honie's comments

Yes, but that's not related to code example I gave, which produces a syntax error.

> The lowdown: you can copy and paste just about any website directly into your JavaScript code without modification and it won't break anything.

The following doesn't seem to be valid though. :( Am I missing something?

    https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Statements/label

    let a = 1;

I'm not sure what it is, but something about the colour palette used in the examples shown feels a bit off to my untrained eyes.

Is there actually a job market for pure UI designers? I often see UI as only a small part of job description that involves other non-art-related technical skills.

It feels like the market is already saturated with people who can do UI design and a lot more (or the other way around for that matter). I am glad that the author managed to find internship as a UI designer and I'm aware that the author plans "to extend into UX", but the emphasis on UI design as a career change for "traditional" artists sounds... dangerous.

Edit: removed confusing parenthetical.

> 8. Apologize for their mistakes.

I haven't read the post, and I think it it's fine if you decide to do that yourself in exchange for higher sales (assuming that there is a correlation).

But please don't expect someone you hire to do that. It's just not okay.

> Off topic - I like the template/theme used in this site. Initially thought it could be like Hugo or Jekyll theme, then couldn't find more details. After some time using it felt this is custom made site with all animations and all.

The site is indeed custom built on top of Next.js. You may be interested in this the "How I Built My Blog"[^1] article on the same site.

[^1] https://www.joshwcomeau.com/blog/how-i-built-my-blog

Absolutely this.

It feels like everyone either hasn't read the rest of the article, or conveniently ignored the "Mixing guided and unguided learning" section, then went on to cherry-pick quotes to trash the article. The irony...

The author does say that it's problematic, but the relevant paragraphs only say that they are unexpected and then mixed the discussion with what would or wouldn't be more natural.

The sentence you quoted is there to give context so I could give the example and the rest of the information. In retrospect, I probably should have used a quote. I apologise if you feel offended.

> It's the best introduction to these methods for the mathematically-minded that I've seen, in the sense that it gives quasi-rigorous justifications for a lot of claims, but at the same time doesn't get bogged down in rigor.

Agree! I remember that reference by heart exactly for that reason! :)

> They mention in the book that simultaneous updates with a checkboard are in fact OK.

Yes, the algorithms discussed in the reference were designed to do exactly that. However, and using the Wolff algorithm as an example, in a perfectly checkerboard at low temperature, a cluster would still not grow beyond beyond its confinement. So the initial iterations actually gets reduced to the single-flip Metropolis algorithm under these conditions, but would eventually break most of them and start updating large clusters every iteration. The only time where one would see only two states alternating is at when the simulation temperature approaches 0, where the cluster always covers the entire lattice.

I have only skimmed through the article but spent a bit of time on the "simultaneous update" part because it caught my eyes.

If I'm not mistaken the author made a mistake in assuming that it's okay not modifying the spins in the first pass and then doing the update in the second pass, which would cause the system to approach a strange equilibrium with that flips between two states.

For example, if you set the simulation temperature to well below that of the critical temperature of the system, a robust algorithm should eventually cause all of the spins align (to take the same sign).

Also, and if I'm not mistaken, the author may have misunderstood that these simulations show the evolution of a system over time -- I think they are meant to show the possible states that a system can be in under a set of conditions, trying to rationalise whether or not it's sensible that spins should or shouldn't is perhaps not quite the right approach.

The robustness of these algorithms are usually tested by carefully collecting many samples, and at different temperatures, and use them to estimate known properties of the systems. If an algorithm fails to estimate those quantities reasonably, then there is a good chance that it's not correctly implemented.

If you're interested in this topic, one paper that I can immediately remember and is easy to read is this: https://arxiv.org/pdf/cond-mat/9703179.pdf. The section on Wolff algorithm, in particular, should solve the "mystery" of the simultaneous update. Here is a demo I have played with a few years ago that has the Wolff algorithm correctly implemented: https://mattbierbaum.github.io/ising.js (make sure you change sweep skip to an odd number for simulations at lower temperatures).

- Does the study exclude the possibility of other factors causing the increase in cases in 2020? For example, potential reduction in exercise and exposure to sunlight?

- How valid is the assumption "assuming a 10-year latency period between alcohol consumption and cancer diagnosis"?

- Why would not one attempt to find correlation between yearly reported cases (or PAF if applicable) and yearly alcohol consumption instead? As it stands, some points in the discussion read like assertions. One example is this:

> Our estimated global PAF was lower than the previous global estimates of 5·5% of cancer cases in 2012, 4·8% of cancer deaths in 2016, and 4·9% of cancer deaths in 2019. This difference could be due to genuine decreases in consumption of alcohol in several world regions, such as in southern Europe and central and eastern Europe, as Shield and colleagues reported a 5·5% decrease in the global alcohol-attributable age-standardised rate of death from cancer between 2000 and 2016.

And this:

> On the other end of the spectrum, alcohol consumption in central and eastern Europe has historically outranked that of other world regions, but has decreased in recent years, whereas increases in alcohol consumption, linked with countries' economic development, are projected in Asian countries such as China and India.

The statements above seem to be at odds with each other according to the number of alcohol-attributed cases from each country in the appendix.[2]

[1] https://www.thelancet.com/journals/lanonc/article/PIIS1470-2...

[2] https://www.thelancet.com/cms/10.1016/S1470-2045(21)00279-5/...

Edit: formatting, removed part of a sentence for a better tone. Edit 2: removed irrelevant part in the second quote.

> It is very unlikely that they could have even explored the full parameter space if they wanted to, it's such a huge undertaking...

Absolutely agree and no arguments whatsoever. In fact, one of the first things that I joked to myself was that we will soon see papers on MOF-suppoorted version of the catalyst -- well before we see any DoE.

In any case, I just want to note that I made the comment in response to the following:

> $190 million -> 190 bucks ;)

Which appears to be a misunderstanding, as the calculation in question has implicitly taken into account the "millions of times" increase in disinfection efficacy.

> I think it'd be a bigger deal if it didn't require pressurization to 10 bar personally, pumps are expensive to operate and catalysts are a one-time-cost (if you're lucky).

I felt uncomfortable about the pressure, too, but it was just a hunch. Thank you for putting this into context. :)

Edit: clarity, typo.

> Seriously though, couldn't they just use far less catalyst and not have to deal with the prices you've calculated?

The flow rate is likely positively correlated with catalyst loading (in the part that we care about) and the calculation is already based on the flow rate of 0.2 ml min¯¹ they reported in the paper. The reported flow rate is also likely already optimised for the Nature Communications paper.

Location: Sydney, Australia

Remote: Yes

Willing to relocate: No, not at the moment. But would be happy to when the pandemic eases out.

Technologies: TypeScript/JavaScript, HTML, CSS/SCSS, React, Node.js, reading/writing documentations

Résumé/CV: Available upon request. For an overview, and if you don't mind LinkedIn, please see [removed].

Email: [removed]

=============================================

Hello! I am looking for a junior software engineering-related role. I highly prefer working at an organisation that has neutral-to-positive social impacts, and can offer mentorship and a clear and sustainable pathway for growing together.

Potential red flags for you to consider:

  - I am self-taught, and the only professional experiences I have had so far are a few freelance jobs and a year of work in applied machine-learning.
  - I have also made some pretty questionable career choices in the past.

What I can offer:

  - 3 Years of web development experience both front-end and back-end. It may be worth noting that I do supplement my studies with computer science and I still practise algorithms and data structures regularly.
  - I am also reasonably familiar with git, GitHub, and common tools in the JavaScript ecosystems. Other technologies that I have a working knowledge of, and would happily work with: Next.js, Python, SQL.
  - A background in natural science if your organisation is in a related field.
  - Multilingual. May be useful when interfacing with product designers and/or spotting design issues during development.
  - Honesty. And I expect that to be reciprocated.

Any feedback that is not job-offer related would also be very much appreciated! :)

Edit: formatting. Fixed typo.

It appears that the molecules on the page are generated with a machine learning algorithm trained on a small organic molecules dataset that is heavily biased towards cyclic, particular nitrogen heterocyclic, structures (I only sampled about 40 of them, so it could also have just been my luck).

The model seems to have learnt chemistry pretty well because, as many have already pointed out, most of the molecules generated do actually exist (or are extremely likely accessible if they haven't already been documented). Even the ones with strange bond angles have otherwise perfectly normal number of bonds. The only time where I get molecules that cannot possibly exist are those with overlapping atoms that just defy known physics.

Addendum: it is worth noting that the model might actually have been trained with data that contain bond lengths, or even spatial information if no post-generation geometry optimisation is performed before a molecule is rendered.