One solution is to try and care the exact right amount: be invested in work a bit, but also have hobbies and a family and whatever else gives you perspective about your work problems. If you have a rich and healthy personal life, it’s hard to find yourself yelling at somebody about React state management. However, this is a tricky balance to maintain over time.

Why not slow down?

Why does it have to be frenetic? Why not just slow down? I suppose you could, but I don’t recommend it. It’s just such a miserable experience to spend your day close-reading LLM output: carefully chewing and savoring each morsel of slop. It’s far less unpleasant to skim through quickly and pick out the useful nuggets of content.

Couldn’t you simply do more of the work by hand? It’s unfortunately true that tech is high-pressure these days. If you’ve got the time and space to work more slowly, that’s great! But when your company gives you a “solve this task ten times more quickly” button, you are heavily incentivized to use it as much as possible, or risk being outcompeted by your peers.

Damn...

База.

If you set out to build a local-first application that users have complete control and ownership over, you need something to solve data sync.

Dropbox and other file-sync services, while very basic, offer enough to implement it in a simple but working way.

Sure, it won’t be as real-time as a custom solution, but it’s still better for casual syncs. Think Apple Photos: only your own photos, not real-time, but you know they will be everywhere by the end of the day. And that’s good enough!

Imagine if Obsidian Sync was just “put your files in the folder” and it would give you conflict-free sync? For free? Forever? Just bring your own cloud?

I’d say it sounds pretty good.

Recently, there has been considerable interest in large language models: machine learning systems which produce human-like text and dialogue. Applications of these systems have been plagued by persistent inaccuracies in their output; these are often called “AI hallucinations”. We argue that these falsehoods, and the overall activity of large language models, is better understood as bullshit in the sense explored by Frankfurt (On Bullshit, Princeton, 2005): the models are in an important way indifferent to the truth of their outputs. We distinguish two ways in which the models can be said to be bullshitters, and argue that they clearly meet at least one of these definitions. We further argue that describing AI misrepresentations as bullshit is both a more useful and more accurate way of predicting and discussing the behaviour of these systems.

How do you know what hourly rate you are worth? What factors should be taken into account? Here are my criteria.

На клавиатуре есть кнопка «Капслок». Если её случайно нажать, то компьютер печатает большими буквами. Такое нормальному человеку не нужно никогда, поэтому во избежание ошибок эту кнопку нужно отключить.

Всё так, только на капслок у меня стоит смена языка.

Я начитался историй об оппозиционной ботферме и подметил закономерность: для союзников и противников у них разные слова, которые обозначают одни и те же явления

Языки программирования как люди

Илья рассказывает про свой движок блога. Акцент на заботу о пользователе и невидимых фичах.

О том, как находить места на фото с помощью Оверпасс турбо

Статьи лучше про раскладки клавиатуры я не видал

Дистрибутив для резервного копирования