> We have received cease and desist letters. Awaiting legal advice at the moment, but for now expect all nitter instances to remain down for the foreseeable future.

[for all those who - like me - first check the comments before clicking on the link]

Wow. A student of mine told me of more or less the opposite (but also negative) experience: they attended a hackathon where the winning team spent the whole weekend playing cards - and then presented an app they had already built before the hackathon as their contribution. It was obviously much more polished than the weekend projects.

How would an LLM be useful in predicting wildfires? A project that incorrectly claims to solve a problem "using AI" has negative value. By proposing a solution that will not actually work, these hackathon participants make a complex challenge seem trivial and take away attention from other, more mature work.

[1] https://www.nature.com/articles/s41598-025-92171-w [2] https://sites.research.google/gr/wildfires/

EDIT: I checked out the project's description. It seems that they do not actually use LLMs but old trusty XGBoost. It looks like they actually have put some thought into this - however, I'm still not convinced that it would work in practice at any reasonable scale. (Maybe some billionaires might want to invest into something like this to protect their own mansions.)

LinkedIn post: https://www.linkedin.com/posts/emmanuel-karibiye-509b9a1b3_h...

Project site: https://zerostrike.live/

Description on Devpost: https://devpost.com/software/zerostrike

Yes - the downsides you mention are all true. But similar downsides apply to most PhD students working directly at the university - either you have some teaching load and administrative duties, or you work in an externally funded project and have to write project reports and do a lot of non-research stuff, too.

As I mentioned elsewhere in the thread, if you want to have an academic career, doing a PhD in industry is not the best choice. But if you want to work in R&D or as a group leader in industry, these PhD positions might be a good stepping stone.

I know the German system quite well and know people who did their PhD in industry, people who did their PhD in an externally funded research project, and people who pursued a more self-directed PhD while working as a research and teaching assistant.

I don't think that there is a general 'PhD inflation' in Germany (though there are some disciplines with this problem). It is well understood by most PhD students that an academic career is the exception, not the rule. Most of them choose to do a PhD because they like the academic environment and want to learn more. Most PhDs go on to work in industry research labs, science-adjacent roles (e.g., museums) or as group leaders in tech companies. There is sufficient need for PhDs in most fields.

Industry-embedded PhD students are required to meet the same criteria as other PhD students. They also publish their research at the same conferences - but it is often more on the applied side. One could argue that such research has "little scientific value" - but so does most research.

The most important outcome of a PhD is not the list of publications but a person who deeply understands a domain, knows how to critically analyze a problem, and finds good solutions. Doing a PhD 'in industry' also allows you to do this. And it gives you a foot in the door at that company.

FWIW, many PhD students I knew, e.g. at BMW, complained a little bit about the side-projects they were expected to do, or the bureaucracy at such large companies. And, because you don't have to do any teaching and rarely supervise undergrads in industry labs, you are less qualified for an academic career than PhD students who work at the university.

But why do you spell MIDI in lowercase there?

> plugs into your piano via midi

Seems to be based on https://github.com/swaruplab/operon as evidenced by the authorization dialog and https://x.com/testingcatalog/status/2037684573161783373 .

Mostly targeted at life sciences - e.g. integration for FDA, PubMed, genomics databases but no ACM / IEEE as far as I can tell.

Edit: arXiv search seems to be supported - but not Google Scholar etc. So, this tool is of little use for most researchers outside life sciences.

Edit 2: Quick walkthrough: the AppImage starts a browser window with an onboarding wizard and a chat interface. It suggests a few things one might do at the start of a research project - e.g. do a quick literature review. When I chose that option, wrote Python scripts that used MCP calls to do arXiv searches. Stayed seemingly stuck there for a few minutes not returning anything. Then:

> The free-text search returned too much noise

Claude decided to choose a certain paper as a starting point for further research. Shortly afterwards:

> That DOI resolved to the wrong paper. Let me find the correct anchor papers by title/author search directly.

Then it meandered a few more minutes doing research and creating a citation graph (that it did not show to me).

> I have a complete picture. Let me verify the key DOIs resolve and then write the review.

Then:

> The lint flags em-dash overuse. Let me reduce them, then save.

Then: a nice but verbose literature overview of my chosen topic

BUT it includes at least one hallucinated reference!

P.S.: What does this mean?

  [reviewer] verifier_mode=default-on downgraded to off: pro subscription tier, autoReviewer withheld (frame=f2a81cb2)

For reference: it's called Kernighan's Law, and can be found in the Second Edition of "The Elements of Programming Style", page 10 [1].

The original phrasing is:

> Everyone knows that debugging is twice as hard as writing a program in the first place. So if you’re as clever as you can be when you write it, how will you ever debug it?

[1] https://archive.org/details/the-elements-of-programming-styl...

Reminds me of the old adage: don't try to be too smart when writing code. Otherwise, dumber people - including your future self - will have trouble working with it.

Gergely Orosz did an interview with them in 2024:

https://newsletter.pragmaticengineer.com/p/twisting-the-rule...

> "Cast iron and carbon steel have nearly identical thermal conductivity (~52 W/m·K), which surprises most people."

is unsourced. And the precise "~52" is quite misleading - Wikipedia and online sources report thermal conductivities in the range of ~30-50¹.

Also:

> "Critically, whipped egg white foam drainage follows a hyperbolic saturation curve: v = V × t / (B + t), where V is the maximum drained volume and B is the drainage half-life (Lomakina & Mikova, 2006)."

As far as I can tell, the article² (cited twice for this claim) does not contain any equations modelling drainage over time, and especially not this equation or the term "hyperbolic".

So, it seems that you cannot really trust the sources the author's LLM included. For me, that means that I cannot trust any of the other claims in the article (or the author in general).

¹) https://en.wikipedia.org/wiki/List_of_thermal_conductivities

²) https://www.agriculturejournals.cz/pdfs/cjf/2006/03/02.pdf

> " Ovalbumin coagulates irreversibly at 80°C (Weijers et al., 2003), permanently setting the foam structure.",

and the paper by Weijers et al. says:

> "A strong temperature dependence on the reaction rate was observed. At 80°C, half of the protein was denatured and aggregated in less than 2 min (half-time, th), while at 68.5°C this took approximately 6 h."

So, the citation is generally true-ish although a little bit imprecise.

At which temperature range Ovalbumin coagulates seems quite irrelevant for the whole article, however. To me it's unnecessary fluff, others might like that kind of detail.

(This does not imply anything regarding the article as a whole - it's just one thing I checked.)

My own experience with flipped classrooms (which seems to be shared by quite a few people who have tried it out): they only work well if all students actually read/watch the materials beforehand. In small, advanced courses, intrinsic motivation may be sufficient - but in most cases you need some extrinsic coercion - such as a mandatory quiz about the materials or hand-written lecture notes that need to be shown at each in-person session.

With AI, some people don't watch the lectures but let ChatGPT give them a summary which they submit. Then these people poison your in-person session with their lack of knowledge and motivation.