September 3, 2025 • 43 mins
This week, we’re turning our attention to our weather apps. Are they getting worse? Are Trump’s DOGE cuts to blame? Why don’t we use feels like as the default temp? And what does the percentage of precipitation (PoP) actually mean?
Then, we answer a listener's question on how Spotify’s shuffle feature actually works.
Check out Heather McCalden’s “The Observable Universe.”
The last track in this week’s episode is from Certain Self’s “Girl in the Walls” from his new EP You Look Tired.
Have a question you want us to answer? Email us at mannynoahdevan@gmail.com or leave a voicemail at (860) 325-0286
For more, subscribe to our newsletter at www.nosuchthing.show and follow us on Instagram.
See omnystudio.com/listener for privacy information.
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:00):
I'm Manny, I'm Noah.
Speaker 2 (00:01):
This is Steven and this is no such thing.
Speaker 3 (00:03):
The show where we settle our dumb arguments and yours
by actually doing the research. On today's episode, are the
weather apps actually getting worse? And how random is Spotify's
shuffle feature?
Speaker 2 (00:16):
Really?
Speaker 1 (00:19):
I no, there's no no such thing, no such thing,
no such thing.
Speaker 4 (00:28):
Such those such.
Speaker 3 (00:38):
So as the weather seems to be getting more and
more unpredictable. With flash flooding overtaking our subways in New
York City, you.
Speaker 5 (00:47):
Can see water spouting out of the walls, people walking
across flooded platforms, and that rather dirty water.
Speaker 3 (00:54):
To the tragic, deadly floods in central Texas, we.
Speaker 5 (00:57):
Confirm that at least one hundred and thirty five people
have been killed. This is in addition to the more
than one hundred people who remain missing across several counties.
Speaker 3 (01:07):
This week, we are turning our attention to our weather
apps and then nottedly. Many people in our lives have
been complaining that the weather apps are just not accurate anymore.
So we're going to get to the bottom of that
and answer all your other questions about weather apps, including
this one from my friend.
Speaker 6 (01:26):
Alek Okay, So I want to know why we as
society can't come together and use feels like temperatures instead
of actual temperatures as the default, because right now, when
they tell me it's going to be sixty five degrees,
I then have to go and do other homework on
like how wendy is it going to be that day?
And is it going to be really humid? And it
(01:47):
seems like they have come up with one number that
can give me the answer that I'm really looking for here,
which is do I need a jacket that day?
Speaker 1 (01:55):
That's a great question, and it reminds me of when
you guys used to make fun of me for saying
that it feels quote unquote good outside like it feels good,
and I maintain that many people say that maybe not
from you know, Connecticut or Long Island, but I just
wanted to get that off my chest.
Speaker 4 (02:17):
Yeah.
Speaker 2 (02:17):
I love the idea of like a Manny weather app that.
Speaker 3 (02:21):
It just says feels good.
Speaker 1 (02:27):
That's all I need to know. But just to be
clear that the feels like it's a part of the app,
I actually don't see it that often. It's a part
is on the weather? Which app is.
Speaker 3 (02:36):
This all the apps kind of have it now, so
there's usually app okay.
Speaker 7 (02:40):
And they don't really have much.
Speaker 2 (02:42):
So like, let's pull it up now.
Speaker 3 (02:44):
It's usually you know, the air temp which is up top,
and then feels like it's usually below that or very deeper.
So like this, this is the default Apple one. You
really got to scroll here. So it's eighty seven right
now from Brooklyn, and then we scroll all the way down,
feels like ninety one.
Speaker 1 (03:04):
Okay, that is helpful. It's helpful, but I guess I
do also want to know the real temperature. That's my
guess is that y'all do you want to know the
real temp?
Speaker 3 (03:14):
I don't care about the real temperature. It doesn't matter.
This was especially true remember when we had those really
really windy days.
Speaker 7 (03:21):
Oh yeah, it's it's yeah, it's in the hot weather.
It's less of a thing I feel for us anymore.
Speaker 2 (03:26):
I mean, well, no, still thing, because.
Speaker 7 (03:28):
It can be in the cold. I feel like that
the wind can change it so dramatic.
Speaker 3 (03:32):
They'll be like it's forty degrees out. I'm like, oh,
this is kind of warm for winter. And then it
was like the wind is making it feel like it's
fifteen exactly.
Speaker 7 (03:40):
But it's like I don't hear what the still cold day? Okay,
I can live in this versus like I'm about to
die in.
Speaker 3 (03:46):
Free because why are we checking the weather? You're checking
them whether to see what I gotta wear.
Speaker 1 (03:52):
Yeah, it's true for me, it's.
Speaker 3 (03:56):
We're not you know, like that's what most people checking
other while they're checking the letter.
Speaker 1 (04:01):
I might wear something different in sixty five degree weather
than you might. Yeah, I mean you still need to
know what to wear for you. That's why I want
to know the real temperature. Yeah, the real temperature is
the real temperature, no matter who well look.
Speaker 2 (04:13):
At no, no, but no.
Speaker 7 (04:14):
But if it's all right it's sixty degrees but feels
like ninety, yes, you you want to dress for ninety yes, yeah, but.
Speaker 3 (04:20):
It feels like it's actually better for you because you
want to know what it's going to feel like, so
you should know what to wear.
Speaker 2 (04:26):
The actual temperature doesn't know what.
Speaker 7 (04:28):
It actually make a difference if it's actually if it's
actually sixty.
Speaker 1 (04:30):
Yeah, because I want to know if we've breaken up
broken any records today.
Speaker 3 (04:35):
Okay, okay, that's different, that's what you're wearing, breaking records is.
Speaker 7 (04:42):
Over here.
Speaker 3 (04:45):
I have a map the historical data. I want to
make sure that you can assume we.
Speaker 7 (04:49):
Are breaking a record every day.
Speaker 3 (04:58):
So I talked to meteorologists at the Weather Company, okay,
which you may know for the Weather Channel brand.
Speaker 4 (05:07):
So my name is doctor Loriana Gaudett. I am a
staff applied meteorological scientist at the Weather Company. I've worked
primarily within our forecast sciences team, so I'm not operationally
forecasting the weather, but i am working on the science
behind the scenes per se to improve our forecasts.
Speaker 3 (05:26):
All right, So one of the first questions that we
got on weather is this idea of real feel. Right,
you get our actual temperature and then we get the
real field tamp. So can we start by just having
you break down what is the real field tamp? How
is that calculated?
Speaker 4 (05:42):
So the real field temperature is more so known as
the feels like temperature, and what it's composed of is
a combination of the heat index and the windshiel temperature.
The heat index itself is a combination of the temperature
and the relative humidity and how those two interchangeably play
(06:04):
together to affect how we feel when we're outside. And
so if it's really humid outside, less of that sweat
is able to evaporate off of our bodies, and we
are less effectively cooling ourselves down, which makes it feel
much warmer. As an example, if it's ninety degrees fahrenheit
outside with a relative humidity of seventy percent, the heat
(06:27):
index or the feels like temperature in those conditions is
around one hundred and five degrees fahrenheit, which is very
warm and it can become dangerous with heat stress impacts.
And then on the other side of the temperature spectrum,
the feels like kind of points to the wind chill temperature.
So this time we're considering the effect of wind speed
(06:47):
on temperature, and the reason that that's important is because
the wind is effectively transporting the heat away from our body,
which is cooling down our body or our skin temperature,
and then over time core body temperature, which can also
come dangerous in its own way through frostbite or through
hypothermia effects. So another example here is that if you
(07:10):
have a temperature of thirty degrees fahrenheit and a wind
speed of fifteen miles an hour, your wind chill or
feels like temperature would be nineteen degrees fahrenheit, give or take.
Speaker 1 (07:30):
Interesting. So basically like seventy degrees with no humidity feels
a lot different than seventy degrees with humidity, and some
you know, X amount of miles per hour.
Speaker 3 (07:39):
Wind exactly to take away I got from talking to her,
it was like, the feels like takes into account how
the temperature affects our body, and it's not just all right,
here's the air temp.
Speaker 7 (07:52):
Yeah, it's not so abstract. I guess it's like kind
of an algorithm basically. Yeah, and yeah, it's about how
you know your sweat's going to be a vap rated
or not or whatever. That's it's more thorough than I thought.
Speaker 1 (08:04):
Yeah, I kind of thought. A scientist and a lab
coat walked outside, I was.
Speaker 7 (08:08):
Like, okay, sixty five, Yeah, we wan'll make them feel
good today.
Speaker 3 (08:14):
But something is interesting about feels like, which we talked
about a little bit already, is that someone like manny,
if it's seventy tract Yeah, if it's seventy seven degrees outside,
man's complaining as hot?
Speaker 1 (08:29):
Yeah, well, not that it's hot, it's too It's like,
does it's warmer than I prefer?
Speaker 7 (08:34):
It feels bad?
Speaker 3 (08:35):
It feels bad, bad, whereas with us that's seventy seven
temperature bad, not bad.
Speaker 7 (08:41):
So well, I.
Speaker 2 (08:43):
Don't know, Okay, don't put that on that.
Speaker 3 (08:44):
Okay, what do you what's what seventy seven.
Speaker 7 (08:46):
Of you that could get pretty hot?
Speaker 2 (08:48):
Okay?
Speaker 7 (08:48):
Seventy three is comfy, okay, And I want a little
bit of clouds.
Speaker 3 (08:53):
And speaking of shade versus sun, when they take the
temperature outside, did you know that they're taking into shade?
Speaker 1 (09:00):
Whoa, oh, why is that?
Speaker 7 (09:03):
I did not know.
Speaker 2 (09:04):
Well, we're gonna find out.
Speaker 4 (09:06):
So temperature and heat index are recorded in the shade,
and so if oh, yeah, what I know, Yes, it's
it's super surprising. But the reason is we don't want
the thermometer that's measuring the temperature to heat up and
not be able to actually tell us what the air
temperature is. We want that not the temperature with the thermometer.
(09:29):
So when you were outside doing whatever activity in the sun,
it's going to feel warmer than the reported air temperature
and even the heat index. So that's important to keep
in mind. And then the other component is that scientists
when they were deriving these heat indices and the wind chill,
they had to make certain approximations and assumptions. So some
(09:52):
of those involve like how tall are you, how much
do you weigh, how much do you sweat, what kind
of clothes are you're wearing, so on and so forth,
and that obviously can't account for each of our unique
body compositions and circumstances, but it's our best general approximation
for what you might experience when you walk out the door.
Speaker 3 (10:15):
The shade thing really blew my mind because I'm every
time I go outside and I'm in the shade, I'm like, oh,
this is cooler than what's the app because I'm in
the shade. But that's the best case scenario. You're only
going to get hotter if it's ninety degrees. That means
if you're in the sun, it's hotter than that.
Speaker 7 (10:34):
Yeah. Yeah, if you're walking around, you're gonna be sucked
there sometimes.
Speaker 3 (10:39):
Yeah.
Speaker 1 (10:40):
There's so many variables, which is why I just try
to distill them all into good or bad. Yeah.
Speaker 3 (10:46):
I know.
Speaker 7 (10:46):
Now I'm kind of like many systems well because it's like, well,
this makes me want them to have shade, Yeah, because
I need to know.
Speaker 1 (10:56):
You open the weather app. It's just like an Excel
sph Yeah.
Speaker 7 (10:59):
Ever dial in and then you dial in your outfit yeah,
and your skin tone calculator.
Speaker 8 (11:06):
I mean.
Speaker 7 (11:09):
Imagine you have an or a ring.
Speaker 2 (11:10):
Yeah, that's that's a lot of data.
Speaker 8 (11:13):
Yeah.
Speaker 7 (11:14):
And then you didn't tell weather you take a selfie
it skins your clothes.
Speaker 3 (11:17):
You don't even need to take a selfie, yeah, right,
your phone.
Speaker 1 (11:20):
Yeah, and and you just exists and Peter Tiel buys
all of this data. Yeah, exactly, and then he but
he tells you it's going to be only seventy two today,
or it's only going to feel like seventy.
Speaker 3 (11:29):
Two when we look at the weather app. Besides many
lying and saying he wants to look at historical data,
we're looking at it to see what we should wear
to go outside. Do I need to bring an umbrella?
Why don't we have this as the default? If I
open up the app, I want to see feels like
and then if I want to search for the actual
air temp, I could find that lower down.
Speaker 2 (11:47):
Why are we doing the reverse?
Speaker 4 (11:52):
We take a certain approach within our Weather Channel app,
and we've supply both temperatures, so not just the temperature,
but also the fields like temperature, so that you can
have both data points and use those to make decisions
about how you want to plan your day or go
about your day. But yeah, it's a really good question
because it gets down to the core of like the
(12:14):
human experience of course, but there's other impacts and implications
of the temperature that also don't relate to humans. So
some of those might be like if a farmer, for example,
is trying to figure out when to plant their crop
or protect their crop, they are looking at the actual temperature.
(12:36):
So that's what our weather forecast models are actually outputting,
is the real air temperature, and that's what we are
measuring and looking at when we are monitoring the climate,
for example, and it allows us to compare the observations
of temperature across the country, across the world, and over time. Whereas,
(12:58):
like with everything that we talked about with the feels
like temperature, there's a lot of approximations that are being used,
and so some weather services given the same environmental setup
might come up with different values. That makes it really
difficult for that inter comparison component. But if you yourself,
as the user of a weather app find that the
(13:21):
feels like temperature gives you everything that you need to
know you can set that as your own to fault temperature.
But we just don't want to make that assumption for
all of our users.
Speaker 2 (13:32):
So they're just very considerate of me at this point.
Speaker 3 (13:36):
The thing that impacts most of us would be it
feels like temperature that should be to default. Make everybody
else look for the other stuff.
Speaker 1 (13:45):
Please contact your company.
Speaker 3 (13:47):
I guess do you feel I understand all everything that
she said. When I'm looking at my app, I'm not
looking for climate. You know, over the last ten years,
I don't have any crops. I guess I can do
it within my own but I feel like as a
country we need to we neither do real feel I'm
not saying get rid of the real number. I'm just
saying put that lower.
Speaker 7 (14:08):
See why I can't?
Speaker 1 (14:10):
Why not just side by side?
Speaker 7 (14:11):
Oh slash yeah, pretty much? Okay, big one, I mean,
pick whichever one you want, big one then basically an
arrow or slash, Yeah, basically, and you just know that
you just know, okay, this is you know, you know
the real thing. And here's the thing. It wouldn't take
up that much space. They're digits and like the numbers
are hopefully not Yeah, yeah, hopefully four digits. You're look
(14:35):
at that total.
Speaker 1 (14:36):
I'm fine with the normal one. I'm fine with the
regular Yeah.
Speaker 7 (14:39):
My main concern with these things is doing an umbrella
or not, which I forget to look for most of
the time.
Speaker 3 (14:44):
Anymore, Speaking of rain, Manny, do you want to explain
this Twitter argument that people have been having.
Speaker 1 (14:54):
When it's raining or when it might looks like it
might rain. You go on the app and says, let's say,
for example, thirty five percent. Doesn't say what the thirty
five percent is. So some people think that it's thirty
five percent chance it's going to rain where you're standing.
Other people think it's in a rain on thirty five
percent of the area around you, yes, and a third
(15:19):
group of people think that's effectively the same thing.
Speaker 7 (15:23):
Well, well what do you what do you guys think?
Speaker 3 (15:26):
Before I did this interview, Yeah, my thought was it's
a thirty five percent chance it's going to rain.
Speaker 7 (15:32):
That's not that's my thought too, because the area doesn't
make because it's like, well, what's what area?
Speaker 1 (15:36):
A lot of people, a lot of people online, these
are not experts, but they were like, no, that is
what they're telling you. Well, what did you think I
was thought, thirty five percent chance is going to rain.
Speaker 7 (15:44):
Chance, Yeah, where you are? I agree, That's what I'm
looking at, and that's how I feel like my lived
experience is.
Speaker 1 (15:51):
You know, it's like, well, that's where it gets tricky.
It's it's never right to me.
Speaker 7 (15:56):
But yeah, I always assumed it's basically there is a
percentage of chance that it will rain. Not whatever area
I'm taking in, it's going to cover sixty percent of this.
Speaker 3 (16:07):
Yeah, yeah, that.
Speaker 7 (16:08):
Doesn't really that would never cross our behind me that much.
Speaker 4 (16:12):
So this is a common area of confusion for users,
and even in our own field of meteorology, it can
be really hard to pinpoint what we are actually communicating
with probability of precipitation or pop In my experience and
knowledge base, I think both of those interpretations are true
(16:33):
in their own right, which of course makes it even
more confusing. But I can I can share from the
Weather Channel app perspective, the way that we're communicating it
is really in your surrounding area. What is the likelihood
of precipitation or of rain impacting you within whatever time
(16:55):
frame you're interested in, So, whether it's today, the next hour,
fifteen minutes, and less so the amount of area that
is going to be impacted. A couple misconceptions that also
come up to are about the intensity of precipitation from pop.
So sometimes people will see like, oh, there's a ninety
(17:17):
percent chance of rain, it's going to be a downpour.
We are indicating the likelihood of rain, of measurable rain,
any rain, and so generally it has nothing to do
with the intensity of that precipitation, which is important to
keep in mind. And then the other component two is
you know, sometimes people think, Okay, there's a fifty percent
(17:39):
chance of pop in New York City, so only half
of the day will be wet, And again you could
have that fifty percent chance, there might only be a
couple hours that are impacted, and then you have a
beautiful remainder of the day. I think that it really
lays on us meteorologists to make sure that we're communicating
what that likely hood and probability means, especially if it
(18:03):
does vary, you know, from a news broadcaster or a
meteorologist who's on TV trying to communicate the likelihood of rain,
to a push alert from your weather app telling you
that there's rain coming within the next thirty minutes.
Speaker 1 (18:17):
That is really funny because the idea that the percent
chance is the total coverage is such a like, actually,
I'm smart, and this is what they're really saying, fools
in conversation. I've heard it so many times and it's
just wrong.
Speaker 7 (18:32):
And then I mean it's kind of fascinating, like people
are just taking this and twisting it in any direction
where it's like, okay, ten percent, that's ten percent of
the day. Yeah, so yeah, okay, so whatever ten percent
of twenty four hours is. It's like, yeah, thinking of
all the different ways to map it out, it's kind
of that's actually like a SAT question or something.
Speaker 1 (18:51):
Yeah, I heard the rain is only coming from ten
percent of the clouds.
Speaker 2 (18:54):
Yeah, exactly. Yeah, but there.
Speaker 3 (18:57):
I thought it was interesting that they're kind of they
could both be right depending on your weather.
Speaker 7 (19:02):
You need explanation.
Speaker 2 (19:03):
I mean, I think that you know, the apps that
we're looking at or telling us, I.
Speaker 7 (19:07):
Would hope so frankly, I think that you know, maybe
they could do a better job than of getting the
word out there hopefully. You know, we're going to do
our part today with our you know, hundreds of thousands
of listeners.
Speaker 3 (19:22):
Some would say millions, So yeah, you could argue that millions.
Maybe all right, we are going to take a quick
break and when we get back, we are going to
find out are the weather apps actually getting worse? And
it's Trump to blame. So in last week's mail bag,
(19:51):
we were debating who gets the right of way between
a school bus with the stop sign out, an ambulance
with the siren on, and a funeral procession, and from
North Carolina called in with this insight.
Speaker 9 (20:02):
I am a state wide law enforcement officer here in
North Carolina. If I am running lights and sirens, I
am allowed.
Speaker 10 (20:13):
To go through a soft sign.
Speaker 9 (20:15):
People legally have to yield to me. But if I
run a soft sign and hit someone, it is still
my fault, and I, as a driver, would.
Speaker 10 (20:26):
Not only be responsible to my agency, but I could
also be personally legally financially responsible funeral processions.
Speaker 9 (20:37):
You guys are correct, that is a courtesy.
Speaker 10 (20:39):
It's not a legal thing, and but it is it
drive me.
Speaker 9 (20:41):
Grace to get down here in the South, they love
them at funeral profession.
Speaker 3 (20:45):
So we're going to try to include more listener responses
to our episodes like this in the future, So you
can email us at Manny Noladevin at gmail dot com
or leave us a voicemail at the number in our
show notes and hey, you may includes you in a
future episode. All right, let's get back to the show.
(21:08):
We do have one final weather question from Mia.
Speaker 11 (21:15):
I have a question, and this is just bugging me
so much, especially this summer, our weather apps getting less
accurate because I feel like they are like these days,
I just don't feel like I can trust the weather
app to tell me exactly what's going to happen, specifically
(21:37):
if it's going to rain, like over this the course
of the summer, It's been a rainy summer in New York,
and now I feel like I'm becoming this kind of
like medieval farmer where I'm sort of gauging what's happening
in the sky, and I feel like I am becoming
a much better predictor of what's actually going to happen
than what my phone is telling me. I'm just starting
to be like, what's big weather even there? So if
(22:02):
you could answer for me, is it getting less accurate?
What's the reason should I trust this app anymore?
Speaker 4 (22:09):
That would be great. Thank you.
Speaker 2 (22:14):
I put this question to our meteorologists from the weather.
Speaker 7 (22:17):
She was sweam bullets at this one.
Speaker 3 (22:19):
Is the weather more unpredictable or is this on in
our heads? And you know, has nothing really changed.
Speaker 4 (22:26):
So that's a super intriguing question. No, as a short answer,
the aps are not getting worse at predicting rain. I
think there's a there's a few different things at play here,
one of which you pointed at. It's psychological, so we're
more likely to remember when the weather apps get it wrong,
versus that when we are seeing a prediction of, oh,
(22:49):
it's going to rain in ten minutes, let me make
sure I grab my rain jacket before I go out,
and it ends up being correct, and so you're prepared.
It becomes a non event and you forget about it.
But let's say that if someone is outside it starts pouring,
there's absolutely no warning. Of course, you're going to be
annoyed that you had no notice that that was going
(23:09):
to happen. And there's a few reasons that those specific
circumstances can crop up. This example of stepping outside and
being met with an unexpected downpour of rain can largely
happen because even if the radar is able to pick
(23:30):
up on a storm let's say it just started raining
within the past few minutes. The weather radar just got
that data. So for example, that data needs to be
ingested into our back end systems or any other weather
app systems to process that data, figure out where that
storm is using proprietary software and algorithms, predict where that
(23:51):
storm is headed and if it will intensify or again dissipate,
get that forecast to our technology system downstream, and then
those then deliver that information to your app. So that
takes a bit of wall clock time for all of
that process to happen. So unfortunately, there are those situations
(24:12):
where rain can impact you within that window of time
that we're trying to get that information to you as
fast as possible, but perhaps missed that window of opportunity.
Speaker 1 (24:24):
I've never thought about a situation where the weather app
was wrong, because it just was like trying to catch
up to the real time m hm. You would think.
Speaker 3 (24:34):
You're supposed to be predicting.
Speaker 7 (24:37):
It's like, don't blame me, I'm not the weather company.
Speaker 3 (24:41):
Hello.
Speaker 1 (24:41):
And now that I remember what Mia was complaining about,
I think her concern is that the apps are more
frequently incorrect, not that they're ever incorrect, but that it's
happening more often.
Speaker 3 (24:55):
Yes, yeah, she thinks it's getting the apps are getting worse.
So the Weather Company said, no, that's you know, the
apps aren't getting worse, but they work at the Weather Company.
So I wanted to call up someone who can maybe
speak a bit more freely about whether or not these
apps are getting worse and do we need to worry,
(25:16):
especially with these Doge cuts, about them being worse in
the future.
Speaker 2 (25:18):
So I called the Mary Glacken.
Speaker 12 (25:21):
I'm the former deputy undersecretary for NOAH for operations there
and I had worked for Noah for just about thirty
five years. I also am on an AI board and
things like that, so today these days I'm mainly in
an advisory capacity.
Speaker 2 (25:40):
She also worked at the Weather Company back in the day.
Speaker 3 (25:43):
All right, great.
Speaker 7 (25:45):
Job finding, Mary.
Speaker 1 (25:46):
I always have stuff to say about my former employees.
It should be good to take.
Speaker 3 (25:52):
So, you know, the big thing that we were concerned
about and seeing the headlines is that with the Doge cuts,
that there's just less people working, which is why the
apps are getting worse. So I asked Mary, like, where
do we currently stand.
Speaker 12 (26:06):
So let me talk about it in terms of personnel first,
because one of the major things that happened was the exodus.
You know, there was the Fork in the.
Speaker 13 (26:15):
Road, another unprecedented action from the Trump administration, offering more
than two million federal workers a buyout. The workers receiving
an email with the subject line fork in the Road,
giving them a choice resign and be paid through September
or risk being fired.
Speaker 12 (26:31):
So the National Weather Service and all of Noah are
down significantly in terms of people, something approaching as much
as twenty percent in some locations. So the National Weather
Service did lose a lot of senior people. In terms
of funding, they had actually been funded by Congress last
(26:52):
year for a full year at a reasonable level, but
the Trump administration has delayed spending of those funds, so
they've really been slow rolling contracts to go out for
various things. I don't have any figures here, but it's
safe to assume there's a fair amount of money still
on the table. But the direction that we saw this
(27:14):
week was a spend plan for the rest of this
fiscal year is clawing back that money. It's saying you're
not allowed to spend that anymore. So that's that's kind
of where we are. We've seen some news about them hiring.
Speaker 5 (27:29):
So earlier this year, President Trump's Doze Office cut more
than five hundred Weather employees. Well now WS says they're
going to hire four hundred and fifty meteorologists, hydrologists, and
radar technicians.
Speaker 12 (27:41):
They're all entry level positions, So you know you're not
going to hire back the expertise that's been lost that
one of the senior officials that Noah career official had quoted,
that was twenty seven thousand years of expertise that walked
out the door between February in April.
Speaker 3 (28:02):
It's just going to be a lot of people, a
lot of college who don't have the expertise, and now
they also don't have the leadership to mentor those people.
Speaker 1 (28:10):
They just went from an NFL team to like JV
high school team.
Speaker 3 (28:18):
So I wanted Mary to answer the question because I
couldn't take the Weather Company's word for it.
Speaker 2 (28:22):
Are the apps getting worse?
Speaker 12 (28:24):
The apps haven't degraded at all. I think the worries
that we have with the Trump administration is if they
move forward with their plans. Literally one of the plans
is to eliminate all of Noah's research arm So and
that's all our future. You know, how do we do
a better job in forecasting that whole section would be eliminated?
(28:49):
Which is crazy, but that's the plan. I do want
to kind of remind folks that Congress has a say
in this whole thing. Both the House and Senate that
have given their plan on how Noah would be funded.
The Senate does a really pretty good job and keeping
Noah funded. The House has some significant cuts in it,
(29:12):
but nothing as draconian as the administration is asking for.
I think the problem is that Congress won't be able
to agree on the overall spending, which will really basically
allow the Trump administration to continue to do what it
wants to do.
Speaker 3 (29:29):
I was curious if we've kind of hit the point
of no return, you know, see if President Biden runs
again and.
Speaker 2 (29:37):
Wins, yeah, you know which, we're in eight can we
get back to where we were?
Speaker 12 (29:52):
One of the things I'd like to make clear with
you know, all of my experience with Noah, both in
Noah and then working with Noah outside is I would
not be the one arguing for status quo. You know,
I'm not here saying oh, it was a perfect world
and now this has happened. I think it will be
possible to get us back if we can keep people
in the field and interested in our problems. You know,
(30:14):
you have the whole advent of AI, and AI is
impacting our value chain all the way from taking an
observation to helping somebody make a decision on what to
do that day. So when I look at AI, it's
pretty clear to me and many is the private sector
(30:35):
is ahead of where Noah is with AI, and that's
no great chock. You know, the private sector can pay
somebody a half a million a year because they're a
genius in AI. You know, if you go to nowhere,
you're going to be making ninety thousand. So that says
to me that we need to find a way to
work with the private sector that meets public good. You know,
(30:57):
how do we ensure that everybody gets warning that they
need and how do we ensure people get the basic information.
Speaker 3 (31:05):
To do that?
Speaker 12 (31:06):
So I do think we need to think more creatively
about public private partnerships. You know, right now, there's not
a lot of mechanisms for the private sector to work
with the government sector. And I think if we you know,
if we started rebuilding tomorrow I think that's what we
would be thinking about, Well, what really makes sense here
(31:27):
to do in the future.
Speaker 1 (31:32):
Yeah, I'm like, I'm like your run of the mill
kind of lefty, right, who has some skepticism about what
the private sector can do, like for the good of
the people, quote unquote. But it's interesting how this situation
is so die or that, like we do just kind
of have to look at it.
Speaker 10 (31:47):
All.
Speaker 3 (31:47):
Right, So we've been, you know, trying to make sense
of the world through weather. We're going to take a
little break. I'm going to leave you guys, and then
we're going to try to make sense of the world
through Spotify's shuffle feature. This is Devin, Welcome back to
(32:13):
no such thing. A few weeks ago, actually, we got
an email from this listener named Austin. He wrote, after
listening to your latest mail Bag episode, it got me
thinking about an issue I've run into over the years.
Whenever I hit shuffle play for a music playlist on Spotify,
the shuffle feature never seems to be truly random. I
(32:36):
want to know how does shuffle work for music apps
and is it truly random.
Speaker 8 (32:45):
I'm Heather mccoldon and I'm a writer, artist, and sometimes
a tarot reader.
Speaker 3 (32:53):
All right, So I reached out because we got a
question from a listener who wanted to know if we
could figure out how Spotify's shuffle feature works. And I
came across your article in the Financial Times cool in
which you dive into this. Would you mind, I guess
kind of setting the scene for me on what happened
(33:16):
on December fifteenth, let's say, twenty twenty three.
Speaker 2 (33:20):
Yeah, when all these random acts started coming together.
Speaker 8 (33:26):
Yeah, basically, you know, it's like one of those things.
It's like holiday time, Brooklyn, like the aras chilled. Everyone's
excited and happy for like the end of the year
beginning of the new year. I was visiting some friends
at a dinner party in Williamsburg and you know, too
(33:47):
much wine, too much festivities, like everyone leaves on masks
to go home, and yeah, I was feeling like kind
of nostalgic, like I had a partner in that neighborhood, like,
and you know, it's just brought back memories and you know,
my friends were like, oh, like we opened the door
from the building, there's a cab right there. And I
(34:09):
was like great, Like the universe is working out for me.
So yeah, like I get in and I'm just like, oh,
like went into my liked songs on Spotify and just
hit shuffle and the song Maps by the yayaya As
comes on, which is, you know, like this epic contemporary
(34:29):
love song and basically like the taxi just like decides to,
you know, go a certain way and we literally like
pass my ex's building as like this song is about like,
you know, saying like you know, they don't love you
like I.
Speaker 4 (34:47):
Love you.
Speaker 8 (34:50):
Then, and I just had this moment of like, what
the fuck is happening, not just like in my life
in the universe, but like what is this app even? So, yeah,
that led me to investigate those Spotify shuffle works in
a very randabout way. But you know, like I think
(35:11):
that's like what's kind of cool about existing now like
in this era where it's like, how are these technologies
and their glitches affecting our emotions in our personal lives?
And you know, is it random? Is it all random?
Speaker 2 (35:30):
Et cetera.
Speaker 3 (35:31):
Can you talk me through I guess starting with the
first iteration of like what Spotify shuffle used to do.
Speaker 8 (35:43):
So basically there's like a very simple, elegant, mathematical description
of randomness. That's called the fisher Yates algorithm, and essentially
this is like from Spotify's insight option. Until somewhere between
twenty twelve and twenty fourteen, it was using fisher Yates
(36:06):
from my understanding from what's available fisher Yates basically, it's
an algorithm that will take an.
Speaker 4 (36:14):
Array of values.
Speaker 8 (36:15):
Let's say, like you have numbers one through ten, it
removes five, and five becomes the first track of the
shuffle list, and then it just keeps kind of you know,
randomly selecting the other values to like create a new list, Sasha,
So every number in that list has an equal chance
(36:38):
of appearing in the first position or the consequential one.
It's great because technically that's completely random.
Speaker 4 (36:48):
Right.
Speaker 8 (36:49):
So Spotify engineers were like, this is awesome. You can
do this in between like two or three lines of code,
Like it was so simple. They're like, yeah, we sold this,
and like kind of instantly the user complaints came in
of like my shuffle is broken. How is it possible
(37:09):
that you could screw this up? It's so simple?
Speaker 3 (37:11):
And the complaints were at the time because I remember
seeing them on social right would be like, how's you
know Spotify Shuffle Ranmouth. They're playing four Taylor Swift songs.
Speaker 8 (37:19):
In a row one hundred percent. But you can see,
like based on how I described how it works, Ye,
randomness does create clusters. So it creates like, you know,
clusters of songs from one album or clusters of songs
by the same artist, Like each of those songs has
the exact same chance of like emerging, right, there's nothing
(37:44):
preventing that. So what the engineers finally determined was users
were falling for what's called the Monte Carlo fallacy. So
this is basically a human perception of randomness that is
not correct, and it's named after this very specific event
(38:06):
that occurred August eighteenth, nineteen thirteen, at the famous Monte
Carlo casino. We're essentially at the roulette table. The ball
kept falling on black. It starts to create this weird
chaos and like sensation in the casino because people are
thinking like this isn't possible, right, like you know, two
(38:29):
or three times sure, then you get into like five
to ten times, you know, like whirl, like this is unusual.
So yeah, something's up, and people kind of come down
with this fever of just expecting like on the next
turn of the wheel, red, Red is going to happen.
(38:50):
Red is going to happen because Black has happened all
these times of beforehand, and that night all fell on
Black twenty six times in a row, which is just
like it's like an astronomical this happened, and yeah, it
just this whole situation pointed out that like it's also
(39:13):
called the gambler's fallacy. It's where you think previous events
somehow affect the current event unfolding. So that's why people
get angry at like the clusters of you know, Addison
Ray or whatever.
Speaker 3 (39:30):
Yeah, it's like, okay, you're already played in Addison Ray song.
Therefore their Monte Carlo Felas is like, yeah, it has
to be something different now because we've done Black so
many times in a row. We played the Addison and
Ray song, so therefore an X song cannot possibly be
diet pepsi, you.
Speaker 8 (39:45):
Know, right, and then like what the fuck it's diet pepsi?
Like are you kidding me? Right now? The engineers were like, oh, okay,
people are having like this weird issue, like they don't
realize what randomness actually is. So then they created essentially,
like it's not like a hybrid algorithm, but they created
(40:08):
a different one that mimics or attempts to mimic, the
human perception of randomness. So it does this way. It
takes your playlist and it's like, oh, like, I don't know,
You've got a lot of like Justin Bieber in here,
So it's spreading Justin Bieber and then with being Justin Bieber,
(40:30):
it's spreading out the albums.
Speaker 2 (40:32):
Yeah yeah, yeah, yeah.
Speaker 8 (40:33):
So it's called cluster breaking.
Speaker 3 (40:36):
Yeah, you're not going to get to Justin Bieber songs
within a ten track shuffle from Swag. We're going to
make sure if you get to Justin Bieber songs, you're
from completely different albums.
Speaker 4 (40:45):
One hundred percent.
Speaker 8 (40:46):
Yes. So that was their solution. And keep in mind,
like what I've just described with the cluster breaks, like
that's from twenty fourteen, so like we there's been nothing
really that they put out since then that describes what's
going on. People are now in this weird position where
they're like they want, like the Fisheryates algorithm to come
(41:10):
back as like an additional like like if shuffle could
have yet exactly, and they're like yeah, like I know
it's bad, but like it would just be preferable to
like whatever this weird thing is. Our human minds are
so small that, you know, we're only able to comprehend
(41:35):
like a very like tiny wedge.
Speaker 4 (41:37):
Of how the world functions.
Speaker 8 (41:39):
Right, So it could be that like we're seeing all
these things as random, but really it's like, oh, if
we could actually take a step back and like have
more of a bird's eye view, potentially these things could
be connected. And they just operate at a pace that's
so slow that like over the core of one human
(42:00):
lifetime it appears random, but over the course of like
five hundred human lifetimes, it's actually like one slow process
that makes perfect sense, but we're just not able to
comprehend it, right, So you know it just randomness is
so interesting because in a sense, it's undefinable. Yeah, right,
(42:24):
So that's why it's so interesting when you have something
that's essentially like a math problem where it's like, come on,
like I have like I don't know, thirty five hundred songs.
It can't like how how complicated could it possibly be?
And it turns out it's really complicated.
Speaker 2 (42:47):
HEATHERN. Mccalldon is the author of the Observable Universe. We're
going to put a link to it in our show.
Speaker 3 (42:52):
Notess Hews Hews her howss. No Such Thing is a
production of Kaleidoscope Content. Our executive producers are Kate Osborne
in Mangesh Hadi Cardur. The show was created by Many Fidel,
Noah Friedman and Me Devin Joseph. Our theme in credit
(43:13):
song or by Manny, mixing by Steve Bone. Additional music
for this episode by certain Self. Our guests this week
we're doctor Loriana Gaudette from the Weather Company, Mary Glacken
and Heather mccauden. Thanks to Alec Mia and Austin for
the questions. If you have something you want us to
get at the bottom of you can email us at
(43:33):
Manny Noah Devin at gmail dot com. Sign up for
our newsletter No Such Thing that shows We'll
Speaker 1 (43:40):
See you guys, those by those such things
I'm Manny, I'm Noah.
Speaker 2 (00:01):
This is Steven and this is no such thing.
Speaker 3 (00:03):
The show where we settle our dumb arguments and yours
by actually doing the research. On today's episode, are the
weather apps actually getting worse? And how random is Spotify's
shuffle feature?
Speaker 2 (00:16):
Really?
Speaker 1 (00:19):
I no, there's no no such thing, no such thing,
no such thing.
Speaker 4 (00:28):
Such those such.
Speaker 3 (00:38):
So as the weather seems to be getting more and
more unpredictable. With flash flooding overtaking our subways in New
York City, you.
Speaker 5 (00:47):
Can see water spouting out of the walls, people walking
across flooded platforms, and that rather dirty water.
Speaker 3 (00:54):
To the tragic, deadly floods in central Texas, we.
Speaker 5 (00:57):
Confirm that at least one hundred and thirty five people
have been killed. This is in addition to the more
than one hundred people who remain missing across several counties.
Speaker 3 (01:07):
This week, we are turning our attention to our weather
apps and then nottedly. Many people in our lives have
been complaining that the weather apps are just not accurate anymore.
So we're going to get to the bottom of that
and answer all your other questions about weather apps, including
this one from my friend.
Speaker 6 (01:26):
Alek Okay, So I want to know why we as
society can't come together and use feels like temperatures instead
of actual temperatures as the default, because right now, when
they tell me it's going to be sixty five degrees,
I then have to go and do other homework on
like how wendy is it going to be that day?
And is it going to be really humid? And it
(01:47):
seems like they have come up with one number that
can give me the answer that I'm really looking for here,
which is do I need a jacket that day?
Speaker 1 (01:55):
That's a great question, and it reminds me of when
you guys used to make fun of me for saying
that it feels quote unquote good outside like it feels good,
and I maintain that many people say that maybe not
from you know, Connecticut or Long Island, but I just
wanted to get that off my chest.
Speaker 4 (02:17):
Yeah.
Speaker 2 (02:17):
I love the idea of like a Manny weather app that.
Speaker 3 (02:21):
It just says feels good.
Speaker 1 (02:27):
That's all I need to know. But just to be
clear that the feels like it's a part of the app,
I actually don't see it that often. It's a part
is on the weather? Which app is.
Speaker 3 (02:36):
This all the apps kind of have it now, so
there's usually app okay.
Speaker 7 (02:40):
And they don't really have much.
Speaker 2 (02:42):
So like, let's pull it up now.
Speaker 3 (02:44):
It's usually you know, the air temp which is up top,
and then feels like it's usually below that or very deeper.
So like this, this is the default Apple one. You
really got to scroll here. So it's eighty seven right
now from Brooklyn, and then we scroll all the way down,
feels like ninety one.
Speaker 1 (03:04):
Okay, that is helpful. It's helpful, but I guess I
do also want to know the real temperature. That's my
guess is that y'all do you want to know the
real temp?
Speaker 3 (03:14):
I don't care about the real temperature. It doesn't matter.
This was especially true remember when we had those really
really windy days.
Speaker 7 (03:21):
Oh yeah, it's it's yeah, it's in the hot weather.
It's less of a thing I feel for us anymore.
Speaker 2 (03:26):
I mean, well, no, still thing, because.
Speaker 7 (03:28):
It can be in the cold. I feel like that
the wind can change it so dramatic.
Speaker 3 (03:32):
They'll be like it's forty degrees out. I'm like, oh,
this is kind of warm for winter. And then it
was like the wind is making it feel like it's
fifteen exactly.
Speaker 7 (03:40):
But it's like I don't hear what the still cold day? Okay,
I can live in this versus like I'm about to
die in.
Speaker 3 (03:46):
Free because why are we checking the weather? You're checking
them whether to see what I gotta wear.
Speaker 1 (03:52):
Yeah, it's true for me, it's.
Speaker 3 (03:56):
We're not you know, like that's what most people checking
other while they're checking the letter.
Speaker 1 (04:01):
I might wear something different in sixty five degree weather
than you might. Yeah, I mean you still need to
know what to wear for you. That's why I want
to know the real temperature. Yeah, the real temperature is
the real temperature, no matter who well look.
Speaker 2 (04:13):
At no, no, but no.
Speaker 7 (04:14):
But if it's all right it's sixty degrees but feels
like ninety, yes, you you want to dress for ninety yes, yeah, but.
Speaker 3 (04:20):
It feels like it's actually better for you because you
want to know what it's going to feel like, so
you should know what to wear.
Speaker 2 (04:26):
The actual temperature doesn't know what.
Speaker 7 (04:28):
It actually make a difference if it's actually if it's
actually sixty.
Speaker 1 (04:30):
Yeah, because I want to know if we've breaken up
broken any records today.
Speaker 3 (04:35):
Okay, okay, that's different, that's what you're wearing, breaking records is.
Speaker 7 (04:42):
Over here.
Speaker 3 (04:45):
I have a map the historical data. I want to
make sure that you can assume we.
Speaker 7 (04:49):
Are breaking a record every day.
Speaker 3 (04:58):
So I talked to meteorologists at the Weather Company, okay,
which you may know for the Weather Channel brand.
Speaker 4 (05:07):
So my name is doctor Loriana Gaudett. I am a
staff applied meteorological scientist at the Weather Company. I've worked
primarily within our forecast sciences team, so I'm not operationally
forecasting the weather, but i am working on the science
behind the scenes per se to improve our forecasts.
Speaker 3 (05:26):
All right, So one of the first questions that we
got on weather is this idea of real feel. Right,
you get our actual temperature and then we get the
real field tamp. So can we start by just having
you break down what is the real field tamp? How
is that calculated?
Speaker 4 (05:42):
So the real field temperature is more so known as
the feels like temperature, and what it's composed of is
a combination of the heat index and the windshiel temperature.
The heat index itself is a combination of the temperature
and the relative humidity and how those two interchangeably play
(06:04):
together to affect how we feel when we're outside. And
so if it's really humid outside, less of that sweat
is able to evaporate off of our bodies, and we
are less effectively cooling ourselves down, which makes it feel
much warmer. As an example, if it's ninety degrees fahrenheit
outside with a relative humidity of seventy percent, the heat
(06:27):
index or the feels like temperature in those conditions is
around one hundred and five degrees fahrenheit, which is very
warm and it can become dangerous with heat stress impacts.
And then on the other side of the temperature spectrum,
the feels like kind of points to the wind chill temperature.
So this time we're considering the effect of wind speed
(06:47):
on temperature, and the reason that that's important is because
the wind is effectively transporting the heat away from our body,
which is cooling down our body or our skin temperature,
and then over time core body temperature, which can also
come dangerous in its own way through frostbite or through
hypothermia effects. So another example here is that if you
(07:10):
have a temperature of thirty degrees fahrenheit and a wind
speed of fifteen miles an hour, your wind chill or
feels like temperature would be nineteen degrees fahrenheit, give or take.
Speaker 1 (07:30):
Interesting. So basically like seventy degrees with no humidity feels
a lot different than seventy degrees with humidity, and some
you know, X amount of miles per hour.
Speaker 3 (07:39):
Wind exactly to take away I got from talking to her,
it was like, the feels like takes into account how
the temperature affects our body, and it's not just all right,
here's the air temp.
Speaker 7 (07:52):
Yeah, it's not so abstract. I guess it's like kind
of an algorithm basically. Yeah, and yeah, it's about how
you know your sweat's going to be a vap rated
or not or whatever. That's it's more thorough than I thought.
Speaker 1 (08:04):
Yeah, I kind of thought. A scientist and a lab
coat walked outside, I was.
Speaker 7 (08:08):
Like, okay, sixty five, Yeah, we wan'll make them feel
good today.
Speaker 3 (08:14):
But something is interesting about feels like, which we talked
about a little bit already, is that someone like manny,
if it's seventy tract Yeah, if it's seventy seven degrees outside,
man's complaining as hot?
Speaker 1 (08:29):
Yeah, well, not that it's hot, it's too It's like,
does it's warmer than I prefer?
Speaker 7 (08:34):
It feels bad?
Speaker 3 (08:35):
It feels bad, bad, whereas with us that's seventy seven
temperature bad, not bad.
Speaker 7 (08:41):
So well, I.
Speaker 2 (08:43):
Don't know, Okay, don't put that on that.
Speaker 3 (08:44):
Okay, what do you what's what seventy seven.
Speaker 7 (08:46):
Of you that could get pretty hot?
Speaker 2 (08:48):
Okay?
Speaker 7 (08:48):
Seventy three is comfy, okay, And I want a little
bit of clouds.
Speaker 3 (08:53):
And speaking of shade versus sun, when they take the
temperature outside, did you know that they're taking into shade?
Speaker 1 (09:00):
Whoa, oh, why is that?
Speaker 7 (09:03):
I did not know.
Speaker 2 (09:04):
Well, we're gonna find out.
Speaker 4 (09:06):
So temperature and heat index are recorded in the shade,
and so if oh, yeah, what I know, Yes, it's
it's super surprising. But the reason is we don't want
the thermometer that's measuring the temperature to heat up and
not be able to actually tell us what the air
temperature is. We want that not the temperature with the thermometer.
(09:29):
So when you were outside doing whatever activity in the sun,
it's going to feel warmer than the reported air temperature
and even the heat index. So that's important to keep
in mind. And then the other component is that scientists
when they were deriving these heat indices and the wind chill,
they had to make certain approximations and assumptions. So some
(09:52):
of those involve like how tall are you, how much
do you weigh, how much do you sweat, what kind
of clothes are you're wearing, so on and so forth,
and that obviously can't account for each of our unique
body compositions and circumstances, but it's our best general approximation
for what you might experience when you walk out the door.
Speaker 3 (10:15):
The shade thing really blew my mind because I'm every
time I go outside and I'm in the shade, I'm like, oh,
this is cooler than what's the app because I'm in
the shade. But that's the best case scenario. You're only
going to get hotter if it's ninety degrees. That means
if you're in the sun, it's hotter than that.
Speaker 7 (10:34):
Yeah. Yeah, if you're walking around, you're gonna be sucked
there sometimes.
Speaker 3 (10:39):
Yeah.
Speaker 1 (10:40):
There's so many variables, which is why I just try
to distill them all into good or bad. Yeah.
Speaker 3 (10:46):
I know.
Speaker 7 (10:46):
Now I'm kind of like many systems well because it's like, well,
this makes me want them to have shade, Yeah, because
I need to know.
Speaker 1 (10:56):
You open the weather app. It's just like an Excel
sph Yeah.
Speaker 7 (10:59):
Ever dial in and then you dial in your outfit yeah,
and your skin tone calculator.
Speaker 8 (11:06):
I mean.
Speaker 7 (11:09):
Imagine you have an or a ring.
Speaker 2 (11:10):
Yeah, that's that's a lot of data.
Speaker 8 (11:13):
Yeah.
Speaker 7 (11:14):
And then you didn't tell weather you take a selfie
it skins your clothes.
Speaker 3 (11:17):
You don't even need to take a selfie, yeah, right,
your phone.
Speaker 1 (11:20):
Yeah, and and you just exists and Peter Tiel buys
all of this data. Yeah, exactly, and then he but
he tells you it's going to be only seventy two today,
or it's only going to feel like seventy.
Speaker 3 (11:29):
Two when we look at the weather app. Besides many
lying and saying he wants to look at historical data,
we're looking at it to see what we should wear
to go outside. Do I need to bring an umbrella?
Why don't we have this as the default? If I
open up the app, I want to see feels like
and then if I want to search for the actual
air temp, I could find that lower down.
Speaker 2 (11:47):
Why are we doing the reverse?
Speaker 4 (11:52):
We take a certain approach within our Weather Channel app,
and we've supply both temperatures, so not just the temperature,
but also the fields like temperature, so that you can
have both data points and use those to make decisions
about how you want to plan your day or go
about your day. But yeah, it's a really good question
because it gets down to the core of like the
(12:14):
human experience of course, but there's other impacts and implications
of the temperature that also don't relate to humans. So
some of those might be like if a farmer, for example,
is trying to figure out when to plant their crop
or protect their crop, they are looking at the actual temperature.
(12:36):
So that's what our weather forecast models are actually outputting,
is the real air temperature, and that's what we are
measuring and looking at when we are monitoring the climate,
for example, and it allows us to compare the observations
of temperature across the country, across the world, and over time. Whereas,
(12:58):
like with everything that we talked about with the feels
like temperature, there's a lot of approximations that are being used,
and so some weather services given the same environmental setup
might come up with different values. That makes it really
difficult for that inter comparison component. But if you yourself,
as the user of a weather app find that the
(13:21):
feels like temperature gives you everything that you need to
know you can set that as your own to fault temperature.
But we just don't want to make that assumption for
all of our users.
Speaker 2 (13:32):
So they're just very considerate of me at this point.
Speaker 3 (13:36):
The thing that impacts most of us would be it
feels like temperature that should be to default. Make everybody
else look for the other stuff.
Speaker 1 (13:45):
Please contact your company.
Speaker 3 (13:47):
I guess do you feel I understand all everything that
she said. When I'm looking at my app, I'm not
looking for climate. You know, over the last ten years,
I don't have any crops. I guess I can do
it within my own but I feel like as a
country we need to we neither do real feel I'm
not saying get rid of the real number. I'm just
saying put that lower.
Speaker 7 (14:08):
See why I can't?
Speaker 1 (14:10):
Why not just side by side?
Speaker 7 (14:11):
Oh slash yeah, pretty much? Okay, big one, I mean,
pick whichever one you want, big one then basically an
arrow or slash, Yeah, basically, and you just know that
you just know, okay, this is you know, you know
the real thing. And here's the thing. It wouldn't take
up that much space. They're digits and like the numbers
are hopefully not Yeah, yeah, hopefully four digits. You're look
(14:35):
at that total.
Speaker 1 (14:36):
I'm fine with the normal one. I'm fine with the
regular Yeah.
Speaker 7 (14:39):
My main concern with these things is doing an umbrella
or not, which I forget to look for most of
the time.
Speaker 3 (14:44):
Anymore, Speaking of rain, Manny, do you want to explain
this Twitter argument that people have been having.
Speaker 1 (14:54):
When it's raining or when it might looks like it
might rain. You go on the app and says, let's say,
for example, thirty five percent. Doesn't say what the thirty
five percent is. So some people think that it's thirty
five percent chance it's going to rain where you're standing.
Other people think it's in a rain on thirty five
percent of the area around you, yes, and a third
(15:19):
group of people think that's effectively the same thing.
Speaker 7 (15:23):
Well, well what do you what do you guys think?
Speaker 3 (15:26):
Before I did this interview, Yeah, my thought was it's
a thirty five percent chance it's going to rain.
Speaker 7 (15:32):
That's not that's my thought too, because the area doesn't
make because it's like, well, what's what area?
Speaker 1 (15:36):
A lot of people, a lot of people online, these
are not experts, but they were like, no, that is
what they're telling you. Well, what did you think I
was thought, thirty five percent chance is going to rain.
Speaker 7 (15:44):
Chance, Yeah, where you are? I agree, That's what I'm
looking at, and that's how I feel like my lived
experience is.
Speaker 1 (15:51):
You know, it's like, well, that's where it gets tricky.
It's it's never right to me.
Speaker 7 (15:56):
But yeah, I always assumed it's basically there is a
percentage of chance that it will rain. Not whatever area
I'm taking in, it's going to cover sixty percent of this.
Speaker 3 (16:07):
Yeah, yeah, that.
Speaker 7 (16:08):
Doesn't really that would never cross our behind me that much.
Speaker 4 (16:12):
So this is a common area of confusion for users,
and even in our own field of meteorology, it can
be really hard to pinpoint what we are actually communicating
with probability of precipitation or pop In my experience and
knowledge base, I think both of those interpretations are true
(16:33):
in their own right, which of course makes it even
more confusing. But I can I can share from the
Weather Channel app perspective, the way that we're communicating it
is really in your surrounding area. What is the likelihood
of precipitation or of rain impacting you within whatever time
(16:55):
frame you're interested in, So, whether it's today, the next hour,
fifteen minutes, and less so the amount of area that
is going to be impacted. A couple misconceptions that also
come up to are about the intensity of precipitation from pop.
So sometimes people will see like, oh, there's a ninety
(17:17):
percent chance of rain, it's going to be a downpour.
We are indicating the likelihood of rain, of measurable rain,
any rain, and so generally it has nothing to do
with the intensity of that precipitation, which is important to
keep in mind. And then the other component two is
you know, sometimes people think, Okay, there's a fifty percent
(17:39):
chance of pop in New York City, so only half
of the day will be wet, And again you could
have that fifty percent chance, there might only be a
couple hours that are impacted, and then you have a
beautiful remainder of the day. I think that it really
lays on us meteorologists to make sure that we're communicating
what that likely hood and probability means, especially if it
(18:03):
does vary, you know, from a news broadcaster or a
meteorologist who's on TV trying to communicate the likelihood of rain,
to a push alert from your weather app telling you
that there's rain coming within the next thirty minutes.
Speaker 1 (18:17):
That is really funny because the idea that the percent
chance is the total coverage is such a like, actually,
I'm smart, and this is what they're really saying, fools
in conversation. I've heard it so many times and it's
just wrong.
Speaker 7 (18:32):
And then I mean it's kind of fascinating, like people
are just taking this and twisting it in any direction
where it's like, okay, ten percent, that's ten percent of
the day. Yeah, so yeah, okay, so whatever ten percent
of twenty four hours is. It's like, yeah, thinking of
all the different ways to map it out, it's kind
of that's actually like a SAT question or something.
Speaker 1 (18:51):
Yeah, I heard the rain is only coming from ten
percent of the clouds.
Speaker 2 (18:54):
Yeah, exactly. Yeah, but there.
Speaker 3 (18:57):
I thought it was interesting that they're kind of they
could both be right depending on your weather.
Speaker 7 (19:02):
You need explanation.
Speaker 2 (19:03):
I mean, I think that you know, the apps that
we're looking at or telling us, I.
Speaker 7 (19:07):
Would hope so frankly, I think that you know, maybe
they could do a better job than of getting the
word out there hopefully. You know, we're going to do
our part today with our you know, hundreds of thousands
of listeners.
Speaker 3 (19:22):
Some would say millions, So yeah, you could argue that millions.
Maybe all right, we are going to take a quick
break and when we get back, we are going to
find out are the weather apps actually getting worse? And
it's Trump to blame. So in last week's mail bag,
(19:51):
we were debating who gets the right of way between
a school bus with the stop sign out, an ambulance
with the siren on, and a funeral procession, and from
North Carolina called in with this insight.
Speaker 9 (20:02):
I am a state wide law enforcement officer here in
North Carolina. If I am running lights and sirens, I
am allowed.
Speaker 10 (20:13):
To go through a soft sign.
Speaker 9 (20:15):
People legally have to yield to me. But if I
run a soft sign and hit someone, it is still
my fault, and I, as a driver, would.
Speaker 10 (20:26):
Not only be responsible to my agency, but I could
also be personally legally financially responsible funeral processions.
Speaker 9 (20:37):
You guys are correct, that is a courtesy.
Speaker 10 (20:39):
It's not a legal thing, and but it is it
drive me.
Speaker 9 (20:41):
Grace to get down here in the South, they love
them at funeral profession.
Speaker 3 (20:45):
So we're going to try to include more listener responses
to our episodes like this in the future, So you
can email us at Manny Noladevin at gmail dot com
or leave us a voicemail at the number in our
show notes and hey, you may includes you in a
future episode. All right, let's get back to the show.
(21:08):
We do have one final weather question from Mia.
Speaker 11 (21:15):
I have a question, and this is just bugging me
so much, especially this summer, our weather apps getting less
accurate because I feel like they are like these days,
I just don't feel like I can trust the weather
app to tell me exactly what's going to happen, specifically
(21:37):
if it's going to rain, like over this the course
of the summer, It's been a rainy summer in New York,
and now I feel like I'm becoming this kind of
like medieval farmer where I'm sort of gauging what's happening
in the sky, and I feel like I am becoming
a much better predictor of what's actually going to happen
than what my phone is telling me. I'm just starting
to be like, what's big weather even there? So if
(22:02):
you could answer for me, is it getting less accurate?
What's the reason should I trust this app anymore?
Speaker 4 (22:09):
That would be great. Thank you.
Speaker 2 (22:14):
I put this question to our meteorologists from the weather.
Speaker 7 (22:17):
She was sweam bullets at this one.
Speaker 3 (22:19):
Is the weather more unpredictable or is this on in
our heads? And you know, has nothing really changed.
Speaker 4 (22:26):
So that's a super intriguing question. No, as a short answer,
the aps are not getting worse at predicting rain. I
think there's a there's a few different things at play here,
one of which you pointed at. It's psychological, so we're
more likely to remember when the weather apps get it wrong,
versus that when we are seeing a prediction of, oh,
(22:49):
it's going to rain in ten minutes, let me make
sure I grab my rain jacket before I go out,
and it ends up being correct, and so you're prepared.
It becomes a non event and you forget about it.
But let's say that if someone is outside it starts pouring,
there's absolutely no warning. Of course, you're going to be
annoyed that you had no notice that that was going
(23:09):
to happen. And there's a few reasons that those specific
circumstances can crop up. This example of stepping outside and
being met with an unexpected downpour of rain can largely
happen because even if the radar is able to pick
(23:30):
up on a storm let's say it just started raining
within the past few minutes. The weather radar just got
that data. So for example, that data needs to be
ingested into our back end systems or any other weather
app systems to process that data, figure out where that
storm is using proprietary software and algorithms, predict where that
(23:51):
storm is headed and if it will intensify or again dissipate,
get that forecast to our technology system downstream, and then
those then deliver that information to your app. So that
takes a bit of wall clock time for all of
that process to happen. So unfortunately, there are those situations
(24:12):
where rain can impact you within that window of time
that we're trying to get that information to you as
fast as possible, but perhaps missed that window of opportunity.
Speaker 1 (24:24):
I've never thought about a situation where the weather app
was wrong, because it just was like trying to catch
up to the real time m hm. You would think.
Speaker 3 (24:34):
You're supposed to be predicting.
Speaker 7 (24:37):
It's like, don't blame me, I'm not the weather company.
Speaker 3 (24:41):
Hello.
Speaker 1 (24:41):
And now that I remember what Mia was complaining about,
I think her concern is that the apps are more
frequently incorrect, not that they're ever incorrect, but that it's
happening more often.
Speaker 3 (24:55):
Yes, yeah, she thinks it's getting the apps are getting worse.
So the Weather Company said, no, that's you know, the
apps aren't getting worse, but they work at the Weather Company.
So I wanted to call up someone who can maybe
speak a bit more freely about whether or not these
apps are getting worse and do we need to worry,
(25:16):
especially with these Doge cuts, about them being worse in
the future.
Speaker 2 (25:18):
So I called the Mary Glacken.
Speaker 12 (25:21):
I'm the former deputy undersecretary for NOAH for operations there
and I had worked for Noah for just about thirty
five years. I also am on an AI board and
things like that, so today these days I'm mainly in
an advisory capacity.
Speaker 2 (25:40):
She also worked at the Weather Company back in the day.
Speaker 3 (25:43):
All right, great.
Speaker 7 (25:45):
Job finding, Mary.
Speaker 1 (25:46):
I always have stuff to say about my former employees.
It should be good to take.
Speaker 3 (25:52):
So, you know, the big thing that we were concerned
about and seeing the headlines is that with the Doge cuts,
that there's just less people working, which is why the
apps are getting worse. So I asked Mary, like, where
do we currently stand.
Speaker 12 (26:06):
So let me talk about it in terms of personnel first,
because one of the major things that happened was the exodus.
You know, there was the Fork in the.
Speaker 13 (26:15):
Road, another unprecedented action from the Trump administration, offering more
than two million federal workers a buyout. The workers receiving
an email with the subject line fork in the Road,
giving them a choice resign and be paid through September
or risk being fired.
Speaker 12 (26:31):
So the National Weather Service and all of Noah are
down significantly in terms of people, something approaching as much
as twenty percent in some locations. So the National Weather
Service did lose a lot of senior people. In terms
of funding, they had actually been funded by Congress last
(26:52):
year for a full year at a reasonable level, but
the Trump administration has delayed spending of those funds, so
they've really been slow rolling contracts to go out for
various things. I don't have any figures here, but it's
safe to assume there's a fair amount of money still
on the table. But the direction that we saw this
(27:14):
week was a spend plan for the rest of this
fiscal year is clawing back that money. It's saying you're
not allowed to spend that anymore. So that's that's kind
of where we are. We've seen some news about them hiring.
Speaker 5 (27:29):
So earlier this year, President Trump's Doze Office cut more
than five hundred Weather employees. Well now WS says they're
going to hire four hundred and fifty meteorologists, hydrologists, and
radar technicians.
Speaker 12 (27:41):
They're all entry level positions, So you know you're not
going to hire back the expertise that's been lost that
one of the senior officials that Noah career official had quoted,
that was twenty seven thousand years of expertise that walked
out the door between February in April.
Speaker 3 (28:02):
It's just going to be a lot of people, a
lot of college who don't have the expertise, and now
they also don't have the leadership to mentor those people.
Speaker 1 (28:10):
They just went from an NFL team to like JV
high school team.
Speaker 3 (28:18):
So I wanted Mary to answer the question because I
couldn't take the Weather Company's word for it.
Speaker 2 (28:22):
Are the apps getting worse?
Speaker 12 (28:24):
The apps haven't degraded at all. I think the worries
that we have with the Trump administration is if they
move forward with their plans. Literally one of the plans
is to eliminate all of Noah's research arm So and
that's all our future. You know, how do we do
a better job in forecasting that whole section would be eliminated?
(28:49):
Which is crazy, but that's the plan. I do want
to kind of remind folks that Congress has a say
in this whole thing. Both the House and Senate that
have given their plan on how Noah would be funded.
The Senate does a really pretty good job and keeping
Noah funded. The House has some significant cuts in it,
(29:12):
but nothing as draconian as the administration is asking for.
I think the problem is that Congress won't be able
to agree on the overall spending, which will really basically
allow the Trump administration to continue to do what it
wants to do.
Speaker 3 (29:29):
I was curious if we've kind of hit the point
of no return, you know, see if President Biden runs
again and.
Speaker 2 (29:37):
Wins, yeah, you know which, we're in eight can we
get back to where we were?
Speaker 12 (29:52):
One of the things I'd like to make clear with
you know, all of my experience with Noah, both in
Noah and then working with Noah outside is I would
not be the one arguing for status quo. You know,
I'm not here saying oh, it was a perfect world
and now this has happened. I think it will be
possible to get us back if we can keep people
in the field and interested in our problems. You know,
(30:14):
you have the whole advent of AI, and AI is
impacting our value chain all the way from taking an
observation to helping somebody make a decision on what to
do that day. So when I look at AI, it's
pretty clear to me and many is the private sector
(30:35):
is ahead of where Noah is with AI, and that's
no great chock. You know, the private sector can pay
somebody a half a million a year because they're a
genius in AI. You know, if you go to nowhere,
you're going to be making ninety thousand. So that says
to me that we need to find a way to
work with the private sector that meets public good. You know,
(30:57):
how do we ensure that everybody gets warning that they
need and how do we ensure people get the basic information.
Speaker 3 (31:05):
To do that?
Speaker 12 (31:06):
So I do think we need to think more creatively
about public private partnerships. You know, right now, there's not
a lot of mechanisms for the private sector to work
with the government sector. And I think if we you know,
if we started rebuilding tomorrow I think that's what we
would be thinking about, Well, what really makes sense here
(31:27):
to do in the future.
Speaker 1 (31:32):
Yeah, I'm like, I'm like your run of the mill
kind of lefty, right, who has some skepticism about what
the private sector can do, like for the good of
the people, quote unquote. But it's interesting how this situation
is so die or that, like we do just kind
of have to look at it.
Speaker 10 (31:47):
All.
Speaker 3 (31:47):
Right, So we've been, you know, trying to make sense
of the world through weather. We're going to take a
little break. I'm going to leave you guys, and then
we're going to try to make sense of the world
through Spotify's shuffle feature. This is Devin, Welcome back to
(32:13):
no such thing. A few weeks ago, actually, we got
an email from this listener named Austin. He wrote, after
listening to your latest mail Bag episode, it got me
thinking about an issue I've run into over the years.
Whenever I hit shuffle play for a music playlist on Spotify,
the shuffle feature never seems to be truly random. I
(32:36):
want to know how does shuffle work for music apps
and is it truly random.
Speaker 8 (32:45):
I'm Heather mccoldon and I'm a writer, artist, and sometimes
a tarot reader.
Speaker 3 (32:53):
All right, So I reached out because we got a
question from a listener who wanted to know if we
could figure out how Spotify's shuffle feature works. And I
came across your article in the Financial Times cool in
which you dive into this. Would you mind, I guess
kind of setting the scene for me on what happened
(33:16):
on December fifteenth, let's say, twenty twenty three.
Speaker 2 (33:20):
Yeah, when all these random acts started coming together.
Speaker 8 (33:26):
Yeah, basically, you know, it's like one of those things.
It's like holiday time, Brooklyn, like the aras chilled. Everyone's
excited and happy for like the end of the year
beginning of the new year. I was visiting some friends
at a dinner party in Williamsburg and you know, too
(33:47):
much wine, too much festivities, like everyone leaves on masks
to go home, and yeah, I was feeling like kind
of nostalgic, like I had a partner in that neighborhood, like,
and you know, it's just brought back memories and you know,
my friends were like, oh, like we opened the door
from the building, there's a cab right there. And I
(34:09):
was like great, Like the universe is working out for me.
So yeah, like I get in and I'm just like, oh,
like went into my liked songs on Spotify and just
hit shuffle and the song Maps by the yayaya As
comes on, which is, you know, like this epic contemporary
(34:29):
love song and basically like the taxi just like decides to,
you know, go a certain way and we literally like
pass my ex's building as like this song is about like,
you know, saying like you know, they don't love you
like I.
Speaker 4 (34:47):
Love you.
Speaker 8 (34:50):
Then, and I just had this moment of like, what
the fuck is happening, not just like in my life
in the universe, but like what is this app even? So, yeah,
that led me to investigate those Spotify shuffle works in
a very randabout way. But you know, like I think
(35:11):
that's like what's kind of cool about existing now like
in this era where it's like, how are these technologies
and their glitches affecting our emotions in our personal lives?
And you know, is it random? Is it all random?
Speaker 2 (35:30):
Et cetera.
Speaker 3 (35:31):
Can you talk me through I guess starting with the
first iteration of like what Spotify shuffle used to do.
Speaker 8 (35:43):
So basically there's like a very simple, elegant, mathematical description
of randomness. That's called the fisher Yates algorithm, and essentially
this is like from Spotify's insight option. Until somewhere between
twenty twelve and twenty fourteen, it was using fisher Yates
(36:06):
from my understanding from what's available fisher Yates basically, it's
an algorithm that will take an.
Speaker 4 (36:14):
Array of values.
Speaker 8 (36:15):
Let's say, like you have numbers one through ten, it
removes five, and five becomes the first track of the
shuffle list, and then it just keeps kind of you know,
randomly selecting the other values to like create a new list, Sasha,
So every number in that list has an equal chance
(36:38):
of appearing in the first position or the consequential one.
It's great because technically that's completely random.
Speaker 4 (36:48):
Right.
Speaker 8 (36:49):
So Spotify engineers were like, this is awesome. You can
do this in between like two or three lines of code,
Like it was so simple. They're like, yeah, we sold this,
and like kind of instantly the user complaints came in
of like my shuffle is broken. How is it possible
(37:09):
that you could screw this up? It's so simple?
Speaker 3 (37:11):
And the complaints were at the time because I remember
seeing them on social right would be like, how's you
know Spotify Shuffle Ranmouth. They're playing four Taylor Swift songs.
Speaker 8 (37:19):
In a row one hundred percent. But you can see,
like based on how I described how it works, Ye,
randomness does create clusters. So it creates like, you know,
clusters of songs from one album or clusters of songs
by the same artist, Like each of those songs has
the exact same chance of like emerging, right, there's nothing
(37:44):
preventing that. So what the engineers finally determined was users
were falling for what's called the Monte Carlo fallacy. So
this is basically a human perception of randomness that is
not correct, and it's named after this very specific event
(38:06):
that occurred August eighteenth, nineteen thirteen, at the famous Monte
Carlo casino. We're essentially at the roulette table. The ball
kept falling on black. It starts to create this weird
chaos and like sensation in the casino because people are
thinking like this isn't possible, right, like you know, two
(38:29):
or three times sure, then you get into like five
to ten times, you know, like whirl, like this is unusual.
So yeah, something's up, and people kind of come down
with this fever of just expecting like on the next
turn of the wheel, red, Red is going to happen.
(38:50):
Red is going to happen because Black has happened all
these times of beforehand, and that night all fell on
Black twenty six times in a row, which is just
like it's like an astronomical this happened, and yeah, it
just this whole situation pointed out that like it's also
(39:13):
called the gambler's fallacy. It's where you think previous events
somehow affect the current event unfolding. So that's why people
get angry at like the clusters of you know, Addison
Ray or whatever.
Speaker 3 (39:30):
Yeah, it's like, okay, you're already played in Addison Ray song.
Therefore their Monte Carlo Felas is like, yeah, it has
to be something different now because we've done Black so
many times in a row. We played the Addison and
Ray song, so therefore an X song cannot possibly be
diet pepsi, you.
Speaker 8 (39:45):
Know, right, and then like what the fuck it's diet pepsi?
Like are you kidding me? Right now? The engineers were like, oh, okay,
people are having like this weird issue, like they don't
realize what randomness actually is. So then they created essentially,
like it's not like a hybrid algorithm, but they created
(40:08):
a different one that mimics or attempts to mimic, the
human perception of randomness. So it does this way. It
takes your playlist and it's like, oh, like, I don't know,
You've got a lot of like Justin Bieber in here,
So it's spreading Justin Bieber and then with being Justin Bieber,
(40:30):
it's spreading out the albums.
Speaker 2 (40:32):
Yeah yeah, yeah, yeah.
Speaker 8 (40:33):
So it's called cluster breaking.
Speaker 3 (40:36):
Yeah, you're not going to get to Justin Bieber songs
within a ten track shuffle from Swag. We're going to
make sure if you get to Justin Bieber songs, you're
from completely different albums.
Speaker 4 (40:45):
One hundred percent.
Speaker 8 (40:46):
Yes. So that was their solution. And keep in mind,
like what I've just described with the cluster breaks, like
that's from twenty fourteen, so like we there's been nothing
really that they put out since then that describes what's
going on. People are now in this weird position where
they're like they want, like the Fisheryates algorithm to come
(41:10):
back as like an additional like like if shuffle could
have yet exactly, and they're like yeah, like I know
it's bad, but like it would just be preferable to
like whatever this weird thing is. Our human minds are
so small that, you know, we're only able to comprehend
(41:35):
like a very like tiny wedge.
Speaker 4 (41:37):
Of how the world functions.
Speaker 8 (41:39):
Right, So it could be that like we're seeing all
these things as random, but really it's like, oh, if
we could actually take a step back and like have
more of a bird's eye view, potentially these things could
be connected. And they just operate at a pace that's
so slow that like over the core of one human
(42:00):
lifetime it appears random, but over the course of like
five hundred human lifetimes, it's actually like one slow process
that makes perfect sense, but we're just not able to
comprehend it, right, So you know it just randomness is
so interesting because in a sense, it's undefinable. Yeah, right,
(42:24):
So that's why it's so interesting when you have something
that's essentially like a math problem where it's like, come on,
like I have like I don't know, thirty five hundred songs.
It can't like how how complicated could it possibly be?
And it turns out it's really complicated.
Speaker 2 (42:47):
HEATHERN. Mccalldon is the author of the Observable Universe. We're
going to put a link to it in our show.
Speaker 3 (42:52):
Notess Hews Hews her howss. No Such Thing is a
production of Kaleidoscope Content. Our executive producers are Kate Osborne
in Mangesh Hadi Cardur. The show was created by Many Fidel,
Noah Friedman and Me Devin Joseph. Our theme in credit
(43:13):
song or by Manny, mixing by Steve Bone. Additional music
for this episode by certain Self. Our guests this week
we're doctor Loriana Gaudette from the Weather Company, Mary Glacken
and Heather mccauden. Thanks to Alec Mia and Austin for
the questions. If you have something you want us to
get at the bottom of you can email us at
(43:33):
Manny Noah Devin at gmail dot com. Sign up for
our newsletter No Such Thing that shows We'll
Speaker 1 (43:40):
See you guys, those by those such things
Popular Podcasts
NFL Daily with Gregg Rosenthal
Gregg Rosenthal and a rotating crew of elite NFL Media co-hosts, including Patrick Claybon, Colleen Wolfe, Steve Wyche, Nick Shook and Jourdan Rodrigue of The Athletic get you caught up daily on all the NFL news and analysis you need to be smarter and funnier than your friends.
On Purpose with Jay Shetty
I’m Jay Shetty host of On Purpose the worlds #1 Mental Health podcast and I’m so grateful you found us. I started this podcast 5 years ago to invite you into conversations and workshops that are designed to help make you happier, healthier and more healed. I believe that when you (yes you) feel seen, heard and understood you’re able to deal with relationship struggles, work challenges and life’s ups and downs with more ease and grace. I interview experts, celebrities, thought leaders and athletes so that we can grow our mindset, build better habits and uncover a side of them we’ve never seen before. New episodes every Monday and Friday. Your support means the world to me and I don’t take it for granted — click the follow button and leave a review to help us spread the love with On Purpose. I can’t wait for you to listen to your first or 500th episode!
Dateline NBC
Current and classic episodes, featuring compelling true-crime mysteries, powerful documentaries and in-depth investigations. Follow now to get the latest episodes of Dateline NBC completely free, or subscribe to Dateline Premium for ad-free listening and exclusive bonus content: DatelinePremium.com