May 14, 2025 • 28 mins
You can make your healthy choices, the choices you wish you’d just make automatically….automatic if you know the language of the future! It’s not Jawaese or Clingon. It’s the language of algorithms, and it can make your life amazing! Algorithms are just a process or set of rules to be followed in a series of steps that can be used to make calculations, resolve problems, and reach decisions. Whether you know it or not, your life is already a series of algorithms. If (this)....then (that). You can leverage this language to install algorithms that run on autopilot that create a life that will blow your mind.
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Come visit me: www.healthcouragecollective.com
email me: healthcouragecollective@gmail.com
$10 off Eye Buy Direct Glasses: https://www.eyebuydirect.com/referral/1615995
Visit me: www.healthcouragecollective.com
Email me directly: healthcouragecollective@gmail.com
Are you ready to give your cells their best chance to not have to stop living before they die by allowing them access to physiologic levels of hormones, but aren't sure how to even get started? Join the waitlist for my new beta program here and help me figure out how best to help wonderful women like you get the hormone care they deserve!
Come visit me: www.healthcouragecollective.com
email me: healthcouragecollective@gmail.com
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:00):
You can make your healthy choices the choices you wish
you'd just make automatically automatic. If you know the language
of the future, it's not Jawa, ease or cling on.
It's the language of algorithms, and it can make your
life amazing. Algorithms are just a process or set of
rules to be followed in a series of steps that
(00:21):
can be used to make calculations, resolve problems, and reach decisions.
Whether you know it or not, your life is already
a series of algorithms. If this, then that you can
leverage this language to install algorithms that run on autopilot
that create a life that will blow your mind. You're
(00:41):
listening to the Health Courage Collective Podcast, Episode two hundred
and two, Make Healthy Choices Automatic with Algorithms, Originally published
July twenty seventh, twenty twenty two. Welcome to the Health
Courage Collective Podcast, the show for women who are too
busy to slog through hours of generalized, in applicable, and
often contradictory health information, but too smart to ignore that
(01:05):
a few minutes of focused attention now can prevent years
of suffering in the future. I'm your host, Christina Hackett,
a pharmacist who doesn't want you to live on prescriptions,
a certified coach specifically trained to maximize your potential, and
a compulsive learner obsessed with preventative, cutting edge, holistic and
integrated medicine. I'm on a mission to increase your physical
(01:26):
and mental resilience so you can fearlessly look forward to
your next forty plus limitless years. Your time is count
Let's go. I'm my friend, and welcome to the episode.
I hope your day is going great today May so
that's usually a good thing. Have you woken up during
the middle of the night lately? I hope not, But
(01:47):
if you do, I hope that understanding the three and
a half different systems that control sleep is useful for
you to find ways to stay asleep all night. A
few years ago, we had my then twelve year old
now fifteen year old son Kipland tell us about our
call to adventure at the same time. Back then, I
asked him a few questions about his obsession with speed cubing.
(02:11):
We didn't really plan it out ahead of time, so
I wasn't prepared with questions and he wanted to go
eat lunch first, but I made him tell me a
little bit about his speed cubing. First, he got a
special Rubik's cube, like at least a couple of years
ago that you can turn more quickly. It has magnets
in it. Anyway, he played with it very rarely, but
then a few months ago he got obsessed with it
(02:33):
and he watched these videos over and over and practiced
and practiced, and now he can solve it ridiculously fast,
he said. He forgot to say that a lot of
the procedure he follows has to do with the rhythm
and the feel and the sound of the cube. Sometimes
I would see him practicing with his eyes closed, which
seems like it wouldn't work well for a Rubik's cube,
but it does. He's a goofy one. So here's what
(02:54):
he had to say. All right, welcome back to the podcast, Kip. Okay,
it's been a few weeks, but really it's only been
like one minute. Okay. So I wanted to ask you
about algorithms. So one day you disappeared in the basement
and you kept watching something on YouTube, right, and that
(03:17):
I was trying. I don't remember. I think you hadn't
eaten all day or something, and I was trying to
get you to eat lunch. But what what you were
doing was learning what do you remember algorithms? An algorithm for.
Speaker 2 (03:31):
For a Rubic's cube.
Speaker 1 (03:32):
A Rubic's cube, So how to solve a Rubic's cube?
Speaker 2 (03:35):
Right?
Speaker 1 (03:35):
Yeah? Okay? And what made you decide you wanted to
learn this algorithm?
Speaker 2 (03:41):
It was a faster way to do to accomplish a
goal on it.
Speaker 1 (03:45):
Okay, all right, so you have your little cube with you, yes,
how so it's not a regular Rubics cube, it's a
speed cube, right, yeah.
Speaker 2 (03:54):
It does the same thing though, right, it just turns faster.
Speaker 1 (03:57):
It turns faster. It's not like the Rubik's cube I
had when I was little.
Speaker 2 (04:01):
That it's the same basic concept, right.
Speaker 1 (04:05):
Mine was black and it had stickers so you could
pull the stickers off. I even had one that I
got from a box of checks. Well, I think I
had to have several boxes of checks, and then I
sent in a thing in the mail and I had
to checks one and so it had like all wheat
checks on one side and all rice checks on one side.
That was my Reubx cube. Okay, so can you tell
(04:30):
us how long did it take you to learn the algorithm?
To the point where you had it memorized and you
could solve.
Speaker 2 (04:36):
Your cube one algorithm or all of them.
Speaker 1 (04:39):
Well, to the point where you could solve the whole cube.
Speaker 2 (04:42):
Probably three or four days.
Speaker 1 (04:45):
Oh okay, I just know you kept watching this thing
on YouTube and I was trying to get you to
eat lunch or something, and you kept watching it. Okay,
So it took you three or four days. And how
long does it take you if someone gave you a
completely messed up Rubik's Cube? How long would it take
you to solve it?
Speaker 2 (05:02):
Uh? With those algorithms, probably around a minute. Oh, but
I learned some new ones.
Speaker 1 (05:09):
Oh okay, So now I know you were timing yourself.
How long does it take you to solve one?
Speaker 2 (05:15):
Uh? My fastist is twenty eight seconds.
Speaker 1 (05:19):
Twenty eight seconds to solve a completely messed up Rubik's Cube? Yes, okay,
and you're messing with it right now. I don't know
if they can hear in our house, we hear a
lot of Rubik's Cube messing around with, right, Yeah, like
even during movies, and it has to get taken away. Okay,
So can most people in the world solve a Rubics
(05:41):
cube in twenty eight seconds.
Speaker 2 (05:43):
No, No, a lot of people can, but not most people.
Speaker 1 (05:46):
Yeah, definitely not most people. What would most people think
about being a like if they gave you a messed
up Rubik's cube and you could solve it in twenty
eight seconds, what would they think?
Speaker 2 (05:55):
I don't know. They've told a lot of people that
don't really care.
Speaker 1 (05:59):
Okay, but most but you know, if they.
Speaker 2 (06:02):
They might be. They might think it's kind of cool
because they can't do it.
Speaker 1 (06:06):
Yes, so they might think you're like a genius because
they can't do it, but you can, right.
Speaker 2 (06:11):
Yeah, But in reality, anyone can do it with practice.
Speaker 1 (06:15):
Mm hmm and learning the algorithm, right, yeah, So can
you describe a little bit, like really short of what
the algorithm is.
Speaker 2 (06:24):
An algorithm is basically a series of turns you have
to memorize to do a specific goal.
Speaker 1 (06:32):
Mm.
Speaker 2 (06:33):
Okay, So if I wanted to switch two pieces in
a place, then like if I wanted to switch two
of the top edges, then there's one algorithm that does
that and it switches their place.
Speaker 1 (06:51):
But do you just solve one side first and then
do the other sides?
Speaker 2 (06:54):
Yeah? Pretty much really, although there's the more advanced method
that I use. Do you solve two sides at a time. Okay,
I don't want to be too loud for this. It
gets pretty loud. Yeah, look, you solve two sides at
a time.
Speaker 1 (07:12):
Some algorithms.
Speaker 2 (07:13):
Let's see if I can get an example. If I
want to solve this part right here, there's an algorithm
for that. And algorithms don't always do the same thing,
so you sometimes you might have to like do a
left hand bruis have the same algorithm.
Speaker 1 (07:33):
Hmm. Okay, And you said something important which you have
to know where you're starting, and you have to know
what your goal is, right, yes, okay, and then now
it's solved. I know you guys can't see it, but I.
Speaker 2 (07:51):
Was trying to not be loud. It gets pretty loud
if I'm trying to.
Speaker 1 (07:54):
Go fast, right, But yeah, it's solved now, okay, Because
ever since you were like six or something, you've liked
coding on computers, right, Like, I remember, I have a
picture of you in a little tank top when you're
like six years old, and your feet don't even reach
the edge of the couch cushion and you have dad's
old laptop and you're like coding a snowman or something.
Speaker 2 (08:18):
Yeah.
Speaker 1 (08:18):
Yeah, So when you write computer code, what is it
that you like about writing computer code?
Speaker 2 (08:25):
Uh, it's just creating something achieving a goal.
Speaker 1 (08:30):
Kay, what was I gonna know? It seems like I
had a good question about this and now I can't
remember what it was.
Speaker 2 (08:35):
Is it about debugger?
Speaker 1 (08:38):
I don't know. Do you even have something to say
about debugging?
Speaker 2 (08:41):
I mean, I don't know who it relates to the topic.
Speaker 1 (08:45):
Well, you can say it, and if it doesn't then
we'll cut it out.
Speaker 2 (08:48):
Uh. Debugging is basically, if you have a problem with
your code or you need to fix it, then you
have to figure out how to find it and take
it out.
Speaker 1 (08:59):
Okay, So that is there a process of how you
find the bug in the code.
Speaker 2 (09:06):
Yeah, you could say there's an algorithm for it. Okay,
you'll first you figure out what's going wrong with the code.
So if you're making a code to like, I don't know,
my brain comes up with bad examples if you can.
If you're making a code to draw I don't know,
to give you bananas, but then it gives you an
apple instead, then you'd you'd know, oh, it's giving me
(09:29):
apples instead of bananas. So then that's the first step.
After that, you need to find the part of the
code that tells them makes it give you a banana, okay,
And then you have to figure out what's wrong with that.
So maybe you wrote give apple instead of give banana
(09:49):
if it's really simple code, or maybe if you're using numbers,
then you just put in the wrong number and you
have to fix it. Sometimes it's really annoying.
Speaker 1 (09:59):
It's annoying, but does it help you solve the problem
in the end?
Speaker 2 (10:03):
Yesh?
Speaker 1 (10:04):
Yeah? But basically, how would you sum up algorithms? Uh?
Speaker 2 (10:09):
J Just basically a I don't know if you would
call it simple, but it's a series of actions you
do to achieve a goal.
Speaker 1 (10:21):
Kay. Good, I like that definition. I know that in
the context I'm gonna talk about it and you're doing
this all just off the top of your head. But
for me, I've been thinking about it and we talk
about if then. So when you write an algorithm, like
if you're gonna program our roomba, which we never even
use anymore, to vacuum, then it it has a series
(10:44):
of if I bump into a wall, then I do this, yeah,
And if I encounter dirt, then I spin in a
circle and clean extra and if I hit the edge
of a stair, then I stop and turn around. That
kind of thing. Would you say, that's an algorithm? Yeah, okay,
the Rubik's cube is getting solved again. All right. The algorithms,
(11:08):
the if and the then can teach us to do
things that we didn't realize that we could have done before,
like solve a rubik quban twenty eight seconds. Right. Yeah.
Speaker 2 (11:17):
People might think it's complicated, but really just need to
learn the algorithm.
Speaker 1 (11:21):
Mm hmm and follow the if this, then that, and
then it's done pretty quick. Okay, cool, all right, thank you.
You're now allowed to eat your lunch. Yay, thanks for
being on. We'll have you back later. Okay, okay, so
we're talking about algorithms. I'm sure you've heard the term before,
(11:42):
but how would you define an algorithm? The dictionary says
it's a process or set of rules to be followed
in calculations or other problem solving operations, especially by a
computer professor. You've all. Noah Harari says that algorithms are
the most important concept of the twenty first century. And
he's a historian, so that's kind of interesting that that's
(12:04):
his prediction for what's most important for our present and
our future. He says, quote, if we want to understand
our life and our future, we should make every effort
to understand what an algorithm is and how algorithms are
connected with emotions. End quote you've all knowah Harari says
that an algorithm is a methodical set of steps that
(12:26):
can be used to make calculations, resolve problems, and reach decisions.
No matter our profession or propensity toward math or computer programming,
we all use algorithms all the time. If you measure
something in inches and want to know how many centimeters
it is, you multiply it by two point five four.
That's an algorithm. If you want scrambled eggs, you crack
(12:49):
the egg into the bowl, whisk, heat up a pan,
pour it in Stirwallli cooks. If I want to gain strength,
I lift my weights, write down what I'm able to do,
and then next time I lift a little bit more
and write it down and note how I felt, get
proper rest, and then repeat. If I want to solve
a Rubik's cube, my algorithm is to try to get
(13:10):
a few squares of the same color on the same
side together. Then watch as I have to move some
out of place in order to get another one where
I want it. If I were given a great deal
of time, I could probably solve one side, while all
the others would just be a jumble. This isn't because
I'm stupid or a failure at life, or lazy or
(13:31):
oppressed by the man, or whatever people like to blame
things on these days. I just don't know the algorithm
to solve it properly or quickly. I'm still using an algorithm,
just not an effective one, but just a day or
two of practice using upgraded algorithms, and my little Kip
is a twenty three second Rubik's Cube prodigy. Okay, prodigy
(13:54):
is an overexaggeration, but it's impressive to watch, and I
think he deserves to be proud of it. Hey, quick question,
have you ever tried blue light filtering glasses yet? You
probably know that the blue light coming out of all
your screens can strain and possibly damage your eyes, so
protecting your eyes is a good idea. I've tried a
few different kinds of blue light filtering glasses over the years,
(14:15):
but lately my eyes were physically aching after looking at screens,
so I got a pair of glasses from Eye by
Direct with Sight Relax lenses that are designed to relieve
eyestrain in addition to blocking blue light. They have a
really subtle magnification at the bottom, which I wasn't sure
I would like, but I really do, and the lenses
(14:35):
are a lot more clear. I always felt like my
other blue light lenses were never all the way clean.
If you've never tried Eye by Direct, there's a link
in the show notes for ten dollars off your first
pair of glasses that you can use, in addition to
fifteen percent off your first pair. They have great deals
on prescription glasses and sunglasses in addition to blue light glasses,
and they have so many different kinds of frames to
choose from, from really conservative ones to really bold. My
(14:59):
Sight Relax glasses look like tortoise shell, but it's actually
little flowers on the frame. Cute. Okay, back to the episode.
Since we're all running off algorithms anyway, maybe it's time
we get intentional about our unconscious programming. We've talked about
Ray Dalio in the past. He's one of the wealthiest
and most influential people on the planet, running the best
(15:20):
performing hedge fund in the world. He says that quote,
your children and their peers must learn to speak the
language of algorithms, because it will soon be as important
as any other language. End quote. The language of algorithms
is the language of the future. It's an if then language.
(15:42):
A super common algorithm we can run is setting our thermostat.
If the temperature dips below sixty nine, then turn on
the heat until it gets back to sixty nine. If
the temperature gets above seventy nine, then turn on the
air conditioning until it gets down below seventy nine. We
can set up that algorithm and think about it again.
It just runs in the background, keeping us comfortable. If then,
(16:07):
what algorithms are you currently running? I guarantee you have hundreds,
if not thousands, that are already humming in the background
on autopilot. If I wake up in the morning, then
if I need to go to the bathroom, then if
I feel first, then if I get a push notification
on my phone, then if I feel fear, then if
(16:31):
someone cuts me off in traffic, then if someone offers
me a piece of birthday cake, then if my child
doesn't do what I asked, then if someone is kind
to me, then it can go on and on and on.
We don't consciously evaluate and make decisions about every second
(16:53):
of our lives, and that's a good thing. We're meant
to be efficient. But it kind of reminds me of
that Peter Drucker quote that says there is nothing so
useless as doing efficiently that which should not have been
done at all. It's not quite the same context. But
what if we became efficient using almost no energy to
run awesome algorithms on autopilot. I think we'd end up
(17:14):
looking like little prodigies and it would be something to
be genuinely proud of. But it's not because we lucked
out or were blessed by a fairy godmother or whatever
people tend to think about people who appear to have
superhuman willpower. It's like my Rubik's cube algorithm compared to
my sons. We both have them, but a little bit
(17:36):
of upfront work can install automatic and nearly effortless algorithms
to run on autopilot. For Mother's Day this year, my
husband signed me up for a women's self defense jiu
jitsu class because he knows I love that kind of stuff.
If I pass the tests at the end, which isn't easy.
I'll get a pink pelt cute, huh. Jiu Jitsu is
about the weak overpowering the strong. It's about the technique.
(18:01):
At first, they kept explaining stuff to us as being
so great because they would say it takes no energy
to do this, or it takes no strength to do this,
and I was kind of like, I have energy and strength.
I worked really hard for them, Thank you very much.
How pathetic do they think I am? But then I
gradually came to realize that the power of jiu jitsu,
(18:22):
or at least the situations I'm training for, is that
I can incapacitate someone, even someone quite strong, without breaking
a sweat. Sure, I have a reserve of power that
I can deploy if the need arises. But the beauty
is in the fact that I can overpower the attacker
without having to exhaust myself, and that opens up so
(18:42):
many more possibilities for what I can do with the
energy and strength that I didn't have to use just
to stay alive. Just by saying this, I'm realizing that
every single thing I've ever learned in that class is
just an algorithm. If you pin me on the ground
with a knife to my throat. Then I grab your
hand with a cross grip sea clamp and move to
a certain pattern that ends with me tearing your rotator cuff.
(19:03):
If you grab me from behind to carry me off
to your creepy van, then I bend and grab in
a certain pattern that ends with me hyper extending your knee.
It's all about building muscle memory to be able to
do it automatically when emotions are high. But it's all
just algorithms. If this, then that, And the point I
was trying to make is that, just like if you
(19:24):
heard that some bodybuilder guy attacked a woman with a
knife and ended up with a dislocated shoulder and knee,
you'd assume the woman was superhuman. But if you can
live your life with algorithms humming in the background on
autopilot that take no energy or strength at all, you'll
appear to have superhuman willpower, And more importantly, you'll still
have all of your energy and strength to put into
(19:46):
something other than surviving the attacks of modern society. You'll
be able to blow your own mind by doing things
you never would have thought you'd be capable of. How
cool is that?
Speaker 2 (19:57):
So?
Speaker 1 (19:58):
How do we know whether the algorith rhythm we're running
are any good? Well, if I got a room ba vacuum,
how would I know whether it was operating from an
effective algorithm. If I walk through the kitchen and bare
feet and a little Rubik's Cuber's cheese at crumbs stick
to my feet, then I know there's a problem with
the room as operation. I know because it's not getting
me the result I want, which is a clean floor.
(20:20):
So we start with the result we want to create.
What do I want is the byproduct of running my systems,
the achievement of my goal. Another thing to consider is
if my room bus successfully sucks up the cheese at crumbs,
but then promptly falls down the stairs or dies before
it can get back to its charging station. That's also
(20:40):
a problem because it's not an easy or sustainable automatic program.
Kiplin talked about algorithms being a methodical set of steps
you take to achieve a specific desired outcome. So the
first thing we need to do is figure out what
we want our algorithm to produce. What is our goal.
It sounds easy and obvious, but I see this with
people who want my help quote getting healthier, awesome. I
(21:05):
love those people. Thank you. I'm happy to do that,
But what does that mean? How wouls you know whether
you succeeded if you use your fantastic new language skills
to write algorithms because it's something you've seen your muscular
neighbor do, or something a fitness influencer proselytizes. Of course,
it's not going to be easy. It's not your fault.
(21:27):
It's not because there's something wrong with you or you
lack motivation. You're probably playing the wrong game. Your algorithm
is not tied to creating a specific outcome that matters
to you. You're not really sure what you're trying to do.
It's really hard to hit a target if you don't
know what it looks like, or how far away it is,
or what direction the wind is blowing from, or what
(21:47):
tool you're using to launch your arrow. Coaching is a
great way to figure out what really matters to you.
It seems like it should be obvious, but oftentimes it's not,
or you feel like everything matters. Ugh, that's exact, but
also not true. I love watching as a client figures
out not the quote right answer, but the true one,
(22:09):
the deep felt sense of yes, this is what I
care about Once you're clear, the awesome thing is that
you can ignore all the other nonsense that's constantly popping up.
You can let that swarm of shoulds fall away, the
fog clears, and it gives you so much more leverage.
It's like your jiu jitsu cross grip seaclamp. You take
(22:33):
firm and unyielding control of what really matters and still
have all of your energy and strength available because you're
not wearing yourself out trying to pay attention to everything
that doesn't matter, or trying to show off how strong
you are compared to someone else. Writing algorithms is the
language of the future. It's what allows Tesla's to drive themselves,
and it's not slowing down. Algorithms decide what Facebook and
(22:57):
YouTube ads you see, and a bunch of other creepy stuff.
I'm sure there's artificial intelligence that could pick your ideal
spouse or the ideal genes to pass to your baby.
Machine learning is a subset of artificial intelligence that uses
statistical techniques to progressively improve performance using data. If we
pay attention, every minute of our life can give us
(23:17):
data that we can use to improve, we can get
better at what we're trying to achieve without having to
be completely reprogrammed. It's about marginal gains, tiny improvements. I
think this can be even more powerful than installing the
algorithm in the first place. The sublime comes through the
(23:38):
mundane once we know what it is we care about
and are actually trying to do. Making tiny adjustments based
on the data is what wins the game, and I
think it should be a game, one big fun game.
Algorithms might sound boring to you, maybe not, but it
doesn't have to be heavy or serious. We can be
playful with our design and see any problem as just
(24:01):
bugs in the code, not moral failures or reasons for
personal shame. Every operating system needs some debugging, at least
I think. I don't know much about computer programming, but
I think it's totally normal for there to be multiple,
multiple iterations before things start to run smoothly. It's part
of the process, so you can expect it and welcome it.
(24:24):
Don't begrudge you're so called negative data. It's the very
thing that helps you spiral up. Have you thought of
some of the algorithms that are already running on autopilot
in your life? I wish we could sit around together
and share what comes to mind for each of us.
I am working on a free community that I think
is going to be awesome, so check the show notes.
(24:44):
I think i'll have it done by the time this publishes.
It would be so awesome to hear from you. I
wish I could hear some of your ideas that could
help me. But here are a few of mine that
I thought maybe might help you get your wheels turning.
If I get frustrated, then I go ah, it's kind
of like a Marge Simpson sound. But when I do that,
it alerts me to slow down and think, Okay, what
(25:07):
do I want here and why? It shifts me out
of just fuming into problem solving. Okay, I want there
to not be pickle juice all over the floor. Okay,
I want traffic to move faster. I want to not
have to hear anyone eating potato chips. Sometimes I can
do something about it, but even when I can't, just
(25:29):
knowing the why can help me be more productive. Like
I want traffic to move faster so I can get
home and relax. Okay, how can I relax now? Put
on a comedy show in the car. Another one is
if I feel nervous, then instead of trying to calm down,
I say I'm excited. That's part of a longer chat
about using fear positively. Or if I sit down on
(25:51):
my desk, then I set my movement timer for seventeen minutes.
If the timer goes off, then I at least stand
up out of my chair. If I wait up on
a Monday, Wednesday or Thursday morning, then I put on
my workout clothes and head to the gym. If it's
the end of the week, then I do my weekly
preview to plan out my upcoming week. One thing that
has been super helpful for me lately is that I
(26:12):
do this little nightly reflection at the end of every day.
I've been doing it for a while. I write down
several things like what was noteworthy today, one thing I
did well, one thing that I didn't do well and
needs work, one thing I'm going to be proud of
in the future, and inside I have five things I'm
thankful for, so on. But I just added a why
after the win and the loss of the day, and
(26:32):
that has been super helpful in my machine learning process.
Most of my positive whys are because I specifically and
exactly scheduled it into my day, and most of my
negative ones are because I ran out of time or
it wasn't on the schedule. That's data I can use.
It's helpful. I hope you're interested in learning the language
(26:53):
of the future. Algorithms are kind of nerdy, but not
as nerdy as Jawa ease, droid speak, or cling on.
So I hope you'll think through how you can use
your well power strategically to install algorithms that make getting
everything you ever wanted the natural byproduct of allowing your
well designed systems to run on autopilot. If you need
(27:14):
someone to help you figure out the outcomes you're going for,
the data you need to collect, or the algorithms you
need to install, I'd love to be the one to
help you create a plan that ensures that you're on
the track you want to be on to stay strong
and resilient for many decades to come. You can click
the link in the show notes or go to Healthcourage
dot ck, slash page, slash interest. Next week, we're going
(27:36):
to talk about a really cool sweetener that's actually good
for you. You want to know about it Until then,
speak the language of algorithms and don't be normal. Thank
you so much for tuning into the Health Courage collective podcast.
I am truly honored that you have paid me the
enormous compliment of your time and attention. I would be
(27:57):
so grateful if you would share this podcast with someone
you know and subscribe so you never miss an episode.
This podcast is for entertainment and information purposes only. Statements
and views on this podcast are not medical advice. This podcast,
including Christina Hackett and producers, disclaim responsibility for any possible
adverse events by use of information contained hearing. If you
(28:18):
think you have a medical problem, consult a licensed Positions
You can make your healthy choices the choices you wish
you'd just make automatically automatic. If you know the language
of the future, it's not Jawa, ease or cling on.
It's the language of algorithms, and it can make your
life amazing. Algorithms are just a process or set of
rules to be followed in a series of steps that
(00:21):
can be used to make calculations, resolve problems, and reach decisions.
Whether you know it or not, your life is already
a series of algorithms. If this, then that you can
leverage this language to install algorithms that run on autopilot
that create a life that will blow your mind. You're
(00:41):
listening to the Health Courage Collective Podcast, Episode two hundred
and two, Make Healthy Choices Automatic with Algorithms, Originally published
July twenty seventh, twenty twenty two. Welcome to the Health
Courage Collective Podcast, the show for women who are too
busy to slog through hours of generalized, in applicable, and
often contradictory health information, but too smart to ignore that
(01:05):
a few minutes of focused attention now can prevent years
of suffering in the future. I'm your host, Christina Hackett,
a pharmacist who doesn't want you to live on prescriptions,
a certified coach specifically trained to maximize your potential, and
a compulsive learner obsessed with preventative, cutting edge, holistic and
integrated medicine. I'm on a mission to increase your physical
(01:26):
and mental resilience so you can fearlessly look forward to
your next forty plus limitless years. Your time is count
Let's go. I'm my friend, and welcome to the episode.
I hope your day is going great today May so
that's usually a good thing. Have you woken up during
the middle of the night lately? I hope not, But
(01:47):
if you do, I hope that understanding the three and
a half different systems that control sleep is useful for
you to find ways to stay asleep all night. A
few years ago, we had my then twelve year old
now fifteen year old son Kipland tell us about our
call to adventure at the same time. Back then, I
asked him a few questions about his obsession with speed cubing.
(02:11):
We didn't really plan it out ahead of time, so
I wasn't prepared with questions and he wanted to go
eat lunch first, but I made him tell me a
little bit about his speed cubing. First, he got a
special Rubik's cube, like at least a couple of years
ago that you can turn more quickly. It has magnets
in it. Anyway, he played with it very rarely, but
then a few months ago he got obsessed with it
(02:33):
and he watched these videos over and over and practiced
and practiced, and now he can solve it ridiculously fast,
he said. He forgot to say that a lot of
the procedure he follows has to do with the rhythm
and the feel and the sound of the cube. Sometimes
I would see him practicing with his eyes closed, which
seems like it wouldn't work well for a Rubik's cube,
but it does. He's a goofy one. So here's what
(02:54):
he had to say. All right, welcome back to the podcast, Kip. Okay,
it's been a few weeks, but really it's only been
like one minute. Okay. So I wanted to ask you
about algorithms. So one day you disappeared in the basement
and you kept watching something on YouTube, right, and that
(03:17):
I was trying. I don't remember. I think you hadn't
eaten all day or something, and I was trying to
get you to eat lunch. But what what you were
doing was learning what do you remember algorithms? An algorithm for.
Speaker 2 (03:31):
For a Rubic's cube.
Speaker 1 (03:32):
A Rubic's cube, So how to solve a Rubic's cube?
Speaker 2 (03:35):
Right?
Speaker 1 (03:35):
Yeah? Okay? And what made you decide you wanted to
learn this algorithm?
Speaker 2 (03:41):
It was a faster way to do to accomplish a
goal on it.
Speaker 1 (03:45):
Okay, all right, so you have your little cube with you, yes,
how so it's not a regular Rubics cube, it's a
speed cube, right, yeah.
Speaker 2 (03:54):
It does the same thing though, right, it just turns faster.
Speaker 1 (03:57):
It turns faster. It's not like the Rubik's cube I
had when I was little.
Speaker 2 (04:01):
That it's the same basic concept, right.
Speaker 1 (04:05):
Mine was black and it had stickers so you could
pull the stickers off. I even had one that I
got from a box of checks. Well, I think I
had to have several boxes of checks, and then I
sent in a thing in the mail and I had
to checks one and so it had like all wheat
checks on one side and all rice checks on one side.
That was my Reubx cube. Okay, so can you tell
(04:30):
us how long did it take you to learn the algorithm?
To the point where you had it memorized and you
could solve.
Speaker 2 (04:36):
Your cube one algorithm or all of them.
Speaker 1 (04:39):
Well, to the point where you could solve the whole cube.
Speaker 2 (04:42):
Probably three or four days.
Speaker 1 (04:45):
Oh okay, I just know you kept watching this thing
on YouTube and I was trying to get you to
eat lunch or something, and you kept watching it. Okay,
So it took you three or four days. And how
long does it take you if someone gave you a
completely messed up Rubik's Cube? How long would it take
you to solve it?
Speaker 2 (05:02):
Uh? With those algorithms, probably around a minute. Oh, but
I learned some new ones.
Speaker 1 (05:09):
Oh okay, So now I know you were timing yourself.
How long does it take you to solve one?
Speaker 2 (05:15):
Uh? My fastist is twenty eight seconds.
Speaker 1 (05:19):
Twenty eight seconds to solve a completely messed up Rubik's Cube? Yes, okay,
and you're messing with it right now. I don't know
if they can hear in our house, we hear a
lot of Rubik's Cube messing around with, right, Yeah, like
even during movies, and it has to get taken away. Okay,
So can most people in the world solve a Rubics
(05:41):
cube in twenty eight seconds.
Speaker 2 (05:43):
No, No, a lot of people can, but not most people.
Speaker 1 (05:46):
Yeah, definitely not most people. What would most people think
about being a like if they gave you a messed
up Rubik's cube and you could solve it in twenty
eight seconds, what would they think?
Speaker 2 (05:55):
I don't know. They've told a lot of people that
don't really care.
Speaker 1 (05:59):
Okay, but most but you know, if they.
Speaker 2 (06:02):
They might be. They might think it's kind of cool
because they can't do it.
Speaker 1 (06:06):
Yes, so they might think you're like a genius because
they can't do it, but you can, right.
Speaker 2 (06:11):
Yeah, But in reality, anyone can do it with practice.
Speaker 1 (06:15):
Mm hmm and learning the algorithm, right, yeah, So can
you describe a little bit, like really short of what
the algorithm is.
Speaker 2 (06:24):
An algorithm is basically a series of turns you have
to memorize to do a specific goal.
Speaker 1 (06:32):
Mm.
Speaker 2 (06:33):
Okay, So if I wanted to switch two pieces in
a place, then like if I wanted to switch two
of the top edges, then there's one algorithm that does
that and it switches their place.
Speaker 1 (06:51):
But do you just solve one side first and then
do the other sides?
Speaker 2 (06:54):
Yeah? Pretty much really, although there's the more advanced method
that I use. Do you solve two sides at a time. Okay,
I don't want to be too loud for this. It
gets pretty loud. Yeah, look, you solve two sides at
a time.
Speaker 1 (07:12):
Some algorithms.
Speaker 2 (07:13):
Let's see if I can get an example. If I
want to solve this part right here, there's an algorithm
for that. And algorithms don't always do the same thing,
so you sometimes you might have to like do a
left hand bruis have the same algorithm.
Speaker 1 (07:33):
Hmm. Okay, And you said something important which you have
to know where you're starting, and you have to know
what your goal is, right, yes, okay, and then now
it's solved. I know you guys can't see it, but I.
Speaker 2 (07:51):
Was trying to not be loud. It gets pretty loud
if I'm trying to.
Speaker 1 (07:54):
Go fast, right, But yeah, it's solved now, okay, Because
ever since you were like six or something, you've liked
coding on computers, right, Like, I remember, I have a
picture of you in a little tank top when you're
like six years old, and your feet don't even reach
the edge of the couch cushion and you have dad's
old laptop and you're like coding a snowman or something.
Speaker 2 (08:18):
Yeah.
Speaker 1 (08:18):
Yeah, So when you write computer code, what is it
that you like about writing computer code?
Speaker 2 (08:25):
Uh, it's just creating something achieving a goal.
Speaker 1 (08:30):
Kay, what was I gonna know? It seems like I
had a good question about this and now I can't
remember what it was.
Speaker 2 (08:35):
Is it about debugger?
Speaker 1 (08:38):
I don't know. Do you even have something to say
about debugging?
Speaker 2 (08:41):
I mean, I don't know who it relates to the topic.
Speaker 1 (08:45):
Well, you can say it, and if it doesn't then
we'll cut it out.
Speaker 2 (08:48):
Uh. Debugging is basically, if you have a problem with
your code or you need to fix it, then you
have to figure out how to find it and take
it out.
Speaker 1 (08:59):
Okay, So that is there a process of how you
find the bug in the code.
Speaker 2 (09:06):
Yeah, you could say there's an algorithm for it. Okay,
you'll first you figure out what's going wrong with the code.
So if you're making a code to like, I don't know,
my brain comes up with bad examples if you can.
If you're making a code to draw I don't know,
to give you bananas, but then it gives you an
apple instead, then you'd you'd know, oh, it's giving me
(09:29):
apples instead of bananas. So then that's the first step.
After that, you need to find the part of the
code that tells them makes it give you a banana, okay,
And then you have to figure out what's wrong with that.
So maybe you wrote give apple instead of give banana
(09:49):
if it's really simple code, or maybe if you're using numbers,
then you just put in the wrong number and you
have to fix it. Sometimes it's really annoying.
Speaker 1 (09:59):
It's annoying, but does it help you solve the problem
in the end?
Speaker 2 (10:03):
Yesh?
Speaker 1 (10:04):
Yeah? But basically, how would you sum up algorithms? Uh?
Speaker 2 (10:09):
J Just basically a I don't know if you would
call it simple, but it's a series of actions you
do to achieve a goal.
Speaker 1 (10:21):
Kay. Good, I like that definition. I know that in
the context I'm gonna talk about it and you're doing
this all just off the top of your head. But
for me, I've been thinking about it and we talk
about if then. So when you write an algorithm, like
if you're gonna program our roomba, which we never even
use anymore, to vacuum, then it it has a series
(10:44):
of if I bump into a wall, then I do this, yeah,
And if I encounter dirt, then I spin in a
circle and clean extra and if I hit the edge
of a stair, then I stop and turn around. That
kind of thing. Would you say, that's an algorithm? Yeah, okay,
the Rubik's cube is getting solved again. All right. The algorithms,
(11:08):
the if and the then can teach us to do
things that we didn't realize that we could have done before,
like solve a rubik quban twenty eight seconds. Right. Yeah.
Speaker 2 (11:17):
People might think it's complicated, but really just need to
learn the algorithm.
Speaker 1 (11:21):
Mm hmm and follow the if this, then that, and
then it's done pretty quick. Okay, cool, all right, thank you.
You're now allowed to eat your lunch. Yay, thanks for
being on. We'll have you back later. Okay, okay, so
we're talking about algorithms. I'm sure you've heard the term before,
(11:42):
but how would you define an algorithm? The dictionary says
it's a process or set of rules to be followed
in calculations or other problem solving operations, especially by a
computer professor. You've all. Noah Harari says that algorithms are
the most important concept of the twenty first century. And
he's a historian, so that's kind of interesting that that's
(12:04):
his prediction for what's most important for our present and
our future. He says, quote, if we want to understand
our life and our future, we should make every effort
to understand what an algorithm is and how algorithms are
connected with emotions. End quote you've all knowah Harari says
that an algorithm is a methodical set of steps that
(12:26):
can be used to make calculations, resolve problems, and reach decisions.
No matter our profession or propensity toward math or computer programming,
we all use algorithms all the time. If you measure
something in inches and want to know how many centimeters
it is, you multiply it by two point five four.
That's an algorithm. If you want scrambled eggs, you crack
(12:49):
the egg into the bowl, whisk, heat up a pan,
pour it in Stirwallli cooks. If I want to gain strength,
I lift my weights, write down what I'm able to do,
and then next time I lift a little bit more
and write it down and note how I felt, get
proper rest, and then repeat. If I want to solve
a Rubik's cube, my algorithm is to try to get
(13:10):
a few squares of the same color on the same
side together. Then watch as I have to move some
out of place in order to get another one where
I want it. If I were given a great deal
of time, I could probably solve one side, while all
the others would just be a jumble. This isn't because
I'm stupid or a failure at life, or lazy or
(13:31):
oppressed by the man, or whatever people like to blame
things on these days. I just don't know the algorithm
to solve it properly or quickly. I'm still using an algorithm,
just not an effective one, but just a day or
two of practice using upgraded algorithms, and my little Kip
is a twenty three second Rubik's Cube prodigy. Okay, prodigy
(13:54):
is an overexaggeration, but it's impressive to watch, and I
think he deserves to be proud of it. Hey, quick question,
have you ever tried blue light filtering glasses yet? You
probably know that the blue light coming out of all
your screens can strain and possibly damage your eyes, so
protecting your eyes is a good idea. I've tried a
few different kinds of blue light filtering glasses over the years,
(14:15):
but lately my eyes were physically aching after looking at screens,
so I got a pair of glasses from Eye by
Direct with Sight Relax lenses that are designed to relieve
eyestrain in addition to blocking blue light. They have a
really subtle magnification at the bottom, which I wasn't sure
I would like, but I really do, and the lenses
(14:35):
are a lot more clear. I always felt like my
other blue light lenses were never all the way clean.
If you've never tried Eye by Direct, there's a link
in the show notes for ten dollars off your first
pair of glasses that you can use, in addition to
fifteen percent off your first pair. They have great deals
on prescription glasses and sunglasses in addition to blue light glasses,
and they have so many different kinds of frames to
choose from, from really conservative ones to really bold. My
(14:59):
Sight Relax glasses look like tortoise shell, but it's actually
little flowers on the frame. Cute. Okay, back to the episode.
Since we're all running off algorithms anyway, maybe it's time
we get intentional about our unconscious programming. We've talked about
Ray Dalio in the past. He's one of the wealthiest
and most influential people on the planet, running the best
(15:20):
performing hedge fund in the world. He says that quote,
your children and their peers must learn to speak the
language of algorithms, because it will soon be as important
as any other language. End quote. The language of algorithms
is the language of the future. It's an if then language.
(15:42):
A super common algorithm we can run is setting our thermostat.
If the temperature dips below sixty nine, then turn on
the heat until it gets back to sixty nine. If
the temperature gets above seventy nine, then turn on the
air conditioning until it gets down below seventy nine. We
can set up that algorithm and think about it again.
It just runs in the background, keeping us comfortable. If then,
(16:07):
what algorithms are you currently running? I guarantee you have hundreds,
if not thousands, that are already humming in the background
on autopilot. If I wake up in the morning, then
if I need to go to the bathroom, then if
I feel first, then if I get a push notification
on my phone, then if I feel fear, then if
(16:31):
someone cuts me off in traffic, then if someone offers
me a piece of birthday cake, then if my child
doesn't do what I asked, then if someone is kind
to me, then it can go on and on and on.
We don't consciously evaluate and make decisions about every second
(16:53):
of our lives, and that's a good thing. We're meant
to be efficient. But it kind of reminds me of
that Peter Drucker quote that says there is nothing so
useless as doing efficiently that which should not have been
done at all. It's not quite the same context. But
what if we became efficient using almost no energy to
run awesome algorithms on autopilot. I think we'd end up
(17:14):
looking like little prodigies and it would be something to
be genuinely proud of. But it's not because we lucked
out or were blessed by a fairy godmother or whatever
people tend to think about people who appear to have
superhuman willpower. It's like my Rubik's cube algorithm compared to
my sons. We both have them, but a little bit
(17:36):
of upfront work can install automatic and nearly effortless algorithms
to run on autopilot. For Mother's Day this year, my
husband signed me up for a women's self defense jiu
jitsu class because he knows I love that kind of stuff.
If I pass the tests at the end, which isn't easy.
I'll get a pink pelt cute, huh. Jiu Jitsu is
about the weak overpowering the strong. It's about the technique.
(18:01):
At first, they kept explaining stuff to us as being
so great because they would say it takes no energy
to do this, or it takes no strength to do this,
and I was kind of like, I have energy and strength.
I worked really hard for them, Thank you very much.
How pathetic do they think I am? But then I
gradually came to realize that the power of jiu jitsu,
(18:22):
or at least the situations I'm training for, is that
I can incapacitate someone, even someone quite strong, without breaking
a sweat. Sure, I have a reserve of power that
I can deploy if the need arises. But the beauty
is in the fact that I can overpower the attacker
without having to exhaust myself, and that opens up so
(18:42):
many more possibilities for what I can do with the
energy and strength that I didn't have to use just
to stay alive. Just by saying this, I'm realizing that
every single thing I've ever learned in that class is
just an algorithm. If you pin me on the ground
with a knife to my throat. Then I grab your
hand with a cross grip sea clamp and move to
a certain pattern that ends with me tearing your rotator cuff.
(19:03):
If you grab me from behind to carry me off
to your creepy van, then I bend and grab in
a certain pattern that ends with me hyper extending your knee.
It's all about building muscle memory to be able to
do it automatically when emotions are high. But it's all
just algorithms. If this, then that, And the point I
was trying to make is that, just like if you
(19:24):
heard that some bodybuilder guy attacked a woman with a
knife and ended up with a dislocated shoulder and knee,
you'd assume the woman was superhuman. But if you can
live your life with algorithms humming in the background on
autopilot that take no energy or strength at all, you'll
appear to have superhuman willpower, And more importantly, you'll still
have all of your energy and strength to put into
(19:46):
something other than surviving the attacks of modern society. You'll
be able to blow your own mind by doing things
you never would have thought you'd be capable of. How
cool is that?
Speaker 2 (19:57):
So?
Speaker 1 (19:58):
How do we know whether the algorith rhythm we're running
are any good? Well, if I got a room ba vacuum,
how would I know whether it was operating from an
effective algorithm. If I walk through the kitchen and bare
feet and a little Rubik's Cuber's cheese at crumbs stick
to my feet, then I know there's a problem with
the room as operation. I know because it's not getting
me the result I want, which is a clean floor.
(20:20):
So we start with the result we want to create.
What do I want is the byproduct of running my systems,
the achievement of my goal. Another thing to consider is
if my room bus successfully sucks up the cheese at crumbs,
but then promptly falls down the stairs or dies before
it can get back to its charging station. That's also
(20:40):
a problem because it's not an easy or sustainable automatic program.
Kiplin talked about algorithms being a methodical set of steps
you take to achieve a specific desired outcome. So the
first thing we need to do is figure out what
we want our algorithm to produce. What is our goal.
It sounds easy and obvious, but I see this with
people who want my help quote getting healthier, awesome. I
(21:05):
love those people. Thank you. I'm happy to do that,
But what does that mean? How wouls you know whether
you succeeded if you use your fantastic new language skills
to write algorithms because it's something you've seen your muscular
neighbor do, or something a fitness influencer proselytizes. Of course,
it's not going to be easy. It's not your fault.
(21:27):
It's not because there's something wrong with you or you
lack motivation. You're probably playing the wrong game. Your algorithm
is not tied to creating a specific outcome that matters
to you. You're not really sure what you're trying to do.
It's really hard to hit a target if you don't
know what it looks like, or how far away it is,
or what direction the wind is blowing from, or what
(21:47):
tool you're using to launch your arrow. Coaching is a
great way to figure out what really matters to you.
It seems like it should be obvious, but oftentimes it's not,
or you feel like everything matters. Ugh, that's exact, but
also not true. I love watching as a client figures
out not the quote right answer, but the true one,
(22:09):
the deep felt sense of yes, this is what I
care about Once you're clear, the awesome thing is that
you can ignore all the other nonsense that's constantly popping up.
You can let that swarm of shoulds fall away, the
fog clears, and it gives you so much more leverage.
It's like your jiu jitsu cross grip seaclamp. You take
(22:33):
firm and unyielding control of what really matters and still
have all of your energy and strength available because you're
not wearing yourself out trying to pay attention to everything
that doesn't matter, or trying to show off how strong
you are compared to someone else. Writing algorithms is the
language of the future. It's what allows Tesla's to drive themselves,
and it's not slowing down. Algorithms decide what Facebook and
(22:57):
YouTube ads you see, and a bunch of other creepy stuff.
I'm sure there's artificial intelligence that could pick your ideal
spouse or the ideal genes to pass to your baby.
Machine learning is a subset of artificial intelligence that uses
statistical techniques to progressively improve performance using data. If we
pay attention, every minute of our life can give us
(23:17):
data that we can use to improve, we can get
better at what we're trying to achieve without having to
be completely reprogrammed. It's about marginal gains, tiny improvements. I
think this can be even more powerful than installing the
algorithm in the first place. The sublime comes through the
(23:38):
mundane once we know what it is we care about
and are actually trying to do. Making tiny adjustments based
on the data is what wins the game, and I
think it should be a game, one big fun game.
Algorithms might sound boring to you, maybe not, but it
doesn't have to be heavy or serious. We can be
playful with our design and see any problem as just
(24:01):
bugs in the code, not moral failures or reasons for
personal shame. Every operating system needs some debugging, at least
I think. I don't know much about computer programming, but
I think it's totally normal for there to be multiple,
multiple iterations before things start to run smoothly. It's part
of the process, so you can expect it and welcome it.
(24:24):
Don't begrudge you're so called negative data. It's the very
thing that helps you spiral up. Have you thought of
some of the algorithms that are already running on autopilot
in your life? I wish we could sit around together
and share what comes to mind for each of us.
I am working on a free community that I think
is going to be awesome, so check the show notes.
(24:44):
I think i'll have it done by the time this publishes.
It would be so awesome to hear from you. I
wish I could hear some of your ideas that could
help me. But here are a few of mine that
I thought maybe might help you get your wheels turning.
If I get frustrated, then I go ah, it's kind
of like a Marge Simpson sound. But when I do that,
it alerts me to slow down and think, Okay, what
(25:07):
do I want here and why? It shifts me out
of just fuming into problem solving. Okay, I want there
to not be pickle juice all over the floor. Okay,
I want traffic to move faster. I want to not
have to hear anyone eating potato chips. Sometimes I can
do something about it, but even when I can't, just
(25:29):
knowing the why can help me be more productive. Like
I want traffic to move faster so I can get
home and relax. Okay, how can I relax now? Put
on a comedy show in the car. Another one is
if I feel nervous, then instead of trying to calm down,
I say I'm excited. That's part of a longer chat
about using fear positively. Or if I sit down on
(25:51):
my desk, then I set my movement timer for seventeen minutes.
If the timer goes off, then I at least stand
up out of my chair. If I wait up on
a Monday, Wednesday or Thursday morning, then I put on
my workout clothes and head to the gym. If it's
the end of the week, then I do my weekly
preview to plan out my upcoming week. One thing that
has been super helpful for me lately is that I
(26:12):
do this little nightly reflection at the end of every day.
I've been doing it for a while. I write down
several things like what was noteworthy today, one thing I
did well, one thing that I didn't do well and
needs work, one thing I'm going to be proud of
in the future, and inside I have five things I'm
thankful for, so on. But I just added a why
after the win and the loss of the day, and
(26:32):
that has been super helpful in my machine learning process.
Most of my positive whys are because I specifically and
exactly scheduled it into my day, and most of my
negative ones are because I ran out of time or
it wasn't on the schedule. That's data I can use.
It's helpful. I hope you're interested in learning the language
(26:53):
of the future. Algorithms are kind of nerdy, but not
as nerdy as Jawa ease, droid speak, or cling on.
So I hope you'll think through how you can use
your well power strategically to install algorithms that make getting
everything you ever wanted the natural byproduct of allowing your
well designed systems to run on autopilot. If you need
(27:14):
someone to help you figure out the outcomes you're going for,
the data you need to collect, or the algorithms you
need to install, I'd love to be the one to
help you create a plan that ensures that you're on
the track you want to be on to stay strong
and resilient for many decades to come. You can click
the link in the show notes or go to Healthcourage
dot ck, slash page, slash interest. Next week, we're going
(27:36):
to talk about a really cool sweetener that's actually good
for you. You want to know about it Until then,
speak the language of algorithms and don't be normal. Thank
you so much for tuning into the Health Courage collective podcast.
I am truly honored that you have paid me the
enormous compliment of your time and attention. I would be
(27:57):
so grateful if you would share this podcast with someone
you know and subscribe so you never miss an episode.
This podcast is for entertainment and information purposes only. Statements
and views on this podcast are not medical advice. This podcast,
including Christina Hackett and producers, disclaim responsibility for any possible
adverse events by use of information contained hearing. If you
(28:18):
think you have a medical problem, consult a licensed Positions
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