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Foundational models can work where so far „needs human intuition“ was the state of things. I can picture a time series model with large enough Training corpus being able to deal quite well with typical quirks of seasonalities, shocks, outliers, etc.

I fully agree regarding how things have been so far, but I’m excited to see practitioners try out models such as the one presented here — it might just work.


Well said! To add a few options

- energy management (shifting loads to times when energy is cheap) for consumer/commercial/industrial use cases

- energy markets, especially power trading: often highly algorithmic, and driven by models that turn fundamentals data (weather, calendar, …) into supply/demand predictions, and from there into price predictions

- retail pricing, both offline and e-commerce


Zcal requires the following permission for Google Calender, Cal.com doesn’t:

“… permanently delete all the calendars you can access using Google Calendar”

Granting that takes a lot of trust in Zcal.


Switch to a German QWERTZ keyboard. That’ll also help with giving rusty muscle memory a fresh kick.


Zooming in on the lower basin of the pumped storage reservoir switches the map from showing the filled basin to an empty basin!



Common, this is genius. Plot reads: in 2154 a soldier from the future is send back in time to the present.

Why send a soldier from the present to the past when you can also have a soldier from the future send to the past!


The same applies within large companies. If you’re within a business team, and you’re requesting work from a design team/engineering team/data science team, you’ll face the same issues with scope creep + churn + competing priorities etc. I wouldn’t blame agencies for being bad, this is people being people plus a bit of other things. Anticipating and steering around/against these dynamics has been one of my biggest career learnings over the last years. The author has some good suggestions for how to do it — if you work in a large company, take another look and ask yourself if they don’t also apply to your work!


https://m.xkcd.com/2606/ — mystery solved!


At least in e-commerce it’s very much possible. The company that I work for does such experiments regularly (there’s a dedicated Data Science team for measurement) and I’ve personally been involved in lift studies for Google Ads. They work, you just have to be careful with the ‘how much’ combined with ‘for what’.

Happy to chat with anyone who is interested in the topic (pfalke at pfalke dot com).

Haven’t had a chance to listen to the podcast, apologies if that made me miss the point of the parent post!


Note that it’s a clearance sale within ‘certified refurbished’, so these are not entirely new devices.


These look to be new. Refurbished listings clearly indicate that, for example, the listing "Refurbished iPhone 7 128GB - Black (Unlocked)" [1], and it goes on to describe details about what a refurbished phone from Apple entails — all things that are missing from the iPhone SE page. My guess is Apple doesn't normally sell new "clearance" products, so they decided to just add it to the existing discount/refurbished store.

[1]: https://www.apple.com/shop/product/FN8L2/Refurbished-iPhone-...


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