crm9125
How are we supposed to stay skeptical of everything if we read anything!?
Even if aqua is neither more green or more blue, wouldn't it be interesting if when given the choice, the outcome leans toward green or blue to a statistically significant degree? or perhaps that there are differences in how it's perceived based on measurable factors like geography, wealth, height, weight, etc?
Collecting data is how we learn, and discover new things. Even if it seems dumb to you.
Something, something, forest, trees.
Without Humans, AI does nothing. Currently, at least.
Somewhere off in the distance I hear billionaires laughing.
This is only important if you care about the future of humans. At least in America, attention spans have shortened, empathy has decreased, and individualism has increased. Billionaires don't care about the future beyond their own life. And unfortunately, one of the worst of them is now the head of the country.
Or do I want to live longer?
They aren't necessarily mutually exclusive, but different actions could result in different outcomes for each.
This is definitely jointly directed by Rogen and Tarantino and I will be watching all 3 hours of it.
Until UBI is a thing, they (necessarily) need to be very cognizant of where they spend their time in relation to where they make their money.
Republicans should propose a reasonable solution that will get the votes to pass, otherwise, this will continue.
Maybe they can't (or don't want to, out of fear of being embarrassed or feeling uncool/uncertain perhaps) explain to you how they find things, but when they are hanging out with their friends and are talking about similar interests, discovering they know about similar things, and sharing things they know about that their friends don't yet/learning similar things from their friends, that's where the magic happens.
But I've been using Claude to help with all kinds of side projects. One recently was to help create and refine some python code to take the latest Wikipedia zipped XML file and transform/load it locally into a PostgreSQL DB. The initial iteration of the code took ~16 hours to unzip, process, and load into the database. I wanted it to be faster.
I don't know how to use multiple processes/multi-threading, but after some prompting, iterating, and persistent negotiations with Claude to refine the code (and an SSD upgrade) I can go from the 24gb zip file to all cleaned/transformed data in the DB in about 2.5 hours. Feels good man.
Do I need to know exactly what's happening in the code (or at lowers levels, abstracted from me) to make it faster? not really. Could someone who was more skilled, that knew more about multi-threading, or other faster programming languages, etc..., make it even faster? probably. Is the code dog shit? it may not be production ready, but it works for me, and is clean enough. Someone who better knew what they were doing could work with it to make it even better.
I feel like LLMs are great for brainstorming, idea generation, initial iterations. And in general can get you 80%+ the way to your goal, almost no matter what it is, much faster than any other method.
Once you get to graduate studies that's where the material coverage is a little more sparse/niche (though usually still not groundbreaking), and for a PhD. coverage is mostly non-existent since the point is to expand upon current knowledge within the field and many topics are being explored for the first time.
We used to hang these guys.
1. Added Boilerplate and Ceremony: Simple tasks may require extra type declarations and structures, adding “ceremony” that feels unnecessary for quick one-off solutions.
2. Rigid Type Constraints: Combining different data types or working with unclear data shapes can force complex type solutions, even for simple logic, due to strict compilation rules.
3. Complex Type Definitions for Simple Data: Handling semi-structured data (like JSON) requires elaborate type definitions and parsing, where dynamically typed languages let you manipulate data directly.
4. Refactoring Overhead: Small changes in data types can cause widespread refactoring, turning minor edits into larger efforts compared to flexible, dynamically typed environments.
5. Complexity of Advanced Type Systems: Powerful type features can overwhelm trivial tasks, making a few lines of code in a dynamic language balloon into complex type arguments and compiler hints.