I understand what they are trying to do, but to me it feels like the moment when MongoDB entered the database space, with semi-structured, "flexible" storage format. It has its uses, for prototyping mostly.
But in high-volume, production workloads, giving a structure to the data you extract (what Parseur does through defining the Fields in your Mailbox, basically giving your output data a schema) adds a ton of value, and the larger the dataset, the truer it is.
Usually, you start by defining where you want your data to go, and which structure it should have, before working backwards from here and starting to extract the data. This is the key to automating your document workflow.
There is no mention of the amount of fuel used to transport the fuel in the article. From what I know it’s a tiny fraction: boats are efficient at transporting stuff (slowly)
It happens for every single Anthropic release. Then I try it on real dev and the result is laughably bad. Except in design where it has been doing a decent job for a while. I am not a designer and my bar is pretty low.
Thank you for this answer, it saved me hours of experimenting.
And bonus points for bashing MongoDB, of course. Every single project that I worked on where MongoDB was used, also had MongoDB as the single largest constraint and operations time sink.
Are you building the site from the same json files that are used in the game?
AIs are still computer programs and are not given the resources to render javascript, so they cannot access the game data from the website. And they obviously don't have it in their parameters.
BTW Google Gemini Pro just told me that they know the game but did not know the value.
Actually, it points out that it is a known trap for AI to confidently give a wrong, hallucinated value. Maybe it had already seen this thread and integrated it in their parameters or fine tuning.
I don't know...
Gemini Fast confidently gives me a wrong value. But very quickly!
I'll attach the entire Gemini response as a sub-reply.
After falling in love and hacking away with Claude for a few weeks, I'm now in the hangover phase, and barely using any AI at all.
AI works well to build boilerplate code and solve easy problems, while confidently driving full-speed into the wall as soon as complexity increases.
I also noticed that it makes me subtly lazier and dumber. I started thinking like a manager, at a higher-level, believing I could ignore the details. It turns out I cannot, and details came back to bite me quickly.
So, no AI for me right now, but I'm keeping an eye out for the next gens.
I disagree with this advice for young, healthy, not-having-kid-right-now people.
To save means keeping money in a low interest rate account, which lowers your short term risk and increases your long-term risk (you are not enjoying the higher returns of high-risk, high-return, investments such as stocks over the long term).
As young people, you should think long term.
If you want to minimize short-term risk in your life, then please, by all mean, save!
But be aware that there is no free lunch, and reducing risk right now will most probably reduce your opportunities in the long term.
Please take into account that I am reasoning with averages and averages are just a massive simplification of real life.
Adjust according to your own assessment of your context and situation. Don't follow random advice from strangers on the internet.
> Don’t assume you’ll make this much money forever.
There is a 90% chance that your salary will increase by 10% per year, on average, during your career.
On average means that sometimes your company will find a lousy excuse not to give you a raise on a given year (you know if it's your fault or not. If in doubt: it's not your fault, they are just being cheap, and you are letting them get away with it).
When this happens, you need to look elsewhere for a better paid job (which are also, on average, more interesting than worse paid ones).
Given that you apply this advice, and barring any black swan event such as the dot-com bubble bursting, you should be able to earn more and more as time passes.
I understand what they are trying to do, but to me it feels like the moment when MongoDB entered the database space, with semi-structured, "flexible" storage format. It has its uses, for prototyping mostly.
But in high-volume, production workloads, giving a structure to the data you extract (what Parseur does through defining the Fields in your Mailbox, basically giving your output data a schema) adds a ton of value, and the larger the dataset, the truer it is.
Usually, you start by defining where you want your data to go, and which structure it should have, before working backwards from here and starting to extract the data. This is the key to automating your document workflow.