It's because none of the stuff you say is obvious is actually obvious. You might be totally right about all of it (my own view is that regardless of what the intention is, this stuff will inevitably be misused), but it needs to be demonstrated that you are. The word obvious has a different meaning.
This is a pretty common phenomenon in politics, where people have a political view that is obvious to them, but other people actually disagree with that view. This is one way that political discussions go off the rails, because if you think your own views are obvious, you quickly start thinking that people have some ulterior motive for debating that "obvious" view. But the reality is often just that they just have a genuine difference of perspective, that the thing that is obvious to you is just not obvious to them.
As someone who enjoys working with AI tools, I honestly think the best approach here might be bifurcation.
Start new projects using LLM tools, or maybe fork projects where that is acceptable. Don't force the volunteer maintainers of existing projects with existing workflows and cultures to review AI generated code. Create your own projects with workflows and cultures that are supportive of this, from the ground up.
I'm not suggesting this will come without downside, but it seems better to me than expecting maintainers to take on a new burden that they really didn't sign up for.
BTW, I went to your website looking for this, but didn't find your blog. I do now see that it's linked in the footer, but I was looking for it in the hamburger menu.
Thanks! We need to re-do the top navigation / hamburger menu -- we've added a bunch of new things in the past few months, and it badly needs to be re-organized.
Very interesting. I am keenly interested in this space and coincidentally had my blood drawn this morning.
That said, have you considered that “Measure 100+ biomarkers with a single blood draw” combined with "heart health is a solved problem” reads a lot like Theranos?
FWIW, the single blood draw is 6-8 vials -- so we're not claiming to get 100 biomarkers from a single drop. The point of that is mostly that it just takes one appointment / is convenient.
This is very cool work! I have a quick follow-up: in the biomarker prediction task, what horizon (ie. how far into the future) did you set for the predictions? Prediction is hard beyond an hour, so it'd be impressive if your model handles that.
The prediction task is set up as predicting the next measured biomarkers based on a week of wearable data. So it's not necessarily predicting into the future, but predicting dataset Y given dataset X.
The specific biomarkers being predicted are the ones most relevant to heart health, like cholesterol or HbA1c. These tend to be more stable from hour to hour -- they may vary on a timescale of weeks as you modify your diet or take medications.
I think the reason that English speakers swap ie/ei is that the pronunciations of these is not really consistent in English (at least in the American accent I speak), and I can't think of any words where both orderings exist but have different meanings. So the general impression I have about this is that I know there are supposed to be rules about it, but it seems pretty arbitrary and unimportant semantically.
Right, we truly don't have a strong rule about differentiating these in the standard American dialect! Most people say STINE for this one, but if you say STEEN, nobody is gonna be confused or tell you that it's wrong.
So, yes, for the past couple weeks it has felt that way to me. But it seems to come in fits and starts. Maybe that will stop being the case, but that's how it's felt to me for awhile.
Yep! The biggest win for me when AWS came out was that I could self-serve what I needed and put it on a credit card, rather than filing a ticket and waiting some number of days / weeks / months to get a new VM approved and deployed.
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