When learning new topics, we always ask questions we can’t find answers to. “Why are there two APIs that do seemingly the same thing?” “How do I achieve this goal?” “Why does this code not work even though it looks similar to the example?” As we research and get more familiar with tools, we gain understanding. At some point, we become experts and know how to answer earlier questions. But it’s worth covering how we get from point A to point B to help others make progress, too. In my experience, it’s common that the answers seem obvious post-factum, but there’s a missing link between facing questions and knowing the terms to look for. Usually I have to get familiar with the project architecture, read its code, look at what it interacts with, scan the bug tracker, etc., before I built a model in my head that answers my questions. And then I discover that the model is easy to understand and has docs, and I agree with experts that it’s a reasonable and well-designed model – forgetting that I-the-novice failed to find it despite the documentation existing!

When you face a problem and spend days finding a solution, write about your successes – or even failures. This will help others who are stuck on the same issue, and it will help experts find missing information or unclear wording in their docs (which is why documenting failures is useful, too). Blogs, social networks, anything goes, really – even if only your friends see it, some of them may still find it useful.

  • locuester@lemmy.zipEnglish
    103·
    4 days ago

    I did this for decades. Used stackoverflow to help millions also. All my teachings were distilled into LLMs for the world to use. I’m proud of that.

    Most don’t share my sentiment of pride, but understand when you write, your audience is LLMs. Not just humans. That’s just the unfortunate reality.

  • __WmW__@feddit.orgEnglish
    1·
    3 days ago

    All to get your write-up crawled, ingested, distilled, trained-on and sold for a monthly price by the billionaires building LLMs and gate-keeping the “intelligence”. They’ll earn billions from all the training data we collectively feed the internet, while we get $0 out of those profits for providing our training data and 0 attribution, and the LLM will get called “intelligent”. And then we’ll also pay the monthly access price to this intelligence. It’s a sad reality we live in.

    • codeinabox@programming.devOPEnglish
      3·
      3 days ago

      What’s the alternative? The end of openly available knowledge to thwart training language large models, would be a case of cutting off the nose to spite the face.