But yes, ^this. We are building around a shareable AST precisely because structure really matters.

(I'm a core developer from Jupyter Book, the team that builds the new MyST stack — see https://jupyterbook.org/stable/community/history/ and https://mystmd.org/guide/background)!

Some people are vegan, some people eat meat. Usually, these two parties get on best when they can at least understand each-other's perspectives and demonstrate an understanding of the kinds of concerns the other might have.

When talking to people about AI, I feel much more comfortable when people acknowledge the concerns, even if they're still using AI in their day-to-day.

As a project, the tooling to parse MyST Markdown was built on top of Sphinx, which primarily expects ReST as input. Now, I would not be surprised if most _new_ Sphinx users are using MyST Markdown (but I have no data there!)

Subsequently, the Jupyter Book project that built those tools has pivoted to building a new document engine that's better focused on the use-cases of our audience and leaning into modern tooling.

(I'm a MyST contributor)

- It's hard to compare Omicron vs delta because of the number of confounding variables - population heterogeneity, vaccine + infection induced immunity, etc. - Severe strains with latency periods are invulnerable to symptom recognition. I don't think the asymptomatic period for the COVID variants varied as much in the lower bound as it did the upper bound. The point being -- behavioural changes are much more likely to be general caution (i.e. limiting contacts, spacing social events in time, etc.) than responsive (I feel unwell).

It's a document engine that ingests Markdown (particularly the MyST superset) and builds upon "structured data" for sharing.

E.g. SciPy's proceedings: https://proceedings.scipy.org/articles/XHDR4700

Suffice to say, it's worth considering whether the cost of a decision can be interpreted solely as how much money there is vs the wider ecosystem level consequences of said decision.

I am not an expert on this, at all... but I'm not sure that's the case. c.f. Wikipedia:

> In March 2024, Federal Audit Office published a report in which it assessed the policy as not meeting goals on a number of points: the planned 80% share of renewable energy requires dispatchable sources but the assumed 10 GW in fossil gas generation is neither sufficient nor on schedule; extension of electric grid is behind the schedule by 6,000 km (3,700 mi) and 7 years; security of the supply chain is not sufficiently assessed; system costs to ensure 24/7 generation are underestimated and based on "best-case" scenarios; capacity installed in renewables is behind the schedule by 30%, whereas demand is expected to grow by 30% as result of electrification of heating and transport

As for

> ROI for those plants is completely abysmal already and continuously getting worse, because they are completely unable to compete with solar/wind energy prices whenever those are available.

That's because the pricing model is arbitrary. If we need nuclear, we can make it economically viable through reforming the way we purchase electricity. But,

> construction alone currently easily takes a decade

is the real problem. Unless SMRs actually materialise _and_ have fast build times, it's just not happening (and realistically, I think _that_ ship has already sailed).

I'm not really making a point here much beyond "it's one thing to say nuclear is no longer viable given our lack of investment" and another to say "it was a good thing to drop nuclear N years ago". You're not saying that for the record. By dropping nuclear, we have to deal with a bigger shortfall and that means gas peakers, etc.

Yes, in a hypothetical world we can just scale up storage and decentralise production, but what are the timelines and costs on that? Because my understanding is that realistically something like nuclear is the best way of making that problem tractable over the timelines that e.g. a nuclear plant can operate.

You can see conceptually how this is similar to a convolution with some simplification, e.g. https://openreview.net/pdf?id=8l5GjEqGiRG

Convolutions are often used in contexts where you want to account for global state in some way. - https://openreview.net/pdf?id=8l5GjEqGiRG

Note, I use PyPI for most of my day-to-day work, so I say this with love!

https://github.com/jupyter-book/myst-theme/issues/513

For UX, we think that MyST has a lot of nice bells and whistles, but we really need to author a blog post or documentation page to clarify the user-facing differences!