ruaraidh

Ah, that's extremely cool! I'm enjoying hacking around with ESP32s etc at the moment (maybe it's just nice to do something physical that an AI can't) and it's amazing how mature the hardware ecosystem around them is. I'd love to hear more, both about the tech and the economics.

Oh, that's such a good idea! I suppose the challenge is knowing where there are installable surfaces are (or at least making defensible guesses). I'm going to have a go at this...

Good idea. I want to add specific options for different mounting locations (sheds etc) as well.

Absolutely! I have some other datasets that might be useful too (e.g. air quality). Drop me a line: ruaraidh[at]southlondonscientific.com :)

Thanks - I didn't know about Inspire Index, I'll check it out. I tend to agree about false precision. My first instinct was to use the synthetic horizon for addresses in that group, but I think that's over positive. A range might be better (if a bit more complex)?

Thanks! Should be doable, I just got excited by the new shiny thing first.

Opus keeps pointing out (in a fashion that could be construed as exasperated) that what it's working on is "obviously not malware" several times in a Cowork response, so I suspect the system prompt could use some tuning...

Aeolus, a Python library to provide a unified interface for air quality data from sources around the world: https://github.com/southlondonscientific/aeolus

Air quality data is now very widely available, but managing access to multiple networks is a massive task (lots of shonky APIs out there - the EEA has a csv endpoint that actually returns a .zip with mimetype "text/html", just to give you a flavour). Integrating new APIs could be a full-time job, but it's something AI can do very well given a pattern.

This is really for me as I build out my company working on turning air quality data into actionable information, but it's open source and freely available.

"high-risk, high-reward" science sounds great, until you suddenly have a replication crisis on your hands. Big, exciting results already attract more attention than good, careful, journeyman science, despite generally being, well, wrong.

thanks - do you think there's anything obvious I might be missing coming from an academic background in terms of getting attention? I don't know how useful side projects really are, for instance (particularly vs things I've done as part of my research).

I'm in a similar place to the OP - I have a PhD in health science with R and Python experience (and a little hobby experience with TensorFlow and some AWS stuff). Is it worth doing kaggle competitions? I've heard mixed things - some people say companies ignore them, others say they're useful evidence on a CV. Thanks!

Oh no, a trend! I bet it carries on forever no matter what!!

How could Merck stall those studies? Ivermectin is widely available, you don't need to get Merck's permission to use it in a study.

This isn't quite true - Scotland's income tax rates are slightly different from the rest of the UK's, so residence inside or outside Scotland is relevant.

That looks interesting, thank you. I'm an inveterate tinkerer too...

Thanks - Andrew Huang sounds like a fascinating guy, I'll check this out.