was indeed faster to go the paper route... It took me a couple of hours to write something that saved hours every week and that basically kick started my software career.
Would AI have made much of a difference here? Yes, in terms of getting to the correct solution faster, but probably not in terms of who would have done that. People would still come to the person who came up with the solution to ask for maintenance and new features.
I use voxtype on my Linux machine with parakeet. Super fast and regularly even gets the tech lingo correct. You can configure prompts and keywords to help with that as well.
Radeon R9700 with 32 GB VRAM is relatively affordable for the amount of RAM and with llama.cpp it runs fast enough for most things. These are workstation cards with blower fans and they are LOUD. Otherwise if you have the money to burn get a 5090 for speeeed and relatively low noise, especially if you limit power usage.
Wouldn't be surprised if they slowly start quantizing their models over time. Makes it easier to scale and reduce operational cost. Also makes a new release have more impact as it will be more notably "better" than what you've been using the past couple of days/weeks.