Doubleslash

The segmentation into attack vectors is interesting. But images from individuals on something like DockerHub have always been untrusted. I expected to read here about recent compromised official DockerHub library images.

I don't see how that threat is new or now more pressing than ever. How would you even count something like `docker pull vesnpsexga/joomla` as typosquatting vs. `docker pull joomla`? It's not even close.

Users should limit there container runtime/podman/docker access to docker.io/library or use a pull-thru caching feature of their own registry to bring in stuff from hand-selected places in public registries like docker.io or quay.io to environments behind the firewall.

Looks like they updated a bunch of their landing pages. Their UI for paying users to browser buckets and computer backups hasn't changed at all. So what?

I feel the same. Google simply has no patience. Though they need that because they simply have no track-record in that industry. It's the same with Google Cloud. They have that huge global infrastructure essentially running their own internet, just with 4 times the bandwidth. They have piles of cash and it looks like they are running out of ideas of what to do with it. In 2018 they announced their 5 year plan to overtake Microsoft and possibly AWS in marketshare for public cloud computing. If they don't achieve that they said they'd simply stop putting money in this business and leave "partners" to fill the gap. To me that reads they'll effectively stop expanding GCP capabilities which would mean the end to that service. It's two years later and while GCP is certainly growing, so is the rest and their marketshare is still miniscule.

I bet the same has been said about VMware, GitHub and LinkedIn. They are all still doing pretty well today, last I checked. And they weren't acquisitions that required the acquiring company to get investors in to close the deal.

German citizen here. I travel to the US quite often - I find that the quality of the Grocery stores is on average one level up. The quality of the goods sold there is another story. But mid-and southern Europe likes it cheap when it comes to these things. There is a reason why we call them "discounters".

I really do hear you on the housing quality - it's ridiculous what people are willing to put up with over there. We have friends living in the southern bay area who pay 3.5k for a 2.5 bedroom apartment in an apartment complex - the whole thing calls itself "Luxury Residencies". Ain't no luxury there. Like you describe everything is built extra cheap: from the walls, to the kitchen appliances and the plumbing. There is a massive top-loader in their supply closet that sounds like it runs on it's own ICE, built in the 50s. In Europe this would be considered a very basic apartment but it's already considered 1-2 levels higher than average and not even 3 years old.

I do think there is something to your Galapagos syndrome theory.

X1 is a nice laptop. The problem is it's weak CPU. Apple manages to crunch a whole different tier of CPU (h(q) series vs. U series for Lenovo) in the same form factor.

Safety is not the only reason German wants to walk away from nuclear energy. The long-term, safe storage of nuclear waste that will radiate for decades to come is not something want to leave behind our future generations.

It's not trivial but it's also not an order of magnitude more difficult anymore, as you describe it. There is a reason why Kubernetes gets a lot of backing from corporate customers - precisely because it hides and abstracts most of the underlying infrastructure and provides platform-agnostic primitives that make sense at the application level.

Once you have deployed your stack on Kubernetes, you can pretty much run it on any cloud or infrastructure with minor tweaks at most.

I wonder what some analysis on driver focus, concentration and eye movement would reveal when comparing Tesla, especially Model 3, drivers vs. drivers of cars with physical buttons and blind operations.

Though, there is a middle ground between running vanilla Kubernetes yourself on your own hardware and tying yourself into the specifics of a cloud provider just to get managed Kubernetes. There are really good Enterprise distros like OpenShift or PKS that relieve you from the hassle of running Kubernetes and focus on using it but don't force you onto a particular platform or cloud.

I don't think this is the case - both the breakthrough and your statement about where the car manufacturers are. First, current machine learning is flawed. I provides reasonably accurate predictions for simple to medium complex problems with manageable amount of input variables, but requires a lot of data already at the high end of that. For high confidence and high dimensionality it's already uneconomical to even get the data in amount and quality needed. These models fail ultimately with complex scenarios like day to day driving.

As you can see with Waymo and Uber even the simple grid like traffic systems in the US with wide lanes and lots of space to cross are already a problem. Here we haven't even looked at organically grown traffic systems like in Europe or the sheer craziness they call traffic in countries like India. Effectively none of these are self-driving cars as they all come with a driver who regularly has to intervene as the cars prefer to drive around the block rather than doing a left-turn. These are problems they will not be able to solve even with 100x the mileage and cars they have today - scalability is not the problem here. Or rather it's uneconomical to solve it like that - by the time you gathered the necessary amount of training data the traffic systems will likely have changed significantly.

The underlying problem is that machine learning is impossible to debug and deep learning is based on the understanding of the human brain from the early 60s. These fields has advanced significantly since then and we will need both, new algorithms and new hardware, of what will eventually become sophisticated neuromorphic networks that truly mimic the human brain capacity. As it stands today, a human needs only a couple of examples to train, a machine needs millions. This is not only a compute problem but also a sensor problem. Today's sensor don't deliver the data at the necessary fidelity fast enough. We need algorithms that can learn more effectively, not harder.

I wouldn't be surprised if the German car makers are basically where everybody else is, but just waiting until the systems gets to a level where it's economical, truly safe and well accepted to get into mass production. Until then, they are probably fine with customers of Uber / Waymo play guinea pig.

Yes. On average the human brain just needs dozens or hundreds of examples (turns, exercises, you name it) to "learn" something. A decent machine learning model needs hundreds of thousands to millions of samples to gain good confidence and can still be fooled easily afterwards with subtile changes.

"So many proxies, so many things happening in the shadows."

A fairly accurate description of a typical experience of running OpenStack. A behemoth of complexity to manage, requiring an operator to understand a huge amount of layers, ranging from high level concepts like VRRP-based HAproxy setups all the down to kernel network namespaces and low-level tap-devices. All at the same time and with no central control plane to debug but rather crawl multiple log files from multiple systems in parallel.

Back in 2014 OpenStack may have been a thing but nowadays, 4 years later, people learned a lesson. There is no value in being a full-stack operator of infrastructure for systems. The idea of running a complex, distributed architecture like a private cloud at the same or higher stability and comparable or even lower cost than public clouds eventually proved futile.

That's why in 2018, consensus is, infrastructure for systems has been replaced by infrastructure for applications (k8s). The infrastructure is typically consumed on public clouds.