Wero is super confusing. They're in the business of acquiring different methods (I don't even know if they always buy them outright or if they merge or they are just associated in some way), branding them ALL wero, and announcing that every payment in every channel will be rolled out SOON via wero, without ever offering specifics.
So in The Netherlands wero is the new name of eCommerce payments, but in another country the new name for peer2peer. But no idea when p2p will launch in the Netherlands or when eCommerce will launch elsewhere. And if the existing services will be degraded when they are internationalized or merged.
The merchant typically uses stripe or adyen or whatever (mollie has a cute name!), a payment service provider or PSP.
The PSP looks at what methods the merchant wants to accept, which methods the user could potentially be using (based on e.g. country by geo IP or some delivery location) and show the relevant icons.
EU users will see schemes like wero or Przelewy24, Japanese customers will see 'konbini' among the icons, and US users may only see credit cards, Apple Pay and Affirm. There are TONS of payment services. Stripe lists 123 of them.
The merchant will want to exclude methods that have high costs (for themselves), maybe they also care about their customers not getting into debt (so no buy-now-pay-later or credit), and some payment methods have higher rates of disputes/chargebacks (e.g. Amex).
In general, most merchants will want to offer as many methods as possible to prevent consumers who have a preference (this week) for using account A over account B from bouncing.
Chrome 124 (april 24) introduced hybrid post-quantum TLS, and Chrome 131 (nov 24) switched to a hybrid using ML-KEM, which was standardized in 2024, just after Firefox 132 (oktober 2024), while openssh introduced a hybrid scheme in release 9.0 (April 2022) and made ML-KEM+25519 default in OpenSSH 10.0 (April 2025).
Hybrid PQ schemes being adopted in other places is people playing catch-up, not the avant garde.
I'd say digital signatures should be the foremost concern, those may need to provide non-repudiation for decades.
>By feeding legacy PRG (circa 1985) and logics to models like Claude, ChatGPT, developers can now instruct the AI to translate decades-old dBase PRG directly into memory-safe Rust, highly concurrent Go, or modern Dart/Flutter cross-platform applications.
And it alludes to this early on, but it doesn't show any examples.
I don't know about converting it to "high concurrent Go" or anything like that, but after searching a bit, i found some old PRG code for dBase III Plus someone posted in a googlegroup, gave it to Devstral Small 2 (local model) and asked it to convert it to C# and the conversion looked fine to me, using .NET's database functionality, etc. It is too long to post it though (also TBH the code had some questionable fields).
In general LLMs seem to be very good at translating between programming languages and something like PRG uses very straightforward syntax and concepts. The first attempt by the LLM did a mostly one-to-one conversion using the console but i asked it to convert it to Windows Forms and the code looked fine for that too, using appropriate controls for the fields like text, combobox or datetime pickers (though it used fixed coordinates for the controls so i'm not sure if that looked fine).
FWIW, i didn't try to run the code (i'm on Linux and i do not even have anything related to C# on my PC nor a DB to work with :-P) and chances are there might be some subtle mistakes, but it looked like a decent starting point. IME, at least with local models, converting code between languages in a piecemeal fashion is trivial even with weird/less common languages (you may need to put some instructions to the LLM on a few edge cases though). And IMO that approach would be the right way to do it instead of dumping the entire codebase to it and hoping for the best :-P
I'm not sure what the article suggests - create a custom rust program that reads and writes to a given dbf file? Create a rust program that mirrors the PRG code, writing/reading data in a custom format?
Or maybe it should be mandatory for all companies to pay ransomware attackers. Think of it as an involuntary bounty program. Now they get to just say 'sorry (for your hurt feelings)' and suffer no consequences.
Apart from the 4% of the total worldwide annual turnover fine that theoretically could be levied under GDPR, but has never been imposed in full.
Lol no. No LLM that exists today can write a legible PhD thesis. Nor a masters dissertation. Maybe a first-year collage student, if we’re being generous, but I wouldn’t leave one of those in a room with a loaded gun either.
While modern LLMs are a far cry from biological synapses, I do find it fascinating that if you take the highly reciprocal data of a biological connectome and unroll it into a DAG, you suddenly see motifs popping up that look similar to what we find in AI. I found this both looking at temporal unrolling of RNNs or mapping layer activation weights of a Transformer. Totally agree though, the current LLM architecture itself is driven by the need to shove all of this nicely into parallelized compute hardware.
> if you take the highly reciprocal data of a biological connectome and unroll it into a DAG, you suddenly see motifs popping up that look similar to what we find in AI
That sounds interesting. Where have you heard about that? Or is this your own research?
In addition both have a property similar to dispersion. In crypto each change to an input bit should cascade through as many output bits as possible. In ML each output bit should depend on as much of the input bits (and hidden layers) as possible. So they both feature a similar maximization of entropy.
This is exactly the point. I was disappointed that I had to scroll so far down the page until I saw the word "entropy." There is a deep connection between machine learning and encryption and compression in information theory. As Shannon demonstrated, the one-time pad's encrypted output is maximum entropy, and so would data compressed to the Shannon limit. Such an optimal compressor learns the underlying probability distribution of the data to represent it with the fewest bits possible, which is exactly the goal of machine learning. A trained ML model can be seen as a lossy compression of the training data. Autoencoding models make the link between ML and compression (and thus encryption) explicit.
More simply, most interesting operations will involve "mixing up" of data. There's only so much you can do by applying a bunch of operations in series with a single input value.
Also, this is not how ER doctors work? They are not trained for this, nor does it reflect their day-to-day performance. If they would work like this, perhaps they would know a bit more about the nurse writing down those notes, and the kinds of things that particular nurse is likely to miss or overemphasize - just as an example.
The article gives a neat example: In one case in the Harvard study, a patient presented with a blood clot to the lungs and worsening symptoms. Human doctors thought the anti-coagulants were failing, but the AI noticed something the humans did not: the patient’s history of lupus meant this might be causing the inflammation of the lungs. The AI was proved correct.
Which is nice and all, but in the presence of a blood clot, I can understand that treating inflammation instead is not the first thing on a doctor's mind, what with blood clots being potentially life threatening and all. It raises the question; was this a real-life case, and what happened to that patient? Since this is a case for which the correct diagnosis is known, it was eventually correctly diagnosed - presumably then the patient did not die of a blood clot, nor of an uncontrollable fever.
Also, how representative is a patient with Lupus? According to House, MD, it's never Lupus.
So in The Netherlands wero is the new name of eCommerce payments, but in another country the new name for peer2peer. But no idea when p2p will launch in the Netherlands or when eCommerce will launch elsewhere. And if the existing services will be degraded when they are internationalized or merged.
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