Currently I'm using NBA.com. See https://github.com/swar/nba_api for more details on that (I used it to create my own Rust client).
So far I'm trying to exclusively rely on cdn.nba.com endpoints. There are repos with historical PBP data too if you want to avoid hitting their API. Eg: https://github.com/ramirobentes/nba_pbp_data
If this project becomes more successful then I would look into paying for a real data provider like https://sportradar.com/
Sure it could. Commercially sanitised corposlop machines like ChatGPT and Claude would never create something so silly, but LLMs are perfectly capable of it. Try turning the temperature up a bit. Might need a bit of human filtering and cleanup as well.
But if you're hand tuning your AI then I suppose it doesn't meet the promise of AI, does it?
That is where a lot of people in tech believe things are headed, executives in particular. Fully autonomous AI-driven codebases. “Dark factories.” One agent generating a plan, one writing the code, one responsible for testing and validating, and one to orchestrate the rest.
The idea is software development will follow in the footsteps of physical manufacturing and eventually become fully automated. I think one huge flaw with this belief/comparison is that the automated assembly line is deterministic. LLMs and AI agents are not. Every Tacoma made on the Toyota assembly line is made from the same blueprint. There are no robots deciding on-the-fly whether to use a laser weld or an adhesive, what material to use for a specific component, etc. Now think about how LLMs build software. No matter how strict a harness or detailed spec you give it, it will always make decisions. That is what makes it “agentic” after all.
I've fully automated the software development part in my company. With production apps, no code has been written by hands, no requirement written by hand. Everything is fully automated except the testing part. And testing means the human testable parts are still performed by hands. The resule, with less people we produce more, actually way more. Our cost has gone down, quality has improved litrally. The code is being produced with AI is of higher quality, we employ multiple agents during planning phase and then different agents during execution phase. Sometimes some tasks has to be redone because some agents are better than others. Our release cycle is reduced from 2 weeks to 2 days.
I am trying. The main issue is it would be extremely annoying for the juniors, who have work to get done, and prefer to throw problems at Claude rather than ask me questions.
In general, I believe learning usually needs to be driven by the learner.
Writing code without an LLM is akin to cooking a meal yourself, as opposed to something in between hiring a private chef and pulling a slot machine lever. When I cook a meal for myself or my loved ones, I become deeply familiar with the ingredients and the recipe. I am a good cook. Every decision, small and large, I make myself. Using feedback, I can tweak the components and the process.
However, if I order food and call it my own creation, I’ll be speechless when my nut-allergic friend asks me if I used peanut oil or olive oil. And does it taste good? The end result is subjective, not binary, like your three similes. What if the food sucks? Must I now micromanage the chef? I don’t want to make them cook it again and again, hoping each time it’ll taste little more like how I want it to. I’d rather just cook it myself; I am a good cook, afterall.
Well to me using an LLM is akin to using microwave for food, and programming is getting your hands dirty in the art of cooking. I used to do the latter but the pace is too much, so I'm forced to go for the former. I still miss the days where I can cook myself
This is a good analogy. Also I hate cooking; it feels like a chore and waste of time. Given the means, I prefer to have someone else do it for me so I can focus on more interesting things.
If the analogy is good, does it mean AI boosters hate programming, and feel like it's a chore and waste of time? Yet they keep calling themselves programmers.
I dislike cooking; I don't call myself a chef, nor go on cooking forums saying they're all idiots for churning butter as a hobby, and telling them they are luddite dinosaurs just because I bought myself an Instant Pot.
And clamoring that instant pot is capable of doing the job of 10 chefs, while conveniently forgetting that chefs using the instant pot is way more useful. And then congratulating each other on new way of adjusting the temperature and timer of the instant pot.
I don't see it the same way. I see it as akin to building a machine which can do the cooking for you. There is a lot of experimentation in terms of how you can construct that machine and how you can set it up, and for some people the payoff is in exactly this process.
That's me. I've just moved the puzzle to figuring out the CC optimal "language" and mode of working with an LLM. At the same time, I haven't lost my love/expertise... Yet.
I explained it yesterday that it's no different than Linq powered by EF Core. You still need to understand the database and how SQL will work, or should. If you don't, you're queries will be slow and inefficient, hazardlessly oftentimes.
LLMs are an impressive abstraction, but they are not magic...yet. or maybe it is, but you still need to figure out the incantations. Not that I like to uptick and witchcraft, so I hate that analogy.
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