I'm a director at the Max Planck Institute for Intelligent Systems. Prior to joining the institute, I was Associate Professor for Electrical Engineering and Computer Sciences at the University of California, Berkeley. My research contributes to the scientific foundations of machine learning and algorithmic decision making with a focus on social questions.[0]
Also simply knowing of him doesn't answer the question.
This looks pretty cool! However I feel that if the README starts with "Drop-in replacement for X", it should also start with "Why use this over X".
I do like the idea of saving prompts for projects like these (Which is also where the above question is answered: "Creating an MIT-licensed wrapper around Moto that has 100% feature parity with Localstack." [0] Which (i assume) is motivated by the recent changes to Localstack's distribution model [1])
I thought that line was kind of funny: When a CI run fails, you don't rerun it and wait for the result, you rerun it and check why the original run failed in the meantime. Is it flaky? Is it a pipeline issue? Connectivity issue? Did some Key expire?
If you just rerun and don't go to find out what exactly caused CI to fail, you end up at the author's conclusion:
extensive tracking of self-related metrics to improve ones health is the equivalent to reading tons of self-help books to improve ones life/social skills/...
We already mostly know what makes people happy/healthy: personal connections, physical activity, healthy diet and some sort of purpose/goal in life that goes beyond day-to-day activities.
The problem is that these things generally require (hard) work and can be unpleasant sometimes, so humans do what humans do and spend unreasonable amounts of time doing the more pleasant things such as reading and gathering info rather than applying these and what they already know.
(That's not to say that a project like this can't be fun or lead to insights, especially across longer time spans, but i feel like all of the questions in the first paragraph have fairly obvious answers if you know yourself at all, that don't require extensive tracking of stats to get)
> Tickets are sent via PDF for trains running 3 hours late
I agree that the delays are unacceptable, but the official app is great w/ digital tickets + seat registration, you don't need the PDF at all (it's even optional during checkout, so if you don't like them you can just uncheck the box lol)
fwiw i think the interesting part about the original study wasn't so much the slowdowm part, but the discrepancy between perceived and measured speedup/slowdown (which is the part i used to bring up frequently when talking to other devs)
this reads kind of... bitter? The theme of the hackathon was AI (as noted by the author further down), so I'm not sure why he seems surprised/upset that 'All winners had "AI" as a significant part of their solution.'
The other points were always true for hackathons (For some more than others, depending on judges/audience), even before AI coding was a thing.
> I've been working on this since last September at a slow burn (no code reused for HackEurope though) and the goal is to have a running startup by May.
If anything, this is pretty much the opposite of what a hackathon is supposed to be: A place where you meet people you might not even know, come up with an idea on the spot and develop an MVP + pitch it on a tight (time) budget. Taking an idea you've already been working on for months and using it for a hackathon submission feels... odd
> A solid 90% of the projects there were just vibe coded slop. Even the ideas were AI. You can tell when multiple people implemented the exact same idea with the exact same title, description, and implementation.
The first is probably true, but to really judge the impact of it (Did AI generated ideas actually win?) we'd have to see the results
> A lot of cool ideas are out of distribution from the training data, and those rarely show up at hackathons anymore. The AI says they're "too hard" and people simply avoid these.
I say it somewhat jokingly. Most of the challenges were AI, but there was a specific security track that wasn't about AI (but AI bug bounty hunter won. Not too mad, just annoyed at miscommunication about which countries the sponsor was actually in).
> If anything, this is pretty much the opposite of what a hackathon is supposed to be: A place where you meet people you might not even know, come up with an idea on the spot and develop an MVP + pitch it on a tight (time) budget. Taking an idea you've already been working on for months and using it for a hackathon submission feels... odd
The thing I've been working on is a much larger encompassing system where this would just be a small component. No code reused because no code was written for this yet. My task now is to take the shit code written during the hackathon and make it actually usable.
> The first is probably true, but to really judge the impact of it (Did AI generated ideas actually win?) we'd have to see the results
Yes, the winner also won the Lovable and Claude tracks. Lovable track was specifically about vibe-coding.
I'm a director at the Max Planck Institute for Intelligent Systems. Prior to joining the institute, I was Associate Professor for Electrical Engineering and Computer Sciences at the University of California, Berkeley. My research contributes to the scientific foundations of machine learning and algorithmic decision making with a focus on social questions.[0]
Also simply knowing of him doesn't answer the question.
[0] https://mrtz.org/
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