I don’t necessarily fear holding eye contact, but I don’t do it naturally. That makes me think a lot of men have trouble with it as well. Once you know this, there is an obvious way to stand out among your competition: hold eye contact.

Back when I was using dating apps, I often complained about how, tragically, a majority of the women’s profiles were carbon copies of the same boring template. And that’s where the controversy starts.

There are (at least) two claims that have never made sense to me ever since I heard them. One is that double-entry bookkeeping, where you track the capital inside a company by keeping two lists of money amounts that add up to the same total, is actually useful and not a formality that is 50% redundant. The other is that fractional-reserve banking (FRB), whereby a bank is not forced to have at least as much cash on hand as the combined total of all account balances open at that bank, allows banks to create cycles of infinite money. The former is true but hard to believe at first. The latter is false but hard to disbelieve at first. Both become apparent when considering the typical claim about FBR through the lens of double-entry bookkeeping.

The promise of DeBERTa is that it does away with absolute positional embeddings added at the start of the transformer encoder, and replaces it instead with an attention mechanism that takes into account the relative distance between tokens when doing attention, in every head of every layer. Since absolute positional embeddings are the only part of a transformer whose dimensionality is related to the length of the input, would taking it out mean DeBERTa can process an infinite amount of tokens at once? Let’s analyse.

There is a very fundamental piece of advice in dating (which generally men struggle most with), which is to never derive your actions from the angle of scarcity. One should always act from abundance. However, this leads to a paradox when things get hard.

The famous roberta-base HuggingFace checkpoint is a serialised version of a RobertaForMaskedLM model, consisting of a roberta field and an lm_head field. Yet, despite this, you can still call .from_pretrained("roberta-base") on RobertaForTokenClassification and get an object that has a roberta field with exactly the checkpoint’s roberta weights, but a head with a different architecture and randomly initialised weights. Even more strikingly, you can call .from_pretrained("roberta-base") on RobertaModel, which is what normally sits inside that roberta field and consists of the fields embeddings and encoder, and somehow it can still match all the relevant checkpoint weights to the correct fields. Ever wondered how that’s done? Here’s how.

I recently watched a video online of someone complaining about the existence of millionaires and billionaires. I commented with the realistic and optimistic message that anyone has the ability to become a multi-millionaire, even if just through supplying a silly backend commodity like toilet rolls. Then, all hell broke loose.

Knowing how to build mobile apps is likely going to be a skill that will stay relevant for the foreseeable future. I’ve had some ideas for mobile apps in the past, and wanted to make sure that if I was going to learn to develop them, it had better be in a framework that allows having one code base and yet runs on both iOS and Android. We’re in luck: Google’s Flutter framework, built on top of the Dart language, does exactly that. Here are my notes learning it.

Life as a young man can be quite depressing once you realise that nobody is coming to save you, and that love might not even be on the horizon for the foreseeable future. It’s easy to fall into cynicism out of rejection and impatience, but there is another way.

Cold approaching people you find interesting – for romantic purposes or not – is hard. I’ve done it during the day and during the night, and I’ve noticed this goes smoothest under circumstances which people surprisingly don’t usually talk about.