Chủ Nhật, 7 tháng 10, 2018

Waching daily Oct 8 2018

Tupac is alive with Kanye West?

Hear me out.

Son of former CEO Death Row Records

Suge Jacob Knight

post a fake picture of Tupac Shakur

with Kanye West on October the 5th

with the claim that Pac and Kanye

have a song together.

Just like the Tupac is Alive hoax video

I am going to debunk this claim

with simple facts.

To provide you the truth

and to cleanse the name of Tupac

Here you see the post on his official

Instagram account.

Left Pac, right Kanye.

Knight writes:

Pac and Kanye have a song together

They, as referred to the Illuminati.

Striking detail what a normal person

can see is the age.

It is not that they put a magical cream

on their face that they still look so young..

What you are looking at are two photos

photographed in two different timelines

and put together.

Kanye West has been added to this photo.

Photo 1: Tupac

MTV Awards after party

made on September 4, 1996.

That is 9 days before the d[***]h of

Tupac Shakur.

Here is the original photo.

And here are some more honorable

pieces of evidence.

Pay close attention to the cup

he's holding, jewelry.

Photo 2: Kanye West

The 49th Annual GRAMMY Awards

made on Feb 11, 2007

with Jamie Foxx.

The question is,

Why is Suge Jacob Knight doing this?

I can not say why he does this.

But my feeling says he wants attention.

Now tell me in the comment section

why Suge Jacob Knight is hoaxing?

For more info open video description

If you like what you've seen.

Subscribe, hit the bell icon next to it

and share this video to support my work.

At lionsground.com and

patreon.com/lionsground

I will explain how to research this.

I'll see you tomorrow.

For more infomation >> Tupac Is Alive With Kanye West? - Duration: 2:04.

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[CS198.1x Week 1] Practical Byzantine Fault Tolerance - Duration: 4:16.

We'll end here with another famous consensus algorithm: Practical Byzantine Fault Tolerance,

or PBFT. Published by Miguel Castro and Barbara Liskov in 1999, the original paper posed a

solution to the Byzantine Generals problem, as we previously mentioned.

Paxos and Raft aren't by default Byzantine fault tolerant, though there are variants

of them – like BFT-Paxos – that are. PBFT was one of the original papers published on

the topic of solving consensus when considering Byzantine faults.

The PBFT algorithm handles less than ⅓ Byzantine faults, as we saw in the section with the

Byzantine generals problem. More traditionally, this has been written as: the system can handle

f Byzantine faults, where there are 3f + 1 total nodes.

It's also really fast. The original PBFT paper showed that when integrated with standard

unreplicated NFS, a distributed file system protocol developed by Sun Microsystems in

1984, the resulting BFT-NFS is only 3% slower, despite the fact that it can now withstand

Byzantine faults.

The main PBFT algorithm consists of three phases – pre-prepare, prepare, and commit.

PBFT begins when the client submits a request to the primary node. The primary node is responsible

for advocating for the client request, and this should be familiar since it's a common

design pattern. For example, remembering back to Paxos, the Proposer proposes new decrees

to other legislators in the Paxon Parliament based on the requests of the people.

In this case, the primary node is Derrick. We have a total of 4 nodes, meaning that we

should be able to withstand 1 fault, since ¼ is less than ⅓. So let's say one of

our 4 nodes, Nadir, drops out due to a spotty internet connection.

Nadir might've dropped out, but the other 3 nodes might not know that yet, so they'll

still send messages to him.

The next step is pre-prepare, which is one of the three main phases – pre-prepare,

prepare, and commit. In the pre-prepare phase, the primary node Derrick sends out pre-prepare

messages to everyone in the network. A node accepts the pre-prepare message so long as

its valid. We won't go too much in detail, but messages contain a sequence number – like

the increasing numbers Proposers in Paxos assign to each of their decrees. They also

contain signatures, and other useful metadata that lets nodes determine message validity.

If a node accepts a pre-prepare message, it follows up by sending out a prepare message

to everyone else. And prepare messages are accepted by receiving nodes so long as they're

valid, again, based on sequence number, signature, and other metadata. A node is considered "prepared"

if it has seen the original request from the primary node, has pre-prepared, and has seen

2f prepare messages that match its pre-prepare – making for 2f + 1 prepares. Again, f is

the number of Byzantine faults.

After nodes have prepared, they send out a commit message. If a node receives f + 1 valid

commit messages, then they carry out the client request and then finally

Send out the reply to the client. The client waits for f + 1 of the same reply. Since we

allow for at most f faults, we need to wait for at least f + 1, and this ensures the response

to be valid. After this point, the client gets the correct response.

Here's a diagram from the PBFT whitepaper, which models exactly the scenario we just

went over. The diagram has five processes, or nodes in our case.

The client is process C. Derrick is process 0, I'm process 1, Gloria is process 2, and

Nadir is process 3.

In the first step, the client sent a message to Derrick, process 0. That's the initial

request. During this time, Nadir fails.

Then, Derrick sends a pre-prepare message to the rest of us processes.

Everyone except Nadir responds with a prepare message.

After acknowledging everyone's presence, we all send the commit message.

After hearing sufficient amount of commits, we respond directly to the client.

For more infomation >> [CS198.1x Week 1] Practical Byzantine Fault Tolerance - Duration: 4:16.

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[CS198.1x Week 1] Proof Stake Overview - Duration: 4:03.

As we mentioned in the Nakamoto consensus section, we can understand both Proof-of-Work

and Proof-of-Stake by looking at what resources are consumed.

In Proof-of-Work, miners have voting power that's proportional to the amount of computational

power that they own.

In Proof-of-Stake, validators are stakeholders that have voting power proportional to the

economic stake -- the native currency -- that they lock up.

The idea here is that the more invested someone is within a Proof-of-Stake system, as in the

more coin they have, the stronger the incentive for them to be good stewards of the system.

Someone with a lot of stake has an incentive to do things that would benefit the system

as a whole since that would increase the value of the coins they hold. So, that's why in

Proof-of-Stake, we give these individuals the most power as validators.

And there's two main flavors of Proof-of-Stake. Chain-based, and BFT-based. The main differences

are in the roles of a chosen validator and its implications on the properties of the

system and its consensus.

In chain-based Proof-of-Stake, a validator is first randomly chosen based on the proportion

of stake they invested. Given a known validator set, the validator who stakes the most coin

is the most likely to be chosen.

After this step, the chosen validator then creates a block which points to the previously

created block.

Then, this validator gets the block reward and transaction fees associated to that block,

assuming that the block is valid and accepted by the rest of the network.

The main observation to make here is that chain-based Proof-of-Stake is like a direct

parallel of Proof-of-Work. Besides the difference in resource consumed – computational power

versus economic stake – the rest of the mechanism is largely the same. Once a miner

or validator is chosen, they create a block that the rest of the network then implicitly

votes on, depending on whether or not they choose to accept the update and append it

to their local copies of the blockchain. This is why chain-based Proof-of-Stake algorithms

tend to choose availability over consistency.

The next flavor of Proof-of-Stake is called BFT -- or Byzantine Fault Tolerant -- based

Proof-of-Stake. BFT-based Proof-of-Stake algorithms choose consistency over availability, in contrast

to the availability-favoring chain-based Proof-of-Stake algorithms.

In the first step of BFT-based Proof-of-Stake, we have the same thing as chain-based Proof-of-Stake:

we randomly choose a validator based on the proportion of stake they have invested.

The differences start in the next step though.

Instead of having a chosen validator directly create a block that is sent directly to the

entire network, a chosen validator proposes the next block. The rest of the validators

then vote "yes" or "no" on whether they accept the block to be valid or not.

If ⅔ or more of the network voting power (⅔ or more of the total stake) votes yes,

then the block is included in the blockchain. Otherwise, a new validator is chosen to propose

a block, and we repeat.

After a block gets more than ⅔ of the network voting power to vote "yes" on it, the

validator that proposed that block gets the block reward and transaction fees.

Historically, there have been a couple major implementations of Proof-of-Stake.

Tendermint was the first BFT-based Proof-of-Stake consensus mechanism, published in 2014. Tendermint

is currently used as the consensus mechanism for Cosmos, the internet of blockchains.

Casper is the planned Proof-of-Stake upgrade to Ethereum. There are two big implementations

of Casper. The first is Casper the Friendly GHOST: Correct-by-Construction, part of a

family of consensus algorithms designed from scratch based on the same safety proof.

The second is Casper the Friendly Finality Gadget, which is a Proof-of-Work and Proof-of-Stake

hybrid.

For more infomation >> [CS198.1x Week 1] Proof Stake Overview - Duration: 4:03.

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VLOG NO PARAMOS DE COMER EN ESTE PAIS! (Subs ESP/ENG) - Duration: 6:03.

Mesmi: You're blocking the camera!

Mesmi: Hello everyone! It's Mesmi! Tomasi: Hello everyone!

Mesmi: What are you doing?

Mesmi: Hello everyone! It's Mesmi!

Tomasi: And Tomasi!

Mesmi: We are at the shopping mall. We are in Lotte mall.

And it's a holiday today. Can you explain what holiday it is?

Tomasi: It's Korean thanksgiving day.

Tomasi: It's almost the same meaning.

Tomasi: During the year, we give thanks for the good....how can I say?

Good...what is it?

Tomasi: Yield? (Harvest)

Tomasi: Something like that.

Mesmi: We are going to continue walking and show you a bit of what we find.

Mesmi: Bye!

Mesmi: We are here with Tomasi who is modeling.

Model that for us please.

How about a twirl?

Mesmi: This is a beer!

What are we watching?

Tomasi: The Nun.

Mesmi: Are you going to be scared?

Tomasi: Of course, yes!

Mesmi: He's scared of alot of things.

He jumps when he's scared.

More than me!

Mesmi: Hello friends! I'm Mesmi and Tomasi!

We are are eating at a restaurant.

I'm sorry I haven't made a vlog, but I will try to make videos.

My makeup is starting to smear.

My hair is messed up.

Mesmi: This is our dinner!

Mesmi: What do you want to say?

Tomasi: Give me....

Mesmi: Say "beer."

Tomasi: "Cermeza?"

Mesmi: "Cer-ve-za."

Tomasi: "Cerveza?"

Mesmi: Deme cerveza.

Tomasi: Deme cerveza.

Mesmi: It means "Give me beer."

Mesmi: Is it ready to eat yet?

It's not ready yet?

You can eat it now or no?

Tomasi: No no no! You can't.

Mesmi: Why?

Tomasi: I told you it's a hot stone.

You should put it like this.

And eat! I'm not good at this.

Where is it? You should use like this.

And...change it.

Mesmi: Hello friends. I'm in Pyeongtaek st.

I am waiting for Tomasi: He was bit upset because he's late.

My birthday is next week on the 13th. He was ordering a birthday present.

He passed the station. He was supposed to get off one before that, but

he didn't get off. Where he was supposed to get off was where he was

going to get on the fast train to get here faster.

But, he couldn't. He's about an hour late.

I came to surprise him to wait for him. I bought him some chocolates.

Hopefully he's not sad anymore.

He's arriving soon, so I'll go look for him.

He doesn't know I'm here.

Mesmi: Where is Tomasi?

I found him!

I got you chocolate!

Mesmi: Hello!

Mesmi: Here you go!

Mesmi: Hey guys! It's Mesmi and I'm with.. Tomasi: Tomasi!

Today we have a challenge.

Tomasi will pick out my makeup.

He will chose many products from different stores.

Tomasi: I want to chose for your lip color.

Tomasi: This means lip gang.

And this one "19+ erotic lipstick."

Tomasi: How do you feel?

Mesmi: Up to you.

Tomasi: Hello Beyonce!

For more infomation >> VLOG NO PARAMOS DE COMER EN ESTE PAIS! (Subs ESP/ENG) - Duration: 6:03.

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Mega Man 11 - Why It Is A Good Game - Duration: 11:36.

- [JAMES] Mega Man 11 for Nintendo Switch, it's good.

(suspenseful music)

Welcome to Stuff We Play,

home of everything weird and retro.

If that sounds cool to you, why not subscribe?

Today, we're talking about a revival for a retro series.

What's funny is that ten years ago, we got Mega Man 9,

which was a reboot of sorts for the classic Mega Man series.

And then, after 2010,

there was kind of a lull in the series,

and now we're at Mega Man 11 in 2018.

That's a reboot again for the Mega Man series.

It's kind of weird,

and this game has been polarizing

since it was first announced.

A lot of people didn't like the new art style.

A lot of people were angry

because it wasn't an 8-bit art style

because apparently, no one's ever liked

a non 8-bit Mega Man game before.

I'll be honest, I was a little skeptical too,

but then I played the free demo

on the Nintendo Switch eShop,

and that skepticism faded.

This is Mega Man 11 for the Nintendo Switch,

a game that's a budget release,

though you can pay full price

and get this sweet, sweet Amiibo Edition.

So let's go ahead and unbox the Mega Man 11 Amiibo Edition.

Now, as you probably gathered

by that very short pre-intro scene,

I actually like the game.

I've beat it three times already.

Four, actually, but the fourth

was the only one that I recorded

so I guess that counts too,

but we'll get into all that in a bit

because I wanna show you what comes

in this really cool Amiibo Edition.

So this is the part of the video

where I'd usually dump out whatever's in here,

except I, more graciously, have already

have taken out the other goodies,

and we have some Mega Man 11 stickers.

We have Mega Man, Dr. Wily, Rush, and the game logo itself.

Side note, I actually like the somewhat small

redesign Mega Man's been given.

But we also have, and this is really cool, this patch.

I'm really big on patches, and we have a Dr. Wily patch,

and I just think that's really cool.

These all seem very high quality.

In fact, here, let's take this little guy out right here.

That is a Dr. Wily patch.

That is very good.

And then, the final thing is, for some reason,

Capcom's had a fetish, I guess, lately

of including microfiber cloths with games.

Another one came with that butchered

Mega Man Legacy Collection release

they did on the Switch, but look at that.

We have an actual boss select.

That's actually really, really cool.

I'm really down for that.

I almost wanna get this framed, almost.

So the last thing we need to look at,

as it's the Amiibo Edition,

is the actual Amiibo itself.

So let's go ahead,

let's put this other cool stuff back in here,

and let's actually take the Amiibo out, and here we go.

I really do like this pose.

I think it's a really cool Amiibo,

but what I like the most,

and I'm not sure if you can see this,

but the base actually has

a Mega Man 30th Anniversary logo on it.

I think that's very cool, that's cool.

But is it worth the extra money?

If you're a Mega Man fan, yeah,

but honestly, why would you buy

a special Mega Man edition if you aren't a Mega Man fan?

But should you buy Mega Man 11

even if you aren't a Mega Man fan,

or if you're a veteran of the series

and are on the fence and haven't made your choice by now?

I have a few thoughts on this,

and right off the bat to start off,

I think that this game has a lot to offer,

whether you're a newbie to the series

or whether you're a veteran.

Keep in mind, in the US, this is a $30 game,

and here in Canada, it is $40.

Budget release, good price.

However, some interesting things to note about Mega Man 11.

Did you know that this is

the first Mega Man game to have been made

that actually runs in a 16:9 aspect ratio?

Did you know that this is one of only three

numbered classic Mega Man games

that is not done in an 8-bit style?

The game itself starts off with the plot,

going into some actual backstory

for like the first time

this damn series has since Mega Man 4.

Dr. Wily and Dr. Light went to college together.

What college?

Robot University.

Yeah, I think the name's dumb too.

But anyways, Dr. Wily was working

on a system there called the Double Gear system,

which could power up robots

to way beyond their main potential,

essentially overclocking them, which was interesting,

but Dr. Light didn't like it because,

first off, it interfered with his research

into making intelligent robots,

and also, if done too much, it could fry your machines.

Dr. Light, being the asshole that he is,

convinced the college board

to defund all of Dr. Wily's research,

sending Dr. Wily himself into a depressive spiral,

which is why he ended up becoming so evil.

Right off the bat, I know Dr. Wily's a bad guy,

and yes, I know within a few seconds

after starting the game itself,

he steals a bunch of Dr. Light's robots

and makes them start attacking people and whatnot.

But I can't help but leave this opening cut scene

feeling like Dr. Light's a bit of an asshole.

So anyways, we get to the game proper,

and we have four difficulty modes.

We have Newcomer Mode, which gives you infinite lives,

makes it so you can't fall into bottomless pits

and that spikes don't kill you instantly.

Really, it's stupidly easy

if you're a veteran to the series,

but I'd say it's really and truly perfect for newcomers.

If you're new to the 2D platformer genre

or legitimately haven't played a Mega Man game

since Mega Man 2 back in the '80s,

then this will be good to get you started.

But if you think that's too easy,

there is a Casual Mode, which is, well, easy mode.

I played this mostly on Normal Mode,

though I also played a bit on Superhero Mode,

and that mode kicked my ass.

I couldn't beat it.

I beat it on the other three difficulties,

including Normal Mode twice,

but Superhero Mode is really, really brutal.

But anyways, so we get into the game itself,

and we have Mega Man, Roll, Dr. Light, and Auto.

Can I just say how much

I actually like the voice acting here?

It's not the best voice acting ever,

but I get this tone like you would

in a hammy Saturday morning cartoon back in the day,

and I honestly really like that.

It's very cartoony and not very serious,

and that's when I think Mega Man is at its best.

- [Dr. Light] It's designed to push robots

far beyond their normal strength and speed.

If he really has completed it,

I'm afraid you won't stand a chance.

- [JAMES] So Dr. Wily gets mad and steals the robots,

and then Dr. Light, once again being an asshole,

decides, oh, Dr. Wily's using his Double Gear system,

so we'll use the same Double Gear system

that I, you know, got defunded and then sent him

into the depressive spiral with, to stop him.

We get thrust right into our boss select

where we have eight different bosses,

and they're interesting.

Let's go into the stage design itself,

and for the most part, it's tried and true Mega Man.

It's 2D run-and-gun platforming,

except you have the new Double Gear system.

By pressing either the L trigger or the R trigger,

you can either slow down time,

and thus make it so you can make the enemies fire slower

and you can dodge things easier or shoot at them more,

or strengthen your own bullets, which is always helpful.

Of course, though, there's a timer,

but what really helps is that,

if you get low on health,

then you can press both buttons together at once

in order to go into this pinch mode

in which you can fire super-powerful weapon energy things.

It's almost like a precursor to the buster parts

in Mega Man X when you get into this mode.

However, the catch is that

all of the bosses can use the Double Gear system as well.

So stage design is actually really solid,

and yeah, some of it's a little gimmicky.

For example, Tundra Man's an ice stage,

and you can slip on the ice unless you buy these boots

that make it so you don't slip on the ice.

And we have Bounce Man's stage,

which has these giant springs, and

these spring balls, which honestly,

I know a lot of people really don't like this stage,

but I think it's a really cool gimmick.

It reminds me of Spring Man's stage in Mega Man 7,

which I also thought was gimmicky yet fun.

What I found odd here, though,

is that a lot of Mega Man games

have mid-bosses in a lot of the stages,

but every single Robot Master stage in Mega Man 11

does indeed have a mid-boss,

and not all of them are winners.

I really don't like this guy in Bounce Man's stage

and this guy in Blast Man's stage,

but overall, I'd never say it feels unfair,

except perhaps on Superhero Mode,

and that's just because of one retro thing

they added to this game that I just don't like,

and that's retro-style knockback.

I mean, at several points,

I felt like I got hit by an enemy

and that I didn't die because it was my fault,

but because of the knockback.

Take this section right here

where I'm on this conveyor belt,

and then I get hit by this enemy

and I can't get out of the stun animation.

I just have to watch as Mega Man

is vaulted off by the conveyor belt

into the bottomless pit and dies.

Graphically, however, I think it does

look a bit like a budget game.

I think it's bright and colourful,

and it definitely looks like Mega Man,

but it certainly doesn't look as sharp as it could.

I'm not expecting a Mega Man game

to look like Breath of the Wild,

but I was expecting something

a little more visually popping than this.

The same, however, cannot be said for the soundtrack.

I wasn't too incredibly crazy

about the soundtrack at first,

but it's grown on me.

Stage select theme, the Wily boss theme, Tundra Man's theme.

I'll be honest.

I think my favorite theme here is Wily's stage one,

but I'm not gonna spoil that stage for you,

except for the fact that the boss

at the end of that stage is clearly nostalgia pandering,

and that's really the only time

this game tries nostalgia pandering,

but I'm not gonna say anything more

about that stage than that.

And now, the one thing I did find odd

was the omission of playable characters

such as Proto Man or Bass like in Mega Man 9 and 10,

mainly as I thought that was a precedent

that was set in Mega Man 9 and 10,

and it's odd not to see them here.

But for an adventure where you just play as Mega Man,

it feels completely tailored

to your abilities as Mega Man, and I enjoy that.

I think Mega Man 11 is worth it,

but do I think it's the best

Mega Man-esque experience out there right now?

Well, first thing's first,

it's sure a hell of a lot better than Mighty No. 9.

That game is a train wreck.

Please act like that game does not exist anymore.

However, I think Mighty Gunvolt Burst

on the Switch is definitely good,

and if you don't feel like buying a new release game

that costs forty bucks, then maybe try that out

if you wanna get some Mega Man goodness.

The Mega Man Legacy Collection,

both the regular and the X collection on Switch, are split,

meaning you get the first collection of each on a cartridge,

and then you have to use a download code for the others,

and as a game that's sold as a physical release,

I find that to be very scummy,

and I cannot recommend in good faith

for you to buy that physically on the Switch.

But if you're a Mega Man fan,

or if you want something

that's a platformer, and that's new,

and that's built from the ground up to run on the Switch,

and that's not a port, Mega Man 11's worth it,

or really and truly, if you want a new game

for the Switch that's not gonna cost a whole ton, and

that's not gonna break the bank, I can recommend this.

I know a lot of people have issues with it.

And I get it, it's Mega Man, and it's a different Mega Man.

It does things differently than Mega Man 9 and 10

and indeed, what I think a lot of people wanted

from a classic Mega Man game.

But I think the overall package here

is something that's different, but in a good way.

It does enough to differentiate itself

from the rest of series and give itself an identity,

but it's still Mega Man,

and more than anything else,

I'm glad Mega Man is back.

Does Mega Man 11 look good to you?

What's your favorite Mega Man game?

Let me know down in the comments section below

before I ramble on more,

and while you're at it,

why don't you subscribe to Stuff We Play

for more great content like this,

or even back us on Patreon

because every dollar from Patreon

does go back into the channel itself.

So-

Wait, you know what?

Last year, I did something called Hacktoberfest

where I looked at one Sonic the Hedgehog ROM hack a week

throughout the duration of October.

Well this year, it's time for Mega Hacktoberfest,

so come back next Tuesday

as we start looking at Mega Man ROM hacks.

For more infomation >> Mega Man 11 - Why It Is A Good Game - Duration: 11:36.

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How to Reduce Performance Pressure On Your Son or Daughter - Duration: 2:51.

- Hey I'm Connor Powers from DeadRedHitting.com.

In this video, what I'm going to talk

to you about is parental pressure,

and what you can do as a parent

to take that pressure off of your son or daughter,

and help them actually reach their

highest levels of performance.

So, I just finished my first course in sports psychology,

and there was a whole entire section

of the book that was completely dedicated to

the impact that parents have on their children

and their ability to perform at their highest level.

And one of the biggest things that they found

in all the studies that they did,

was the more pressure that parents put on their children,

especially at a younger age,

especially in that bracket of 12 under,

shoot, even when you get up to high school,

to perform when they get on the field,

the worse that the players perform.

Now, as a parent, there may be some of you

that are watching this video that are saying,

hey, I never want to put pressure

on my son or daughter, I never.

The issue issue is that it's how

that your child, how the player's perceiving that.

So, while you may not feel like you're doing that,

and you might not be,

if they perceive any pressure,

or perceive that you're putting pressure on them,

it's going to cause them to perform

at less than their highest level of performance.

And so if they are perceiving that

you're putting pressure on them,

and again you may not be, at all,

but if they perceive it, that's when

they're gonna have those negative reactions.

So here are a couple of things that

you can do that are really easy.

After the game's won, tell them they did a good job,

don't even critique them,

don't even bring up their performance.

Just say, hey, good job, good effort out there today,

and just leave it at that.

That's something super simple.

Two, let them know no matter what they do on the field,

they can never disappoint you.

If they go 0 for 4 with 4 strikeouts,

it's not gonna disappoint you.

And when you start to have those

types of conversations,

what it's going to do is it's just going to

alleviate any pressure that they

perceive that you're putting on them,

which is going to allow them to

perform at a higher level,

it's gonna allow them to enjoy their sport more.

Now, there may be some parents that are

watching this video that are saying, oh, garbage,

we need to be really hard on them,

we need to yell at them, we need to critique them

constantly if we expect them to improve.

The reality is, if you want your son or daughter

to have the opportunity to play at the highest level,

to play in college, to play professionally,

that needs to come from within them.

That needs to come from intrinsic motivation.

And if you are constantly all over them,

if you are constantly critiquing them,

that's gonna make them hate the sport,

they will not have intrinsic motivation,

they will not be the best player they can be.

They probably will stop playing before varsity,

or maybe even stop even sooner than that.

So if you do truly want them to be able

to reach that highest level,

it's important for you to make sure,

one, that you aren't putting pressure on them,

two, clear the air with them,

and make them understand that there is no pressure

on them from you as a parent to perform.

For more infomation >> How to Reduce Performance Pressure On Your Son or Daughter - Duration: 2:51.

-------------------------------------------

Oh no! What is he going to do?! [The Return of Superman/2018.09.30] - Duration: 13:28.

(William is playing in the room.)

(He finds his dad's headphone.)

(Shall I listen to some music?)

He pretends the music is playing.

- Right. / - He knows what it's for.

(He shows up with his cute footwork.)

What's going on?

(The song isn't playing, but he's feeling it.)

What are you doing?

Are you listening to music?

How cute.

(You should listen to it too.)

Were you listening to the song?

(I gave him the headphone.)

- Hey. / - Me, too.

- "Me, too"? / - Is there one more?

(Is there one more headphone?)

Come here.

(He goes out with something in his hands.)

(Laughing)

(This is how one uses the hairband.)

Is that a hairband?

- You have one too? / - Goodness.

No, no. That was great. That was creative.

William.

(William)

What did he do?

I've seen this from somewhere.

(It's a scene from the movie "The Party".)

(Even his hairstyle is similar.)

- My gosh. / - He's Willie Marceau.

(Willie Marceau)

(I'm Willie Marceau.)

(I want to enjoy the song by myself today.)

Excuse me. Excuse me.

He gets sentimental. He's only a baby, though.

William. William.

You can't hear me.

You can't hear me.

He's playing a song in his head.

Do you like it?

Meanwhile, this guy is still eating.

(Bentley is singing a song after the rice poppers.)

(Hiccuping)

He doesn't miss out on anything.

(After choosing a song carefully...)

(Bentley, listen to the song.)

(Let me check out your music preference.)

He's so cute.

- He's unbelievable. / - He's staying still.

- What? Is it stuffy? / - He doesn't like it.

Come here.

Should I change the song?

All right.

(Find a song Bentley likes.)

How about this?

(This is also known as "Tuk Soil Song".)

(His face grows stiff.)

He doesn't like it either.

- This is a no again. / - No?

- He's picky. / - I got it.

What about this?

(A soundtrack for "Between Calm and Passion")

He listens to it first like that.

That's the face he has when he sees good food.

He's getting all sentimental now.

- You don't like this? / - He doesn't like that either.

You're quite picky.

(What about a hip-hop song?)

- It's out right away. / - Yes.

He doesn't like loud songs.

Or isn't that just uncomfortable for him?

What about this?

(This is his last song choice.)

(What is this?)

(Smiling)

What is it? He likes it.

(His nostrils widen. He must like the song.)

- His nostrils got bigger. / - What is the song?

Hold on.

(What's the song that won over his heart?)

Do you really like this song?

My goodness.

(It's Yunjung's "Oh, My Goodness".)

(Surprised)

I'm not lying. Babies love my voice.

- Why is that? / - It melts in their ears.

(Brazen)

Doesn't it melt in yours?

Right.

You hate all the songs. You only like this song.

Look at his posture.

- He knows what's good. / - Seriously.

- How cute. / - She must feel proud.

(Are you watching this, Yunjung?)

(Acting cute)

Goodness.

- I'll sing it live for you. / - You're unbelievable.

You've never seen this before, right?

I bet it's your first time too.

(What's that?)

(He starts walking in curiosity.)

He can walk well now.

(While Bentley walks, William is...)

(He comes down in a roughly.)

Be careful.

(He's getting playful day by day.)

What is this?

Don't you like to draw?

You like drawing.

We used that a long time ago.

I don't know.

Is he going to show them calligraphy?

- Watch me, William. / - I guess so.

The baby is holding a brush.

- Brush. / - Brush.

- Inkstone. / - Inkstone.

- Ink stick. / - Ink stick.

Ink stick. You try it too.

(Ink stick? Is that edible?)

Okay. Shall we try this?

- Yes. / - All right.

(Sam kneels determinedly.)

- William. / - Yes.

This is an important project.

I'm going to use the brush.

You must wait.

I'll be back.

Why is he talking like that? Where is that from?

(Is he copying people in period dramas?)

(It's a mess.)

Be on standby. Did you get that?

Sit down.

Dad.

Dad.

You can't call me Dad now.

Your Highness.

(It turns into a period drama.)

King Sam.

- King Sam. / - King Sam.

Or is it Sam the King?

Look. I can't be barefoot now.

This is a serious task.

This is a serious task.

- And he's wearing those? / - Gosh, how colorful.

Look. I'm going to write letters.

(This is a real show with an ink stick.)

All right.

You can't eat that.

Hey.

(How dare you! You brought this upon yourself!)

(Let go of me!)

Sit down.

Sit down. Sit.

Rice poppers.

(He doesn't care if he's exiled.)

You can have this all you want.

Right. That's enough for him.

- How chaotic. / - He bought some time.

(Life in exile with rice poppers)

(Prince Bentley achieved his goal.)

You can have it all.

William, today is a national holiday.

We're going to write our family motto.

William, mix the ink with the stick.

Try it.

Mix it with this.

You're good.

The crown prince is mixing the ink,

and the king is writing the letters.

(The ink got on his finger.)

(He becomes playful again.)

(He approaches his dad quietly.)

What are you doing?

What are you doing?

What did you just do?

(He becomes a commoner from a king.)

- Did you make a mole? / - Yes.

Dad, you're Mr. Mole.

- William made it. / - All right.

- Good boy. / - You're Mr. Oh.

Good.

(Prince Bentley looks at them with affection.)

(Are you happy without me?)

(It's a bit lonely here.)

(I make movements to bring attention to me.)

(But I'm out of their world.)

(You're my only comfort, rice poppers.)

We must be a close-knit family.

We'll write our family motto.

(Glancing at the back of Bentley's head)

(He lifts the brush hesitatingly.)

- Right now, he's / - What is he doing?

getting the urge.

- He got up. / - He lifted it.

- Is he going to Sam? / - Where is he going?

Where is he going?

(With a playful look on his face,)

(he goes to Ben.)

To the baby?

Oh, no. What is he going to do? No.

- No, stop it. / - No!

(Almost touching)

Why is he doing that?

(Ben unexpectedly grow hair.)

He said it was Ben's hair.

Poor Ben!

- Hasn't Sam seen that yet? / - No. He's focused

- on writing the motto. / - What are you doing?

(He draws on his arm to distract Sam.)

He's drawing on his arm now.

(He distracts Sam's attention.)

What are you doing?

Sam didn't see it yet.

(When his dad focuses again...)

Baby, here's your hair.

(William focuses on his artwork as well.)

(Gosh, why is my head so itchy?)

The baby got hair.

The baby is bald, so I...

(The baby is bald, so I made hair for him.)

What? The baby is growing hair?

- He saw it. / - Did you...

Ta-da.

(Is this the globe?)

(Ben the Globe)

What did you do to the baby?

- Just like this. / - I have no choice.

Come here, baby.

- My gosh. / - Gosh.

He's unbelievable.

Since he needs a shower anyway,

- Sam opted to complete the work. / - He shouldn't.

- He likes it. / - He is clueless.

My goodness.

I want to try it.

(Hello, everyone.)

Sam took it too far.

Did you see this?

- How cute! / - Hello!

- My goodness. / - Mario.

Hello.

Are you an Italian man?

Pizza?

(Every baby in the family)

(gets transformed into an Italian man.)

I would like some pizza.

They look similar. They look the same.

William, do you want to do it?

Yes.

William, do you want a tattoo?

(A tattoo?)

(He draws haphazardly.)

William, show Uncle.

Why is he doing that?

- Do you want to be a man? / - Yes.

(A man needs six-pack abs.)

You now have six-pack abs.

He needs to draw one more line.

It looks like a Chinese character.

Bang!

I can't believe them.

Bang! Try it. Bang!

Bang!

(It's time to wash away the ink.)

Let's get washed up.

- I can't believe him. / - What is he doing?

He is making it worse.

I don't know

if you are getting cleaner or dirtier.

That's right.

My goodness.

William, what is up with the water?

- He is so cute. / - Look at the color.

- Look at the color. / - My goodness.

Dad, it's seaweed water.

- Is it seaweed water? / - Yes.

- It's seaweed water. / - Is it seaweed water?

It's our first time to have an ink bath.

Gosh, you have a mustache.

(Bentley still smiles.)

You look like a cartoon character.

The bath isn't making you any cleaner.

(Who are you?)

My goodness.

William, you look like a soldier.

Loyalty.

Loyalty.

Look at this. Who is this?

It's not me.

Then who is it?

It's not me.

Then who is it?

A middle-aged man.

- Is that a middle-aged man? / - Yes.

(I'm back to being bald.)

Are you ready?

- Yes. / - Good.

That's good.

He is wearing hanbok.

The first child, William.

- Where are the children? / - Come on out.

How pretty!

(A pretty young man with smokey eyes appear.)

It didn't get washed off.

There's still ink on his eyes.

(He has showmanship.)

The second child, Bentley. Come on out.

Come on out.

- How pretty! / - My goodness.

(Sam is too busy dancing to see Bentley.)

Dad.

(Pay attention to me.)

(The runway becomes a dance floor.)

Bentley! Bentley!

(The second pretty young man appears.)

Why do you look so cool?

My children are the best.

- You wear hanbok on Chuseok, okay? / - Okay.

Let's read the family motto together.

"Sharing food makes it tasty."

William, don't eat food by yourself.

Okay.

The same goes for you. I won't eat alone too.

William, isn't it cool? It's a Korean costume.

Happy New Year.

(You bow on the New Year's Day.)

(Ben bows as well.)

(Uncles and aunts, Happy Chuseok.)

For more infomation >> Oh no! What is he going to do?! [The Return of Superman/2018.09.30] - Duration: 13:28.

-------------------------------------------

[CS198.1x Week 1] Paxos & Raft - Duration: 7:35.

So before we talk about our first consensus mechanisms, let's take a short break.

We've been going through a lot of technical material, so for now, let's just enjoy some

nice, relaxing images.

All these images are from a beautiful island called Paxos, located off the west coast of

Greece.

It's famous for its wine and is home to some of the finest sand beaches in all of

the Ionian Sea.

Apparently, it's also a very popular yachting location.

Yeah right there on the map, on the west side of Greece, just south of Corfu, is Paxos!

The most important thing though is that not only is Paxos a Greek island,

but also…

a consensus algorithm!

Invented by Leslie Lamport in 1989: the same guy responsible for the Byzantine Generals

Problem, some other very influential papers in the field of distributed systems, such

as Time, Clocks, and the Ordering of Events in a Distributed System, and also author of

LaTeX, the famous document preparation system.

Since publication, Paxos has been used for a variety of use cases, and has spawned an

entire family of consensus algorithms.

Implementations may vary, but here, we'll talk about the basic Paxos algorithm.

The reason Paxos is named the way it is is because the original whitepaper was titled

The Part-Time Parliament, and in the paper, Lamport told the story of how the ancient

Paxon parliament used the Paxos algorithm to pass decrees and make sure everyone on

the island was in consensus of the latest law.

And the reason it was the "part-time" parliament was that legislators in the Paxon

parliament were known to leave sporadically – to attend banquets and other outside activities.

No one was willing to remain in the chambers all the time, and they didn't have secretaries

either, so instead, each Paxon legislator maintained a ledger, where they'd record

everything that happened.

So diving a bit more in depth, the paper specifies that within the Paxon parliament, there are

three types of legislators: proposers, acceptors, and learners.

Proposers champion the requests of citizens, and bring up new bills to others in Paxon

parliament.

Acceptors are legislators that vote, and after consensus is reached, learners "learn"

the result of the consensus and carry out the bill.

In order for consensus to be reached, a majority of acceptors must vote for a new bill.

We call any majority of acceptors a quorum.

A key observation to make here is that any two quorums must overlap.

We'll see later on that the idea of quorums is also very important in other consensus

algorithms, such as those that are considered federated consensus algorithms.

The protocol that the Paxon parliament uses proceeds over several rounds, until consensus

is reached.

Each successful round has two phases: prepare and accept.

First, some citizens will talk to a proposer.

And then after that, the proposer proposes a decree to the Paxon parliament.

A decree has a number and a value, the value can be anything, such as "The sale of brown

goats is permitted," or "Painting on temple walls is forbidden."

However, there's a requirement that the number associated with the decree must be

strictly increasing.

After a decree is proposed, within the parliament, the acceptors discuss.

If at any point in time, a quorum, or majority, of legislators are in the chambers, and all

vote yes, then the decree would be passed, and they'd all write it down on everyone's

ledgers.

After reaching consensus, the learners learn the result, and then let the entire island

know what happened in parliament.

So there's a very clear parallel between the Paxon parliament and distributed consensus.

As Lamport put it in his paper: "There's an obvious correspondence between this database

system and the Paxon Parliament."

Individual nodes within a distributed system are like the legislators, since they're

the ones whose job it is to serve the citizens – the client programs – and come to consensus,

and then broadcast or take action on the result.

And the state of the distributed database is like the current law passed by the parliament.

One major assumption that Paxos makes is that nodes aren't trying to subvert the protocol,

and that messages are delivered without corruption.

In other words, Paxos only works for the fail-stop scenario, and does not account for Byzantine

faults.

In the paper, Lamport describes that the people of Paxos are fairly agreeable and that there's

an atmosphere of mutual trust on the island.

So long as they're in the chambers, all legislators would agree with one another.

The result of this is that Paxos is very fast, and in practice is used to replicate large

sets of data.

At Google, Paxos is used in their Chubby distributed lock service, where it's very important

for everyone to agree to only allow one process to have write access to a certain piece of

data at a given time.

And just to name a few more, Google Spanner, Microsoft Bing Autopilot, Heroku, WeChat,

and WANDisco all use Paxos internally.

The original Paxos whitepaper was known to be pretty difficult to understand, and despite

the original author's efforts to clarify further, with a follow up paper titled "Paxos

Made Simple," it still caused mass confusion – maybe because of the huge Paxon parliament

story.

In 2014, an alternative to Paxos was proposed.

The paper was titled "In Search of an Understandable Consensus Algorithm," poking fun at the

confusion Paxos caused, and even the name of the algorithm – Raft – is meant to

be a joke.

You'd use a raft to escape the island of Paxos.

So the general consensus nowadays is that Raft is a bit more understandable than Paxos,

and easier to implement.

Its design is leader-based, meaning that on each round a leader would be chosen to propose

new updates, and JP Morgan's Quorum, which was designed to be an enterprise version of

Ethereum, swaps out the public Proof-of-Work algorithm that Ethereum uses for a faster

Raft-based consensus.

There's a really good Raft simulation that I'll link in the "Readings" section

for this week, but here's a brief rundown of how Raft consensus works.

Each instance of Raft has one elected leader, who communicates with the client directly.

The leader's responsible for orchestrating the sending of messages to other nodes within

the cluster and for maintaining log replication of everything that happens.

The leader accepts a client request, and then oversees all other nodes to make sure that

they too have followed the request; and then the log of that request is replicated.

The leader leads the whole consensus until it fails or stops, in which case a new leader

is elected.

And the consensus algorithm proceeds in partial synchrony.

The leader is tasked with sending out "heartbeat" messages to other nodes, telling everyone

else that it's online.

And these heartbeats are sent out at regular intervals – like an actual heartbeat.

If no other nodes hear a heartbeat, then they can assume that the leader is dead – failed,

stopped, or crashed – and then they too start an election cycle.

This involves all nodes beginning an internal timer, starting whenever they realize that

they're no longer receiving heartbeats, and the first node to timeout gets to become

the new leader.

And just as a reminder, please check out this awesome raft simulation that we'll link

in the readings section.

It teaches everything we just went over in the previous slide but by example.

All credit and respect to the original author!

For more infomation >> [CS198.1x Week 1] Paxos & Raft - Duration: 7:35.

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[CS198.1x Week 1] Federated Consensus Implementations - Duration: 2:06.

Because of the way that Federated Byzantine Agreement functions, it offers a few nice

properties.

First, like other blockchain consensus algorithms, it offers decentralized control.

No one decides the quorums – except the individuals.

On top of that, the latency is low, as consensus can be achieved with a relatively low cost

of computation.

Finally, trust is flexible.

No node has to trust anyone who they don't want to trust, instead only choosing to listen

to the closest and trusted peers.

The first cryptocurrency to leverage this consensus algorithm is Ripple, which refers

to itself as a real-time gross settlement system.

Ripple targets big financial institutions, offering quick and decentralized cross-border

payments.

Rather than having to create a new branch of trust between banks for every international

payment, sometimes taking up to days, the Ripple network allows these institutions to

self-select trusted peers and maintain a collective history of transactions.

Naturally, Ripple is a somewhat permissioned ledger, as banks need to be authenticated

before getting access to the Ripple ledger.

A note, Ripple is a distributed ledger but not necessarily a blockchain system.

This means that the data structure holding information is not a blockchain, but many

of the assumptions and goals of the system are similar to that of well-known blockchain

projects.

This image quickly represents the makeup of the Ripple network.

Validating nodes participate in consensus, while tracking nodes maintain the history.

Stellar is the public version of Ripple.

It's actually a software fork of Ripple, meaning that the codebase

for Stellar broke off from Ripple.

However, it split off to focus on bringing federated consensus to the common people.

This platform connects all types of institutions and users, from banks to non-profit institutions.

It aims to make wealth accessible.

As we saw in the federated consensus overview, this diagram represents the process by which

someone in the Stellar network might come to a decision on what they want to have for

lunch.

If interested in exploring Stellar, check out the Adventures in Galactic Consensus comic

strips.

For more infomation >> [CS198.1x Week 1] Federated Consensus Implementations - Duration: 2:06.

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[CS198.1x Week 1] Federated Consensus Overview - Duration: 4:15.

Before diving into federated consensus, we're going to refresh ourselves with the definitions

of all the terminology we've picked up thus far.

Remember that a quorum in a distributed system is a set of nodes sufficient to reach agreement.

In PBFT, that's over two thirds of the voters.

We refer to this as a Byzantine agreement within a BFT consensus algorithm.

Let's say you're part of a decentralized distributed system, but you can't choose

which nodes to trust.

How can you and your peers still come to consensus without relying on a central party to dictate

the truth?

This is where the concept of a federated Byzantine agreement comes in.

Rather than relying on a central actor to choose quorums for us or decide the truth

for us, we can individually select who we trust by using a quorum slice.

This is a subset of a quorum that can convince a particular node, aka you, of agreement.

Individual nodes can decide who to include within their quorum slice.

Let's say you're the purple node, the leftmost of this set of four nodes.

You believe a crayon to be red.

The other nodes, who happen to be in your quorum slice, tell you that the crayon is

actually green.

Because you have decided to trust them, you change your opinion, instead coming to consensus

with them that the crayon is in fact green.

You then celebrate and rejoice the ability of trust to permeate through a decentralized

network and allow you all to reach consensus.

Hooray.

You might ask, "What's the point of trusting these people?

Seems arbitrary to me.

Why not just trust a centralized entity if we're handing out trust so freely?"

Keep in mind, these aren't the only nodes in the network.

Let's say you only trust these four out of a thousand nodes, and you only want to

listen to them.

Of course, your follow-up question should be, "If I only trust these four nodes out

of thousands, how does the whole network reach consensus?"

This is where quorum intersections come in.

By having overlapping sets of quorum slices, we can form a larger quorum and ensure consensus

throughout the entire network.

If all nodes share some individually selected trusted entities, then the whole network will

still come to the same decisions without requiring a direct line of trust to any unknown node.

You can trust popular identities with a lot of reputation, nodes which have been in the

network for a while, or anyone else who you believe deserves the right to influence your

decision-making.

In the scenario where these quorum slices don't line up, we get what are known as

disjoint quorums, the pitfall of federated consensus.

If this happens, then there is no way to guarantee that all actors will come to consensus.

We've talked a lot about consensus in theory; how's about going through an example?

Let's say that Rustie, Derrick, and I (Nadir) are trying to figure out what to get for lunch

again.

But this time, our friend group is ever expanding, meaning that new people will join during the

day.

Since we're nice people, we're open to letting others join in, but there's always

trust issues when meeting people for the first time.

Rustie suggests pizza, his favorite dish, making the suggestion to Derrick and myself.

We two, however, know of a secret deal for $1 burgers today.

Rustie, also a fan of burgers,

And also acknowledging the value of the discount, cedes to our preference and decides on burgers

as well.

The three of us celebrate for coming to consensus.

Saroj notices our cheeriness and asks to join our friend group for lunch.

However, he doesn't trust Rustie for whatever reason.

Perhaps because Rustie tried to get pizza on $1 burger day.

Because of this, Derrick and I decide to form a quorum slice with Saroj, meaning we all

influence each other's decision making.

Trust is beautiful, isn't it?

Gloria, seeing how much our love has flourished, also wants to join us.

However, she only trusts Rustie and Derrick.

Rustie and Derrick also trust her, so they form a quorum slice as well.

And tada, we now have two new overlapping quorum slices with Derrick in the middle.

Including the first quorum slice, there are a total of three quorum slices.

The intersections are the most crucial parts, as those ensure that decisions made within

individual quorums propagate to the other parts of the network.

For more infomation >> [CS198.1x Week 1] Federated Consensus Overview - Duration: 4:15.

-------------------------------------------

「LIVE」Splatoon 2 (#32): Almost done with Clam Blitz (famous last words) - Duration: 53:02.

For more infomation >> 「LIVE」Splatoon 2 (#32): Almost done with Clam Blitz (famous last words) - Duration: 53:02.

-------------------------------------------

[CS198.1x Week 1] Proof Stake Implementations - Duration: 12:36.

Tendermint was the first BFT-based Proof-of-Stake consensus mechanism, created by Jae Kwon and

his team in 2014.

To give a bit of historical context, Tendermint brought with it the academic rigor of more

traditional consensus mechanisms such as PBFT, Paxos, and Raft to a space that at the time

was mostly dominated by newer blockchain consensus mechanisms, namely Proof-of-Work and other

Nakamoto consensus mechanisms.

In the beginning, Tendermint was an all-inclusive Proof-of-Stake system with its own native

token to drive incentive, but since then, it has evolved to become a more generalized

middleware for replicating applications on many machines. This allows developers to write

their applications and business logic in any language. These applications talk to what's

called the Application BlockChain Interface, which in turn talks to the Tendermint Core

consensus engine. You can see a diagram of the state machine on the right.

Tendermint is energy efficient and fast, but currently is still testing scalability to

a large number of validators, so today it's primarily being used in production in private,

permissioned environments. It's also the backbone of the Cosmos Network, which aims

to create the Internet of Blockchains and fix the problem of blockchains existing in

silos, unable to communicate with one another.

In Tendermint, we have a globally known and predefined validator set. Consensus proceeds

in three rounds, and at each round, more than ⅔ of the entire validator set must vote

for the proposed block in order to proceed to the next round. Otherwise, if we don't

hear from validators within a certain time, we count their vote as nil. And this reliance

on a timeout makes Tendermint a weakly synchronous protocol, rather than an asynchronous one.

And because it's BFT-based, Tendermint favors consistency over availability. Validators

wait for each other to vote or time out, and come to consensus, before the state of the

application is updated.

Alright, so let's run a little demo to see how the Tendermint consensus engine runs at

a high level.

On the right we have a state machine diagram for Tendermint. And on the left

We have our little demo network. If you've taken the first course in the Blockchain Fundamentals

program, Bitcoin and Cryptocurrencies, then this diagram should be pretty familiar to

you.

So here's how things work: at each new block height, a validator is chosen to propose a

new block.

We've circled this in red. If we were using Tendermint for Proof-of-Stake, like how Cosmos

does it, the validator would be chosen at random based on their proportion of bonded

token.

Then, in the first round,

called "prevote", the rest of the validators can either prevote the block, or prevote nil.

Prevoting the block indicates that the validator marked the block as valid. Prevoting nil either

means that the validator marked the block as invalid, or failed to respond within time

– possibly due to network timeout, power outage, or some other arbitrary reason. If

we receive more than ⅔ prevotes, that's called a polka – hence the image of the

two people dancing in the bottom right corner.

Then, in the next step

is "precommit." If a validator witnesses a ⅔ prevote for the block from the network

and prevoted for the block, then it precommits the block. Otherwise if the validator prevoted

nil previously because the block was invalid, or they just simply didn't prevote in time,

then the validator precommits nil.

If ⅔ validators precommit the block, then the block is committed. Else, a new voting

round starts.

We start off in the New Height phase. In this step, we select a validator to propose the

next block. Tendermint does this with a round-robin selection algorithm that selects proposers

in proportion to their voting power – which in the case of Proof-of-Stake is however much

every validator decided to stake.

Let's say Rustie gets chosen as the proposer. So in the "Propose" phase, he proposes

the next block. That's the yellow block on top of his name.

The next phase is to prevote. Since we have 5 validators, we need at least 4 of us to

prevote the block that he proposed in order to reach the greater than ⅔ threshold.

So we wait for a bit of time for all the prevotes to come in. Remember that Tendermint is partially

synchronous, so we stop waiting after the timeout time has passed, and just take whatever

prevotes we have so far, and assume the rest are prevote nil.

Let's say everyone prevotes for the block before time's up, which means

that we have 5/5 prevotes for the block, and that's more than ⅔.

And yay that's a polka!

Alright, so now we're on the next phase, which is precommit. Everyone who originally

voted prevote block in the last phase should be voting precommit block this time around.

So let's see what happens.

Uh-oh! Looks like there was a network partition, and both Nadir, myself, and Nick are separated

from the rest of the network. Maybe my computer crashed and Nick's neighborhood had a blackout.

So everyone precommits, and Derrick, Gloria, and Rustie all precommit the block. Meanwhile,

since no one's heard from neither Nick nor myself yet, and it's past the timeout time,

we count those votes as precommit nil.

So this time, we have ⅗ precommits for the block, which isn't more than ⅔ of the

total number of validators.

Alright, so since we didn't get more than ⅔ precommits for the block, we can't commit

the block Rustie proposed, so we have to go to a new round.

As mentioned earlier, Tendermint's proposer selection algorithm is round-robin, so it

can't choose me to propose again. The algorithm is also proportional based on the amount of

voting power we have, so let's say Derrick staked the second highest amount after Rustie,

so he's the next to be able to propose a block.

So that's the yellow block on top of his name here.

And let's say that I managed to reboot my crashed computer, and I'm back online as

a validator. This time around, if everyone prevotes and precommits the block, all within

time, and no one crashes or fails in any other way, then we have ⅘, which is more than

⅔, so we could potentially commit this next block.

And if Nick continues to be offline, his stake could be slashed.

Casper is a very intentional pun for being the spiritual successor of GHOST. To preface,

the GHOST protocol is Ethereum's solution to its fast block times. Fast block times,

when compared to block propagation rates and latency, mean that there will be more naturally

occurring forks and such. Valid blocks that are verified by part of the network might

end up being cast off because it lost the propagation race to another equally valid

block.

These castaway blocks are called uncle blocks, and while they might not in the end be included

in the longest chain, they do represent the fact that there was some degree of computation

done on them. So, the GHOST protocol includes these uncle blocks into the calculation of

which chain has had the most cumulative work done on it: serving as a fork resolving policy

for miners.

Casper is a planned upgrade to Ethereum that's an adaptation of some of the principles of

the GHOST protocol for Proof-of-Work into Proof-of-Stake.

This upgrade from Proof-of-Work to Proof-of-Stake has been long in planning, pretty much ever

since the Ethereum yellow paper was written in 2014, and evidence for which has been publicly

viewable in the form of the Ethereum ice age and difficulty bomb – both of which are

artificial limitations on the Ethereum software that have been purposely placed in the protocol

so as to force a hard fork years later.

The first steps to upgrade Ethereum from Proof-of-Work to Proof-of-Stake will likely be first a Proof-of-Stake

overlay on top of the existing Proof-of-Work protocol. We'll talk about this in the next

slide, but Casper the Friendly Finality Gadget, an implementation of Casper in the form of

an Ethereum smart contract, is set for launch with a 2 year plan and 1.2 million ether in

funding.

There are two big implementations of Casper, each taking a different approach. Casper the

Friendly Finality Gadget, or Casper FFG, is a Proof-of-Stake overlap on top of Proof-of-Work.

It's a chain and BFT-based Proof-of-Stake hybrid, and is taking a step-wise approach

to upgrading Ethereum, with the ultimate goal of eventually casting away Ethereum's Proof-of-Work

scaffolding entirely.

On the other hand, Casper the Friendly GHOST is a member of a family of consensus protocols

derived from a Correct by Construction, or CBC, methodology. Sometimes you'll hear

Casper the Friendly GHOST being referred to as CBC Casper. CBC means that this implementation

of Casper was designed from scratch to formally satisfy a certain safety proof, so it's

correct by construction. It's a pure Proof-of-Stake protocol, not a hybrid like Casper FFG, and

when in implementation would require a much more drastic overhaul.

As we mentioned earlier, Casper FFG is a Proof-of-Work and Proof-of-Stake hybrid. It's known colloquially

as "Vitalik's Casper" since he's the main advocate of this form of Casper.

Casper FFG is a Proof-of-Stake overlay on top of Proof-of-Work that – as its name

implies – is a tool that aims to fix finality in Ethereum. In Bitcoin, Ethereum, and many

other Proof-of-Work blockchains, we rely on the probabilistic finality of blocks. For

example, in Bitcoin, we assume that blocks are finalized with reasonable probability

if that block has more than six confirmations on top of it. Though it's unlikely for the

block to be forked by a longer chain, the possibility will always exist. Hence, the

longest chain is never truly final in Bitcoin. FFG attempts to take away that probabilistic

finality from Proof-of-Work systems.

In FFG, we carry on with Proof-of-Work as normal. However, every 50 blocks – called

an "epoch" – we run a round of Proof-of-Stake, which consists of prepare and commit phases,

where validators vote on which chain they believe to be canonical.

Each round of Proof-of-Stake at an epoch is called a checkpoint, and checkpoints are first

justified, and then if another checkpoint is built on top of it, then that previous

checkpoint is considered finalized. If a block b is finalized, then the blockchain cannot

reasonably revert back to a time in history before block b.

This is also advantageous because we can now consider only the justified chain rather than

the entire Ethereum blockchain.

Casper FFG also implements deposits and slashing conditions; we'll explain these in next

week's lecture about Proof-of-Stake and cryptoeconomics.

On the other hand, Casper the Friendly Ghost is colloquially known as Vlad's Casper,

named after Vlad Zamfir, cryptography researcher.

As we mentioned before, it is designed from the ground up with a correct-by-construction,

or CBC, methodology, so it would be a more drastic overhaul to the Ethereum network,

aiming to redefine the fundamentals of the protocol.

CBC Casper is a family of protocols that allow consensus on different types of data, but

all satisfy the same safety proof. Currently, there are six consensus protocols that exist

in the CBC family, which serve purposes ranging from binary and ordinal consensus to list

and concurrent schedule consensus and blockchain consensus.

Instead of aiming for probabilistic finality, as with Bitcoin, CBC Casper aims for absolute

finality. Most Nakamoto Consensus networks, because of the fork resolution policies they

adopt, allow for extremely historic chains to be overridden by even longer forks; a miner

in Iceland secretly mining an incredibly long blockchain which took more computational power

to produce could invalidate the entire Bitcoin blockchain any day. This is because Nakamoto

Consensus assumes that the probability of such an event happening is incredibly low.

However, it's never completely impossible.

CBC Casper, on the other hand, achieves eventual finality. This means that, after some point

in time, a block will eventually be finalized and immutable. Though the time to finality

could be unbounded, it is still a finite amount of time. This is the subject of much present-day

research, and it may change even soon within the future. To best understand cutting-edge

developments such as these, it's best to explore by yourself for the best understanding.

It's difficult to get into details with so much theory to understand, but recognizing

the gist of the difference between probabilistic finality and absolute finality and the differences

between how the two types of Casper achieve finality are the most important takeaways

for this particular section.

For more infomation >> [CS198.1x Week 1] Proof Stake Implementations - Duration: 12:36.

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[CS198.1x Week 1] Nakamoto Consensus - Duration: 9:28.

In general, Nakamoto consensus delivers a few key properties.

First off, this paradigm serves as a way to come to consensus with unknown, untrusted

peers.

In Bitcoin, the first distributed system to use Nakamoto Consensus (hence the name "Nakamoto

Consensus"), anyone can join or leave the network at any time and even send corrupted

messages or false messages to others.

Additionally, any user can have as many virtual identities, or public/private key pairs, as

they want.

To prevent unfair voting from anyone who dishonestly creates multiple identities, voting power

must be made scarce, done by tying voting power to a scarce resource.

Bitcoin happened to use computational power as the scarce resource, but that is just one

of many possible resources to expend to achieve Nakamoto consensus.

Because of this implicit manner of voting, leaders are elected through a random lottery,

where the likelihood of winning this lottery increases with the expended resources, pay-to-play

style.

The winner of the lottery is then allowed to create the next update to the system.

In blockchain networks, the block represents that very update.

When the vote is cast, it's possible that the lottery chose a malicious leader.

In this case, others in the network vote implicitly on the proposed update by either including

the update in their own local view of the system state or by refusing to accept it.

For example, in Bitcoin, full nodes can choose which blocks they include in their blockchain:

but their chain will only be valid if the rest of the network is in consensus.

Hence, each Nakamoto Consensus protocol must have a set of rules defining how to choose

the most valid state of the network, such as the longest chain policy in Bitcoin, one

example of a fork resolution policy.

For this reason, some choose to refer to Nakamoto consensus as chain-based consensus.

We'll understand this better in a bit when we look at chain and BFT based Proof-of-Stake

implementations.

In the next couple slides, we'll be taking a look at various consensus mechanisms that

fall under the wide umbrella of Nakamoto Consensus, and for each, we'll be focusing on what

resource each consumes and how the network uses that resource to come to consensus.

First, just as a recap and to understand Nakamoto Consensus in something that we already know,

take Bitcoin.

In Bitcoin, miners solve partial preimage hash puzzles so as to prevent naive Sybil

attacks, and to tie voting power to a scarce physical resource.

The image on the right hand side is from our first course: Bitcoin and Cryptocurrencies,

and shows exactly that.

The miner who solves the block first gets to send it to the rest of the network.

The rest of the network then implicitly votes on that block, since they have the power to

choose to append it to their local copies of the blockchain.

In Proof-of-Work, the resource consumed is computational power.

The faster a miner can compute hashes, the more likely they're able to find the next

valid block and send it to the rest of the network.

The next example of Nakamoto consensus is Proof-of-Stake.

This one's a bit tricky since there are very many different implementations of Proof-of-Stake,

but in general some of them can be classified as Nakamoto consensus mechanisms as well.

Like the miners in Proof-of-Work, validators in Proof-of-Stake are tasked with creating

updates and supporting the network.

Instead of expending computational power, Proof-of-Stake instead uses its native currency.

As in: the more native currency a validator stakes, the more likely it's going to be

elected to propose or create the next update.

There's a lot to talk about when it comes to Proof-of-Stake, so we'll be going into

that in the next section, after the following comparisons with other Nakamoto consensus

mechanisms.

Proof-of-Activity is a hybrid consensus mechanism that borrows from both Proof-of-Work and Proof-of-Stake.

The general algorithm is as follows: First, miners solve and submit blocks to the system,

in the standard Proof-of-Work fashion.

Then, the system switches to Proof-of-Stake.

The block header contains data that represents a random group of validators who are required

to sign the new block.

And like in Proof-of-Stake, the more coins a validator has staked, the more likely they

are to be chosen to sign off of the block.

The block fees and rewards are split among the miner and the various validators.

Once the validators have signed the newly found block, the block is complete and users

are free to add it to their copies of the blockchain.

So in Proof-of-Activity, the resources consumed are both Proof-of-Work and Proof-of-Stake

resources.

Miners consume computational power, and validators consume the native currency.

Next up is Proof-of-Burn.

It's like Proof-of-Stake, but edgier.

This one's a bit tricky since the term Proof-of-Burn can be used to refer to a number of things.

For example, in Bitcoin, you can prove the existence of arbitrary data by sending a transaction

to an irretrievable address, thereby writing your data into the blockchain and paying the

transaction fees to no one, "burning" your coin.

That's not what we're talking about here.

Proof-of-Burn, in this context, refers to a Nakamoto consensus mechanism.

In Proof-of-Burn, the more coins you send to an irretrievable address, or the more coins

you "burn", the higher the likelihood is of you being elected and being able to

create the next block.

Proof-of-Burn is like Proof-of-Stake, except that once you stake your coin, you can't

get it back.

As a side note, burning coins can also be used as a bootstrapping mechanism for new

systems.

You could tie a new coin's value to some other existing coin.

For example, a new cryptocurrency could derive its value from the burning of Bitcoin.

In summary, the resource being consumed in Proof-of-Burn is currency -- and that could

be either native currency, or potentially some other non-native currency.

Another novel category of Nakamoto Consensus is Proof-of-Space.

Proof-of-Space consumes storage space as a limiting resource for voting power.

Proof-of-Space is seen by some to be more fair and energy efficient than other consensus

mechanisms, such as Proof-of-Work, due to the availability of storage, as well as its

lower energy cost.

Nowadays, Proof-of-Space is used primarily for file storage rather than as a implicit

voting method, though the implementations vary.

For example, there are also protocols that give rewards directly for storing information,

such as Filecoin, Storj, and Sia.

On the other hand, Proof-of-Capacity is a type of Proof-of-Space that is very similar

to the other Nakamoto Consensus mechanisms we've been talking about.

Instead of proving you've done work or staked some coin, you show that you've solved some

problem involving a nontrivial amount of storage capacity.

Then you can propose the next block or update to the system and receive coins for your computation.

In a sense, Proof-of-Capacity can be seen as a memory-hard Proof-of-Work protocol.

At their core though, all these systems use some sort of Proof-of-Space in order to limit

user's influence on the system.

There's also Proof-of-Elapsed-Time, which has users consume time.

The way they do this is by first picking a random duration of time to wait, and then

waiting for that duration of time.

In order to enable this sort of behavior, we need to trust the users are actually waiting

for however long they say they're waiting, and that it's truly random.

This is where Trusted Execution Environments come in, particularly Intel SGXs.

Trusted Execution Environments are separate environments from your computer's main memory

that can only be accessed with certain CPU instructions.

This prevents tampering by other external processes.

Think processes that govern fingerprint sensors on mobile devices, or alternative payment

options.

The way it works is that we simply ask users to wait a random time, and once that random

time is up, then they get to propose the next update.

The two assumptions to satisfy here are (1) that the time chosen to wait is actually random,

and (2) that we have actually waited that long.

And this is functionality that is built in to trusted execution environments.

Once code is loaded into the trusted execution environment, it generates an attestation,

or proof that the environment is working as intended, and that the right code is running

the right way.

Users can't interfere with the trusted execution environment since the memory space that it

runs on is private, and any sort of tampering would result in a faulty attestation.

However, this is all based on your trust in the manufacturer of the trusted execution

environments.

So if you're using Intel SGX, you have to trust Intel and their entire production process,

that they haven't accidentally – or maliciously – introduced a backdoor or vulnerability.

A mechanism that does not directly fall under the category of Nakamoto consensus, but that

is worth mentioning, is Proof-of-Authority.

Proof-of-Authority relies on trusted individuals signing off on updates to the network, and

is primarily used in permissioned or non-production networks.

For example, the Kovan and Rinkeby Ethereum test networks use Clique, a Proof-of-Authority

implementation.

Following some attacks on Ropsten, Proof-of-Authority was chosen as a way to better control vulnerable,

valuable, but not publicly controlled testnets.

The resource consumed here is Identity.

There is a question mark because this follows the paradigm of politically centralized systems,

with a few actors making decisions for the rest of the network.

For more infomation >> [CS198.1x Week 1] Nakamoto Consensus - Duration: 9:28.

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[CS198.1x Week 1] Distributed Systems Origins - Duration: 7:22.

Before we explain what a distributed system actually is, let's define a fundamental,

age-old problem that pervades not only computer science and blockchain technology, but all

of humanity.

How does a group make a decision?

Whether this be by majority opinion, general agreement, or force, how do we each consensus?

Consensus is trivial when we only have one actor.

I can always agree with myself on where to have lunch, but when I go out with my friends,

we have to all agree on where to go first, and the process by which we reach agreement

might be difficult.

In this example, my group of friends had to come to consensus on a common choice of action

– what to get for lunch – before we were able to move forward – to actually get lunch.

This is no different from how a distributed system works, and is where we start building

our intuition and context.

Consensus has been studied for ages in fields such as biophysics, ethics, and philosophy,

but the formal study of consensus in computer science didn't start until the 70s and 80s,

when people decided that it would be a good idea to put computers on airplanes.

The airline industry wanted computers to be able to assist in flying and monitoring aircraft

systems: this included monitoring altitude, speed, and fuel, as well as processes such

as fly-by-wire and autopilot later on.

This was a huge challenge because being at such a high altitude poses many threats to

normal execution of computer programs.

For one, it's a very adversarial environment.

Being so high up means that the atmosphere is thinner, increasing the chance of a bit

flip due to solar radiation.

And all it takes is a single bit flip to completely destroy the normal execution of, for example,

the sensor measuring the amount of fuel an aircraft has left.

Compounded on this was the fact that aircraft could cost hundreds of millions of US dollars,

and that commercial airliners where these computers were going to be deployed could

carry hundreds of passengers.

So, super dependable computer systems were first pioneered by aircraft manufacturers.

They realized that the problem they were solving could be solved by introducing redundancy

in their system.

Instead of using a single computer onboard their aircraft, thus having a single point

of failure, they used multiple computers onboard to distribute the points of failure.

How these computers coordinated amongst each other though was another challenge.

Early literature had focused on enabling coordination of processes – where these processes could

be processes on a CPU, or computers in a network, separated spatially.

One of the most impactful pieces of literature during this time was "Time, Clocks, and

the Ordering of Events in a Distributed System", written by computer scientist and mathematician

Leslie Lamport in the late 70s.

In "Time, Clocks, and the Ordering of Events in a Distributed System", Lamport shows

that two events occurring at separate physical times can be concurrent, so long as they don't

affect one another.

Much of the paper is spent defining causality – what it means for an event to happen before

another – in both the logical and physical sense.

This is important because determining the order of when events take place, such as the

measurement of a sensor or detection of error and subsequent error correction – as well

as determining which events actually took place in the first place – is crucial to

the correct functioning of a distributed system.

On the right, you can see one of Lamport's diagrams, depicting three processes – the

vertical lines – each with their own set of events – the points on these lines.

Time flows in the upwards direction, and each squiggly line between events represents a

message being sent, and received at a later time.

If there exists a path from one event to another, by only traveling upwards in the diagram,

then that means that one event happened before the other.

If an event doesn't happen either before or after another event, then it's said that

those events are concurrent.

For those of you with experience in quantum physics, you may notice the resemblance between

Lamport's diagrams, and Feynman diagrams, which show the interaction of subatomic particles.

Lamport realized that the notion of causality in distributed systems was analogous to that

in special relativity.

In both, there are no notions of a total ordering of events – events may appear to happen

at different times to different observers, in the case of relativity, or processes, in

the case of distributed systems.

While this is at a depth that is out of scope for this course, it's important to recognize

that through the efforts of Lamport and other scientists, the formal study of distributed

systems began to take shape.

And as it turns out, the same problem that was originally studied to coordinate computers

on commercial airliners is still studied today, for example on more high tech jet planes

And more recently, on various spacecraft, such as SpaceX's famous Falcon 9 or Dragon

spacecraft.

For example, spacecraft have to be tolerant of the violent vibrations when accelerating

through Earth's atmosphere, and when they do leave the atmosphere, they have to deal

with intense heat and cold depending on which side of Earth they're on, and also solar

radiation.

SpaceX Dragon specifically uses three flight computers, which perform calculations independently,

and reboot automatically if errors are found after cross checking.

Distributed systems and consensus is also studied in the context of big enterprise operations

too.

Distributed lock servers for example, ensure that no two processes can read or write to

the same piece of data at the same time – a problem called mutual exclusion – thereby

preventing potential corruption to important data.

And finally of course – the main focus of this course – the blockchain and distributed

ledger revolution.

Fundamentally, each of these problems we just went over, and more, reduce to the problem

of consensus.

In aircraft like rockets, jet planes, and commercial airlines, a number of redundant

onboard computers must come to consensus on sensory data for example – the position

of the aircraft, its location and altitude, as well as its fuel levels, etc..

In enterprise distributed lock servers, processes must come to consensus on who can write what

data at what time, as the coordination of this prevents data loss and corruption.

And finally, in blockchain, full nodes agree on some state of the system, depending on

implementation.

In Bitcoin, users agree on who owns what bitcoin.

In Ethereum users agree on the correct execution of transactions and the general state of the

Ethereum network.

Consensus attempts to create a reliable system from potentially unreliable parts – parts

like the computers in aircraft that are vulnerable to bit flips due to radiation, or power outage

in a data center…

Or in public blockchains, where the ever changing network topology, as well as the existence

of malicious entities trying to subvert the network for economic gain, for example, don't

align with the goals of the system as a whole.

Instead of trusting the execution of individual processes or reliability of any individuals,

we trust the general protocol and the math behind it.

It's trust – without trust.

For more infomation >> [CS198.1x Week 1] Distributed Systems Origins - Duration: 7:22.

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The Most Cringeworthy Live! Moments Ever - Duration: 5:01.

One of the longest-running talk shows on TV, Live! offers a light-hearted combo of current

events discussion, host banter, and celebrity interviews.

For millions, it's as much of a part of their morning routine as coffee and toast.

And while the show is, by design, an hour of fluff, sometimes the lightweight fare gets

dark and messy.

Here are some of the most cringeworthy Live! moments ever.

Kelly's awkward return

In 2016, ABC announced that then-Live! co-host Michael Strahan would be leaving the show,

and heading to Good Morning America.

But CNN reported Strahan allegedly first told co-host Kelly Ripa about it less than half

an hour before the news went public.

Ripa was so upset that she didn't show up for work.

"I'm leavin this show...I know.

To go to GMA full time"

But when Ripa returned to Live! the following week, it wasn't business as usual.

"Our long national nightmare is over"

Ripa delivered a long, serious speech about her frustration over how she found out her

co-host was leaving.

"I needed a couple of days to gather my thoughts.

After 26 years with this company, I earned the right"

Ripa added that "apologies [had] been made" and that, despite it all, she was genuinely

happy for Michael.

"I am thrilled for Michael.

I am thrilled for you.

This is a tremendous opportunity"

Strahan, who was on set the whole time, got an ear-full of the entire speech.

Way too close for comfort

Nicki Minaj has one of the most celebrated backsides in showbiz, but that doesn't mean

you can look with your hands.

In 2010, Minaj performed her hit "Right Thru Me" on Live! and afterward, Philbin went way

off-script.

"...You're wearing pink because in honor of pink Friday, I think you're so cute.

I love it."

"Well thank you very much.

This looks like you got a little strap around you there, you know what I mean?"

"Regis!"

Kelly blurted out what everyone was thinking:

"What just happened?"

But that wasn't the first time Philbin got sleazy on the show.

When singer Alicia Keys guest hosted Live! in 2004, Philbin asked for a kiss, and also

pronounced her name wrong.

"C'mon Alisha!"

"You won't go to jail will you?"

"We'll make em real jealous, we'll make em real jealous"

"Aahhhhhh"

Um, gross much?

"He's always wanted to do that."

"I love to kiss a poet."

"Now I have to go to the hospital, to check everything out."

Kelly defends her co-host

Now co-host of Live!, Ryan Seacrest also remains a fixture on E!

But in November 2017, a lawyer representing stylist Suzie Hardy sent a letter to E!'s

parent company accusing Seacrest of abuse and harassment during the six years she worked

with the host.

An anonymous insider corroborated Hardy's claims, telling Today,

"She would go to tie his shoe and Ryan would shove her head toward his crotch.

I saw that more than once."

Seacrest was ultimately cleared of any wrongdoing, due to a lack of sufficient evidence in an

internal investigation.

And on a 2018 episode of Live!, Kelly Ripa defended her co-host.

"I just want you to know, you are a privilege to work with and I adore you"

She continued,

"I know what an easy, professional, great person you are and I feel very, very lucky

to work with you each and every day."

In light of everything, awkward.

A bun in the oven

After Kathie left Live! in 2000, the show invited All My Children star Kelly Ripa to

come audition for her spot.

Ripa's Live! tryout began on November 1st, 2000, with guest psychic Char Margolis, who

got a very good read on Ripa.

First, she acknowledged Ripa's late grandmother, then she dropped a bombshell:

"She's going to watch over you when this new baby comes…It's soon, it's not far away."

"Excuse me uh, are you expecting?"

After gathering herself, Ripa blurted out,

"I haven't told my boss yet!"

Fake news about Meryl Streep

In 2009, Oscar-winning actress Meryl Streep was a guest on the show to promote her movie,

Julie and Julia.

And her visit was more than a little awkward.

First, Philbin incorrectly asked Streep about her "four daughters."

"So many exciting things happening in your house.

You had all those...four daughters?

I have three daughters and my son"

Then, he suddenly declared,

"And then the third daughter broke her leg!

I mean it never ends in your place!"

"What?"

"No."

It turned out Philbin had read the cue card wrong.

"Daughter Grace just got her big break.

Well, I was close!

What do you want!"

Regis gets into punk

If you watch Live! all the way to the end of the episode, you might just catch a musical

performance.

But probably the most unlikely band to ever guest on the show was the Ramones, the New

York punk icons best known for hard-charging, noisy hits.

Yet in 1988, the band sat down for an interview on the show.

"So here are the Ramones"

Hosts Regis Philbin and Kathie Lee Gifford were game, and the Ramones were polite, but

it was still a trip to watch two worlds collide.

"So Dee Dee you're happy aren't you?

Yes I am.

Oh that's good.

He's jolly.

He's a jolly kinda guy"

For more infomation >> The Most Cringeworthy Live! Moments Ever - Duration: 5:01.

-------------------------------------------

OMG 3Q | Chân Lục Tốn - Cấn lắm cao thủ cải tạo được vị tướng này | Moon Su - Duration: 42:33.

For more infomation >> OMG 3Q | Chân Lục Tốn - Cấn lắm cao thủ cải tạo được vị tướng này | Moon Su - Duration: 42:33.

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A CONVENÇÃO DA ONU OBRIGA A LEGENDA PARA SURDOS? - Duration: 2:07.

[Marcelo in Portuguese] Hi everybody!

My name is Marcelo!

I am part of this campaign

"Subtitles for those who don't hear, but get emotion!"

So let's talk about the Convention on the Rights

of Persons with Disabilities,

created by the UN - United Nations.

So, can you start, Raíssa?

[Raíssa in Spanish] Hi! My name is Raíssa

and I am also part of it with the Spanish version.

The Convention require

the use of the subtitle, of Sign Language,

Audiodescription and Induction loop

in accordance with Article 30.

[Olívia in Spanish] Anything else, João?

[João in English] Hi, my name is João

and I support the campaign too!

My message is in English as you can see, Raíssa.

There is also Article 21 dealing

with freedom of expression and opinion

and access to information.

It's up to you, Andrea!

[Andrea in German] Hi,

my name is Andrea and it's in German!

So we have the right to publicize

our campaign shirt

to show it, including 6 languages,

besides the international sign language

which is recognized by the UN too!

How can we get the shirt, Uaiana?

[Uaiana in French] Hello, my name is Uaiana

and I wear the shirt in French!

Here it is, Andrea,

But how can you get the shirt in your language?

Let's download the shirt art at:

www.legendanacional.com.br

and make it!

[Natália in Italian] Hi, my name is Natália,

and we have the Italian version too.

Have you noticed?

Each one spoke in their language, but the subtitles include,

join and form a union,

so let's get united

to express a greater struggle?

Thank you all.

For more infomation >> A CONVENÇÃO DA ONU OBRIGA A LEGENDA PARA SURDOS? - Duration: 2:07.

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ASMR Unintelligible & Inaudible Whisper Ear to Ear Mouth Sounds ACMP - Duration: 10:20.

Hello and welcome back to another video from Dark ASMR

Today I'm going to be doing some inaudible whispers for you all

Let me know if you enjoy them

Leave a comment. Like the video. Subscribe to the channel

Let's get started

That's all for today

I hope your enjoyed my inaudible whispers

If you liked the video then please click the button below

Click subscribe and share the video

Leave a comment. Let me know what you want me to do next

Have fun. See you next time

Dark ASMR

For more infomation >> ASMR Unintelligible & Inaudible Whisper Ear to Ear Mouth Sounds ACMP - Duration: 10:20.

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[CS198.1x Week 1] Byzantine Fault Tolerance - Duration: 7:09.

We've defined distributed systems, understood the definition of correctness in their context,

and understood the various properties and limitations of these systems. We're now

going to finish off by understanding all the possible areas in which these systems can

fail, best summarized by the Byzantine Generals' Problem.

The Byzantine Generals' Problem is a question of consensus. Allow me to tell you the story

of the Byzantine generals, laying siege to an enemy city.

They've all surrounded the city, but there's a problem: large physical barriers, like mountains

and canyons, separate the generals. They can communicate only by sending messengers. In

addition, they had not decided prior whether to attack the city, or to give up and retreat.

Only after observing the enemy do they then decided on a common plan of action.

Only an attack launched by all generals at once can successfully conquer the city. Having

come this far, they send messages with their votes on how to proceed.

"Okay," you may ask, "if they can send messages to each other, then what's the

issue? They just need to collect enough information, right? So what's the problem?"

Well, the problem is that some generals may have been bribed. In exchange for their betrayal,

they'll earn themselves a small fortune from the city. These traitor generals will

send incorrect information to their peers in order to screw up the attack, preventing

consensus. In addition, their messengers might get lost or corrupt their messages.

The question which remains: how do we achieve consensus, if we can at all? Spoiler alert:

there is no solution in the presence of ⅓ or greater percentage potential traitor generals.

We call these potential traitor generals Byzantine nodes, which may act maliciously or arbitrarily.

In this image, there are two separate triangles. We consider scenarios with 3 generals and

1 traitor, since the case is trivial with just 1 or 2 generals.

Let's say the first general to send out a message is the commanding general issuing

commands, and the listening generals are the lieutenant generals.

The node on top of the triangle is the "Commander," and the bottom two nodes are labeled "Lieutenant

1" on the left and "Lieutenant 2" on the right. The commander casts a vote for

either "attack" or "retreat" and the lieutenants record the vote.

As you can see in the first triangle, Lieutenant 2 is shaded. This implies, according to our

story, it's a traitor bribed by the enemy. In distributed systems terms, it's a Byzantine

node. Either way, it's going to send incorrect information to its peers. Let's say the

commander votes to "attack," sending the message to both Lieutenant 1 and Lieutenant

2. Lieutenant 2, to mess things up, tells Lieutenant 1 that the commander said "retreat."

Lieutenant 1 now sees two different facts, only one of which can be true: the commander

said attack, or the commander said retreat.

In the second triangle, the commander is shaded instead. It tells Lieutenant 1 to "attack,"

but sends a conflicting message to Lieutenant 2, telling it to "retreat." Lieutenant

2 dutifully reports to Lieutenant 1 that the commander said to "retreat," but Lieutenant

1's problem remains. Notice that, for Lieutenant 1, the information he's receiving is exactly

the same as the previous scenario even though the Byzantine node changed. The Commander

is telling it to "attack," but the other Lieutenant claims otherwise. Because these

two situations are indistinguishable,

the Lieutenant cannot detect which node is malicious. In addition, because there is no

clear majority value, Lieutenant 1 cannot decide on a value. With just one Byzantine

node out of three, it's impossible to achieve consensus.

This example with three nodes extends to any general set of nodes attempting to come to

consensus, meaning that consensus with the presence of ⅓ or more Byzantine generals

is impossible. If there were a solution that could tolerate more than ⅓ Byzantine nodes,

we could use it to solve this much more simple scenario with 3 nodes. Since we know 3 nodes

cannot come to consensus with one Byzantine node, we can extend this to a general fraction.

However, that doesn't mean it's not possible to make an algorithm for the remaining situations.

Practical Byzantine Fault Tolerance released in 1999 by Miguel Castro and Barbara Liskov

gives an algorithm to allow nodes to come to consensus with no more than ⅓ Byzantine

nodes. This paper has inspired many iterations of Byzantine Fault Tolerant algorithms, especially

as research into blockchain consensus algorithms continues.

Let's formalize the various types of faults within distributed systems. There are two

main types of faults that are possible. While we can get more detail between the types of

faults, these two are the most fundamental to understand.

The first type of fault is a fail-stop fault. During a fail-stop fault, the node can crash

or not return values. Recall that a node's functionality includes sending, receiving,

storing, and processing information. Early research into distributed consensus first

aimed to solve these kinds of problems. This kind of failure may be temporary or indefinite,

but it will always be easier to handle than the second type of fault.

A Byzantine fault, referring to the Byzantine Generals' Problem, is a fault that refers

to any arbitrary deviance from the protocol. In other words, not only might nodes stop

replying or receiving information, but they may also send corrupted and/or false information.

You can note that Byzantine faults are a superset of fail-stop faults.

The behavior of most attackers, such as the bribed generals trying to hinder consensus,

falls under this kind of fault. It is this type of fault that all public blockchains

must protect against, since the participants in the blockchain network are unknown and

unpredictable.

Of course, the question after learning all this distributed systems material is, "How

does this relate to blockchain fundamentals?"

Keep in mind the nature of blockchain. We've described it as many things: a technological

solution to a social problem, the world's worst database, and a replacement for trusted

third parties. But what is it in the context of distributed systems?

Well, a blockchain is simply a data structure governed by a distributed system with an unbounded

number of participants in the consensus process and an unknown number of Byzantine faults

possible. In other words, it's the most adversarial environment possible. In spite

of the incredibly low amount of restrictions possible, blockchain makes it possible for

everyone to come to consensus.

Clearly, the distribution of computational power and data storage represents the geographic

distance between the generals. The nodes represent generals, and the traitors represent faulty

or malicious nodes. The possible dropped packets or messages between nodes represent unreliable

messengers, and coming to consensus on whether to include a block or the valid chain of transaction

history is the same as coming to agreement on whether to attack or retreat. It's the

same concepts with different names.

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