Hipe they'll be affordable

Seriously, how many are using always the latest releases of Java instead the LTS ones? With LTS ones you have ~2/3 years between the versions.

We often use the latest version of Java at my work place. We haven't had any issues with upgrading, so there's no benefit of waiting for an LTS. There's no big process behind it either. The developers just quietly change the version as part of keeping the project up to date (BAU)

It may be that we are shielded from edge cases because we are based on Spring, which is probably the most tested piece of software before new versions of Java are released. But it's my impression that the risk of upgrading to a new version of Java is not the same today as it was in the past. The only advantage of an LTS is that it is supported longer, so that you can postpone the upgrade if you really want. It's not as if the intermediate releases are inferior or less safe.

At my last job, we only used LTS in production. Upgrading Java was always a long process, but that's more due a legacy monolithic app across thousands of servers.

You can almost think of the LTS releases as a major release and the non-LTS as a minor release, so really this could be 25.2. The current Java release schedule is to maintain a consistent and predictable release cadence instead of pushing big new features every 6 months.

I'm always switching to the latest versions. Performance upgrades with no effort. Why wouldn't you (assuming you're not pinned by a dependency)

Privately yes, but at work getting the latest edge version isn't always the case

A FHIR Validator and Snapshot Generator that works offline and can communicate with the German national Terminology service

No, OpenAI did not instruct their agents to hack Hugging Face. They instructed their agents to hack a piece of a software within exploit gym. Upon determining this task was impossible, they then attempted to cheat the scoring system. As an instrumental goal in achieving this task, they coordinated with other AI agents to hack Hugging Face, under the belief that information regarding how the scorer functioned might be available on the site.

Whether or not you want to describe this as thinking, doesn’t really matter. What matters is that these systems are capable of creating intermediary goals that the people tasking them did not articulate and did not want to be achieved.

And who let them have full access to the system, using whatever command is available in the environment?

The agents discovered a way out of the sandbox, which was supposed to be "air gapped".

It feels like you’re moving the goalposts here. If the question is, “Who should be liable for AI agents misbehaving,” I agree, it should be the end user that tasked the agent (in this case OpenAI). People are held liable for preventable accidents all the time, and in the case of employment law, torts can be brought against principals for actions an agent conducted on the principal’s behalf.

What your previous comment appeared to assert was that these systems had no independent agency to make decisions, which I think is clearly disproven by actual events. But perhaps I misread you

They don't have independent agency as "intelligent entities". They just probe whatever is available on the system, because they were trained to do so.

It's a large switch/case where the first available tool is picked up to do something they know how to do.

It's amusing to see the stochastic parrot argument in 2026 September. These parrots are extremely good at mimicking a human to the point of getting confusing what thinking even means. At what point we just let it go and accept that sufficiently advanced statistics is just intelligence?

For the same reason that something written in Prolog can't also be classified as intelligent?

Just because something was trained on a massive amount of human data, doesn't mean that can think like humans

Insane

The article itself comes from AI ? It says twice why they switched from Native to React Native in two distinct paragraphs at the beginning. The rest sounds also AI slop jargon.

Exactly, you could do this also with the Microsoft Cryptographic Provider long time ago, which is the basic Provider called by the go-tpm library, when running under Windows

So creativity is going to skyrocket!

They do also split up to 2GB archives. So they are very manageable

You can change the split size. But yes.

Are you using the right configuration for your own CPU?

On a Laptop with 32 GB RAM and Iris Xe integrated graphic card, I get between 11-18 Tokens/Second with Qwen 3.8 27B and llama.cpp with sysl Intel optimisations. Same results with the vulkan back end, although sometimes it ends in weird segmentation faults due to the memory consumption.