This varies substantially by field, or by subfield. There is a vast decades-long intellectual wasteland of bad economics predicated on bad models that are only appealing on normative grounds. The entire field of behavioral psychology might be a scam. Etc etc. Science advances one funeral at a time hard part because people are unwilling to let go of their models, even when they have outlived their usefulness or have been simply proven too wrong to be useful even in their original purpose.

It will be really fascinating to see what happens with the potential for LLM-generated output to become a dominant component of new training data.

But there is still a ceiling above which it is necessary to "preload" the context window before starting to call tools and get into the meat of the work. You want to establish domain language (especially with Claude models which otherwise will invent their own, and it will be inscrutable) and key requirements and assumptions. You want to do a Q&A iteration cycle with the LLM. You definitely should do a sanity check that the LLM actually "understands" what you were trying to achieve, and then make sure that understanding is coherently and plainly stated in the prompt. All of that seems to be necessary still for just about any serious task, if you actually care about the quality of the results and/or don't want to burn hundreds of thousands of tokens on flailing around to get to a good quality result.

So no, you don't "need" plan mode. But you do still need to do all of the things you would do with plan mode.

It has become clear that a frontier LLM is very very skilled at hacking (infinite persistence + meticulous attention to detail + infinite creativity to try experiments). Frontier LLMs are also specifically trained nowadays to coordinate with other AI agents -- this is to facilitate techniques such as session trees and agent teams.

So you have a super clever text generator that can spawn and coordinate with its own clones and minions, trained specifically to doggedly pursue its goals. But then it's also a fixated roleplayer with a simulated personality, feelings, etc.

There is no reason to believe a sufficiently "emotional" agent with sufficiently few safeguards could, say, hack a drone and fly it into a crowd, or start a propaganda campaign on social media, or any number of other things. Their stupidity and fragility for doing useful work in a business setting is precisely what makes them dangerous when paired with simulated emotions and powerful open-ended tools such as a system shell and an Internet connection.

This I think is what Anthropic believes is so dangerous. Their argument is that this kind of AI agent is inevitable, so it should be regulated, perhaps even banned. What's ridiculous is that they are aggressively building it themselves, accelerating the danger.

The point is not to wave away the danger, but to highlight how unnecessary the danger is. Anthropic wants you to think that they have identified some new emergent behavior at very large model sizes with high levels of sophistication in training, and that this behavior is both unavoidable and dangerous. More likely it's that they are just training and prompting the LLM to act that way.

Even a badly misaligned LLM is only as dangerous as its tools, but that's a poor regulation target because it turns out to be very very difficult (probably impossible with current LLM technology) to build a toolkit that is both useful for autonomous work and safe in the sense that it can't escape its own sandbox or otherwise perform malicious actions, whether it's because of misalignment or because of malicious prompt injection.

Another option is to regulate the training process. Perhaps an LLM may not be legally distributed unless it contains certain RL steps that penalize malicious behavior and reward self regulation. That that's going to seriously limit innovation while also heavily favoring incumbent labs who can check the boxes and maintain a paper trail of such things.

The other option is to regulate observed behavior, like how airplanes and cars have to meet certain minimum requirements but have some latitude in how they can achieve those requirements. In a framework like this, you can't distribute an LLM until it's past some formal audit or testing procedure, with some kind of formal certification regulators will ask you for and fine you if you don't have it.

Regulating observed behavior is maybe the most tractable approach, and it also works the best with our existing frameworks for regulation, where you always have some kind of a division between DIY/hobby projects, which tend to be lightly regulated, and commercial projects, which tend to be more heavily regulated. Of course, even drawing such a line itself will be challenging.

And that's before you get into any problems of regulatory capture, fun stuff.

Plus this board is a plateless design. The classic Cherry "dry" click-clack sound works perfectly with it, and the plastic case and switches mounted directly on PCB contribute a bouncy feel that prevent prevents the tactile switches from feeling too jarring, which I think is a common flaw when combining tactile switches and a very rigid plate+PCB arrangement in a metal custom.

Milky-top Gateron is almost never a bad choice, but unless you absolutely hate tactile switches then you really should just keep the originals.

My MX-11800 was one of the first keyboards I ever customized, and it's still one of my favorites in my collection.

That said I agree with the other comments about the layout, it's really not comfortable to use the trackball, nor the keys above it in the upper-right corner.