raphman

Oh, I have some ultrasound probes lying around - would be very interested in a report!

FWIW, this isn't a problem at my German university.

I don't fail them usually - I expose them; and once I asked a student to leave the room because they were blatantly unprepared despite me warning them before.

My main point is: flipped classroom works great in some contexts but is no panacea and requires setting clear expectations.

Aside: as English is not my native language: "you fail them" can mean both "you give them a failing grade" and "you fail to provide the support they need", right?

This only works if you either have very intrinsically motivated students or you verify that really everyone has actually read the assigned materials before class. Otherwise, some people will turn up unprepared, you will have to explain stuff they should have gotten from the reading assignment, other students will be annoyed, students will stop preparing for class, vicious cycle.

Might be. There certainly were bugs in my understanding of micro-controllers and compilers at the time...

EDIT: but SDCC indeed ignored 'volatile': https://sourceforge.net/p/sdcc/bugs/436/

As a student, 20 years ago, I felt like I was going crazy when using SDCC for pic16. Being new to microcontrollers and C, SDCC seemed cursed - even simple 'blink LED' test cases pseudo-randomly worked or didn't. It turned out that the compiler optimized away some 'empty' loops a little bit too aggressively without realizing that these loops were setting output pins. These bugs have long been fixed but still live on in my long-term memory.

Oh cool. Would you mind expanding a bit on the implementation? I have been thinking about a generic auth system like this for some time.

May I just note that there are other jurisdictions on this planet that are less money-biased than than the US one but will be disrupted by law-LLMs as well?

It seems much more probable to me that these LLMs will make good things worse than that they will make bad things better.

Actually, Markus Hess already gave such a talk this year (in German, no closed captions but lots of AI-generated illustrations):

https://media.ccc.de/v/ho26-124-vom-hacker-zum-spion-die-ges...

mildly interesting detail: some of the videos also include an audio track - and because some of the videos are mirrored in time, you get to hear demonic voices:

https://flat-social-bucket.lon1.cdn.digitaloceanspaces.com/p...

https://flat-social-bucket.lon1.cdn.digitaloceanspaces.com/p...

(and I really like this audio-less one: https://flat-social-bucket.lon1.cdn.digitaloceanspaces.com/p...)

Sounds interesting - especially as gather.town no longer offers an event option. However, the landing page uses 100% CPU and stutters a lot. Also, signup does not work for me, with https://app.flat.social/api/auth/signup returning 403 forbidden:

  {"success":false,"error":"Verification failed, please try again"}

(No captcha was shown on the page.)

I hope that this is temporally and spatially far away enough. In order to fully appreciate the joke, one should have read https://openai.com/index/navier-stokes-solution/ (I didn't beforehand).

I don't think that "death" is the perfect word here - it typically describes a permanent state. FWIW, what you describe ("temporary state of complete loss of personal purpose") is called "Sinnkrise" in German (crisis of meaning/purpose).

(And because not everybody might know: the comment references Kübler-Ross' five stages of grief (when confronted with a tragic outlook)¹: denial → anger → bargaining → depression → acceptance)

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

It seems that at least three groups tried to replicate the study and failed (i.e., found no or little actual effect)¹.

> In contrast, other research in economics and sociology has hypothesized and found a positive relationship between SES and prosocial and ethical behavior. We review the empirical evidence for these contradictory findings and conduct two direct, well-powered, and preregistered replications of the field studies by Piff and colleagues (2012) to test the relationship between SES and unethical/selfish behavior. Unlike the original findings, we find no evidence of a positive relationship between SES and unethical/selfish behavior in the two field replication studies.

¹) https://pubpeer.com/publications/7363E2E1B57AC42EF166E0D0266... ²) https://psycnet.apa.org/record/2023-34988-001?doi=1

Not much detail so far.

> 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]

Why do you think that? Does your view include colleges worldwide or is your comment about a specific country?

Yes. But in my limited experience, the practically useful fresh takes come from people who have immersed themselves deeply into a problem, not from people who spend a weekend with ChatGPT.

> winning participants had simply made powerpoints

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.

There is already a lot of research on predicting [1] and monitoring [2] wildfires using traditional machine-learning approaches.

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

I guess it depends. While doing my PhD at a top German research university, our chair advised multiple of these projects, too (e.g., BMW, Siemens, Audi).

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.

a) No. b) It depends.

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.

The website gives a very nice and captivating intro to the device.

But why do you spell MIDI in lowercase there?

> plugs into your piano via midi

There are quite a few people who own a 3D printer but not a laser cutter. And not everyone can or wants to use an external service.

tl;dr: Use this if you don't like doing science or doing things well. It hallucinates references.

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

<blink>BUT it includes at least one hallucinated reference!</blink>

P.S.: What does this mean?

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

Ah thanks - I couldn't remember the original version.

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...

> It's been really frustrating that neither Codex nor Opus can make targetted edits to Fable's code without screwing something subtle up.

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.

Oh my gosh - that article sounds like the winning entry to a 'write like an LLM' contest. So over-the-top that I first thought it was satire. If the author indeed wrote that all by themselves, they have certainly read too many AI slop articles.

I'm not sure whether the Guardian article mentioned this or another one I read: the road segment in question seems to be in the old town which makes it difficult to find an architectural solution.

As the article says: this is not a general speed limit but an experimental speed limit for an apparently very crowded area where many kids cycle to school. As someone whose 'normal biking speed' ist typically 30+ km/h on suitable bike lanes, I have no problems with speed limits at critical choke points.

Hmm, assuming that the AI bubble might pop a little bit after the upcoming IPOs, maybe it's better not to call yourself an AI company then?