Thread

1. ben_w · 1790877906 · focus · HN ↗

   Very pleased one of my predictions was totally wrong: <a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=23252711">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=23252711

   Sure, sure, what LLMs make still isn&#x27;t &quot;efficient bug-free code&quot;: my prediction is falsified because while LLMs can write and train new models with machine learning, ML is fundamentally not advanced enough to throw arbitraty new tasks at like this.

   1. FabCH · 1790878254 · focus · HN ↗

      Somewhat appropriate the site the OP links to is called „goalposts“ because as far as I can see, people keep shifting theirs.

      In your case, the comment you link to says „business tasks“ and you expanded it now to „arbitrary new tasks“. Those are not the same. An LLM today sure can do many many many business-speak conversion tasks.

      1. tripleee · 1790879112 · focus · HN ↗

         &gt; An LLM today sure can do many many many business-speak conversion tasks

         Not reliably, and not without supervision. That&#x27;s the main point. I&#x27;m trying really hard to figure out a workflow that doesn&#x27;t require me to review the code and I just don&#x27;t see how it&#x27;s possible (yet)

         You either need a comprehensive test suite (which requires understanding the code in order to create) or you need to review the actual implementation code to make sure it does the right thing

         1. sokoloff · 1790934958 · focus · HN ↗

            I have a task that I do once per year for a robotics team that I mentor: Roughly,

            Take this calendar of events and rank your preferences for the event lottery. Events are spread across 5 weeks, some are 20 minutes away, some are 4.5 hours away, some are Friday&#x2F;Saturday, some are Saturday&#x2F;Sunday, some are historically extremely competitive, some fill up in round 1, others don’t even fill after round 2. For the last two years, I’d written some scripts to scrape the event sites, find the addresses, ask Google Maps to give me driving distances and times, scrape prior year registration information to find which teams went and the strength of those teams, etc. It was several hours of effort.

            This year, ChatGPT was capable of doing almost all of that basic research and data conversion, filling out our internal spreadsheet. It probably still took 4 hours on the wall clock, but at 2% attention (5 minutes of human toil).

            I doubt I go a single workday without some kind of “I have an idea and I know there are disparate data sources out there; go find those and cross-correlate or extract the relevant data points.” question that is now 10-50x more efficient than 2 years ago.

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