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<v Speaker 1>You know, there was a time, not even the long ago,

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<v Speaker 1>in the grand scheme of things, when writing assembly language

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<v Speaker 1>by hand was considered this ultimate badge of honor for

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<v Speaker 1>a programmer.

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<v Speaker 2>Oh yeah, absolutely. The real hardcore engineers, right.

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<v Speaker 1>And software engineers genuinely believed that a piece of software,

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<v Speaker 1>you know, automatically generating machine code could never ever beat

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<v Speaker 1>a skilled human who is just manually optimizing those memory

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<v Speaker 1>registers and CPU cycles.

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<v Speaker 2>Which makes sense for the time, I mean, they were

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<v Speaker 2>working closer to the metal exactly.

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<v Speaker 1>But fast forward to today, and modern compilers routinely generate

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<v Speaker 1>highly optimized machine code that just completely blows handwritten assembly

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<v Speaker 1>out of the water. Like we reached a point where

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<v Speaker 1>the machine became vastly better at writing machine code than

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<v Speaker 1>we are.

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<v Speaker 2>The paradigm just shifted completely. I mean, efficiency used to

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<v Speaker 2>be the primary argument against using high level languages early on.

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<v Speaker 2>The fear was really that adding all these layers of

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<v Speaker 2>abstraction between the programmer and the hardware would just result

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<v Speaker 2>in bloated, sluggish execution.

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<v Speaker 1>Right because you're adding middlemen exactly.

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<v Speaker 2>But compiler technology along with it, just the sheer complexity

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<v Speaker 2>of modern process or architectures advanced so aggressively. Humans can

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<v Speaker 2>no longer keep the entire dependency graph and the cash

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<v Speaker 2>locality and all the pipelining optimizations in their heads all

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<v Speaker 2>at once. But you know, the compiler can.

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<v Speaker 1>Welcome today's deep dive, we are unpacking the incredibly complex,

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<v Speaker 1>highly mathematical translation pipeline that makes all of modern software possible.

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<v Speaker 1>We're talking about the black box of compilers.

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<v Speaker 2>It's a fascinating topic, it really is.

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<v Speaker 1>And we're drawing from the concepts laid out in a

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<v Speaker 1>really great textbook. It's called a Practical Approach to Compiler

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<v Speaker 1>Construction by Des Watson, and the mission here for you

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<v Speaker 1>listening is pretty straightforward. Understanding this hidden translation process is

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<v Speaker 1>basically a shortcut to becoming a better problem solver and

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<v Speaker 1>just a much more informed tech user overall.

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<v Speaker 2>Yeah, because it strips away the.

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<v Speaker 1>Mystery exactly, Because you know, when you understand how your

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<v Speaker 1>human read code is structurally torn apart and then synthesized

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<v Speaker 1>into native hardware instructions, you stop treating your tools like magic.

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<v Speaker 1>You literally type words on a screen and somehow a

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<v Speaker 1>processor routes electrons to execute your commands. Okay, so let's

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<v Speaker 1>unpack this.

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<v Speaker 2>Well, the compiler is essentially the fundamental bridge between abstract

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<v Speaker 2>human thought and physical hardware execution. And to understand why

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<v Speaker 2>that bridge is even necessary, we kind of have to

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<v Speaker 2>look at the limitations of the nineteen forties, the.

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<v Speaker 1>Dark ages of coding very much so.

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<v Speaker 2>Programming back then literally meant writing raw machine code. It

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<v Speaker 2>was an incredibly slow, highly error prone.

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<v Speaker 1>Process because you were basically speaking binary right.

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<v Speaker 2>Essentially, Yeah, you were forced to think exactly like the

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<v Speaker 2>specific hardware architecture you were operating on. You were tracking

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<v Speaker 2>individual memory locations completely manually.

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<v Speaker 1>And then assembly languages came along and provided a slight buffer,

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<v Speaker 1>you know, by introducing symbolic names instead of just raw numbers,

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<v Speaker 1>but you were still fundamentally tied to whatever the hardware's

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<v Speaker 1>architecture was.

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<v Speaker 2>Right, if you wrote it for one machine, it lived

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<v Speaker 2>on that machine. The real seismic shift happened in the

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<v Speaker 2>nineteen fifties with the birth of high level languages.

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<v Speaker 1>Like Fortran and Cobol.

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<v Speaker 2>Right exactly, Suddenly you had Fortran, which was designed specifically

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<v Speaker 2>for complex numerical mathematics, and then kobol, which was engineered

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<v Speaker 2>for business data processing.

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<v Speaker 1>So it became about the domain, not the hardware.

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<v Speaker 2>Precisely, those high level languages introduced just a massive leap

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<v Speaker 2>in abstraction. You were no longer micromanaging the hardware's exact state.

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<v Speaker 2>You could focus entirely on the logic of the problem

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<v Speaker 2>you were actually trying to solve.

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<v Speaker 1>It kind of reminds me of ordering food at a restaurant.

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<v Speaker 2>Okay, I like where this is going.

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<v Speaker 1>Well, think about it. Using a high level language is

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<v Speaker 1>like just sitting down and asking the waiter for a burger.

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<v Speaker 1>That's the abstraction, right. You don't march into the kitchen

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<v Speaker 1>and tell the chef exactly what temperature the grill needs

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<v Speaker 1>to be and exactly what angle to slice the tomatoes at.

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<v Speaker 1>That would be writing machine code. You just want the burger.

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<v Speaker 2>That's actually a perfect analogy, and because you aren't in

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<v Speaker 2>the kitchen, you get vastly better readability, drastically easier debugging,

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<v Speaker 2>and crucially you get portability. You can take that same burger,

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<v Speaker 2>order your source code, and run it in different kitchens,

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<v Speaker 2>provided you have a translator or a compiler for that

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<v Speaker 2>specific kitchen.

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<v Speaker 1>But the downside originally anyway, was that perceived loss of control,

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<v Speaker 1>like you couldn't easily go in and check a specific

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<v Speaker 1>device status location or manipulate a raw memory address with

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<v Speaker 1>the same freedom if you needed to.

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<v Speaker 2>Yeah, that was the big fear. But as we established earlier,

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<v Speaker 2>compiler optimization essentially rendered that concern totally obsolete for the

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<v Speaker 2>vast majority of use cases. There is actually this golden

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<v Speaker 2>rule mentioned in Watson's book, which is there is no

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<v Speaker 2>need to optimize if the code is already fast enough.

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<v Speaker 1>That makes total sense. I mean, developer time became significantly

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<v Speaker 1>more expensive than CPU time exactly.

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<v Speaker 2>The abstraction of a high level language optimizes for human

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<v Speaker 2>problem solving speed if the resulting machine code meets the

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<v Speaker 2>performance requirements of the app. Manually shaving off a few

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<v Speaker 2>microseconds is just an irrational allocation of your engineering resources.

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<v Speaker 1>So okay, if we are ordering the burger in this

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<v Speaker 1>human readable format, how does the kitchen actually know what

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<v Speaker 1>to do? The compiler's job is to translate that into

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<v Speaker 1>specific hardware instructions. But it doesn't do it all at once, right, No.

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<v Speaker 2>Not at all. Doesn't execute that translation in one massive,

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<v Speaker 2>monolithic step. To manage the immense complexity of parsing all

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<v Speaker 2>that syntax and generating native instructions, traditional compilers are structurally

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<v Speaker 2>split into two very distinct phases, the front end and

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<v Speaker 2>the back end. Exactly. The front end is entirely focused

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<v Speaker 2>on analysis. It basically reads the source code you wrote,

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<v Speaker 2>validates its structure, and translates it into what's called an

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<v Speaker 2>intermediate representation or IR.

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<v Speaker 1>So it's not even compiling to machine code yet.

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<v Speaker 2>Yeah, not even close. The IR is a completely machine

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<v Speaker 2>independent format. The front end just analyzes what the code

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<v Speaker 2>logically means, entirely decoupled from the specific processor it's eventually

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<v Speaker 2>going to run on.

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<v Speaker 1>Okay, so once that intermedia representation is generated, then the

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<v Speaker 1>back end takes over, right.

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<v Speaker 2>The back end handles the synthesis. It takes that generic

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<v Speaker 2>IR and synthesizes the highly optimized architecture specific machine code.

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<v Speaker 1>Whether that's like an I eighty six Intel processor on

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<v Speaker 1>a desktop, or an ARM chip and a smartphone.

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<v Speaker 2>Or a specialized embedded system in a car. And this

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<v Speaker 2>separation is a brilliant architectural decision because it solves the

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<v Speaker 2>scaling problem.

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<v Speaker 1>Oh right, the multiple languages problem, because if you have

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<v Speaker 1>say ten programming languages and ten different processor architectures, a

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<v Speaker 1>monolithic design would require writing one hundred entirely separate.

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<v Speaker 2>Compilers exactly ten times ten, but with a separated front

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<v Speaker 2>end and back end, you only write ten front ends

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<v Speaker 2>to translate the ten languages into that shared IR, and

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<v Speaker 2>then you write ten back ends to translate the IR

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<v Speaker 2>into the ten hardware architectures.

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<v Speaker 1>So you reduce the workload from ten times ten to

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<v Speaker 1>ten plus ten one hundred down to twenty. That is

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<v Speaker 1>incredibly efficient.

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<v Speaker 2>It's massive. If a manufactured or releases a brand new

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<v Speaker 2>silicon architecture tomorrow, they only have to write a single

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<v Speaker 2>new back end and they instantly get support for ccplus

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<v Speaker 2>plus Java and any other language that already has a

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<v Speaker 2>front end producing that standard IR.

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<v Speaker 1>That is so smart. But wait, we should also touch

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<v Speaker 1>on the alternative route here, right, because not everything precompiles

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<v Speaker 1>directly to machine code like that. What about interpreters and

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<v Speaker 1>virtual machines like the Java virtual machine, Right.

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<v Speaker 2>That's a very important distinction. Instead of a back end

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<v Speaker 2>synthesizing native hardware machine code ahead of time, a compiler

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<v Speaker 2>can output a sort of virtual.

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<v Speaker 1>Machine code bycode, basically.

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<v Speaker 2>Exactly bycode the Java compiler stops at bytcode. Then a

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<v Speaker 2>totally separate piece of software, which is the interpreter, runs

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<v Speaker 2>on the host machine and translates that bycode into native

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<v Speaker 2>instructions on the fly as the program is actually executing.

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<v Speaker 1>But wait, if interpretation is inherently slower because it has

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<v Speaker 1>to read and translate the code on the fly at runtime,

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<v Speaker 1>why do so many modern languages use virtual machines, why

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<v Speaker 1>take that performance hit?

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<v Speaker 2>Well, if we connect this to the bigger picture, that

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<v Speaker 2>slight hit in runtime efficiency is a deliberate trade off,

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<v Speaker 2>and it's for two major advantages. The first is ultimate portability.

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<v Speaker 2>You compile the code once and it will execute on

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<v Speaker 2>literally any device running the virtual machine, regardless of the

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<v Speaker 2>underlying hardware. Run anywhere, exactly, run anywhere. And the second advantage,

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<v Speaker 2>which is often more critical today, is safety. The virtual

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<v Speaker 2>machine acts as a highly controlled sandbox, oh.

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<v Speaker 1>Because it's interpreting the code dynamically, so it can catch

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<v Speaker 1>things precisely.

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<v Speaker 2>It can enforce runtime constraints that a raw native binary

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<v Speaker 2>just can't. If a pre compiled native executable tries to

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<v Speaker 2>access a restricted memory block, the hardware might just attempt

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<v Speaker 2>the operation and cause a catastrophic.

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<v Speaker 1>System faull a total crash.

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<v Speaker 2>Right, But the interpreter monitors the virtual machine code as

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<v Speaker 2>it runs, it performs bounds checking and validates operations before

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<v Speaker 2>they ever physically touch the hardware, so.

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<v Speaker 1>It creates a protective buffer against like militious instructions are

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<v Speaker 1>really bad memory leaks.

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<v Speaker 2>Yeah exactly.

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<v Speaker 1>Okay, that makes sense, But let's rewind back to the

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<v Speaker 1>main compilation pipeline for a second, because before the front

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<v Speaker 1>end can even generate that intermediate representation we talked about,

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<v Speaker 1>it has to parse the raw text you typed. Like

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<v Speaker 1>computers are notoriously terrible at guessing what humans mean.

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<v Speaker 2>They have zero intuition, absolutely zero, which is why compiling

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<v Speaker 2>requires an incredibly strict formalization of the language. Compilers operate

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<v Speaker 2>on absolute mathematical rigidity.

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<v Speaker 1>So a programming language has to strictly define its syntax,

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<v Speaker 1>which is the structure, and its semantics, which is the

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<v Speaker 1>actual meaning of those structures, right.

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<v Speaker 2>And to define the syntax, language designers use what are

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<v Speaker 2>called metal languages. The most common one is bacis nar

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<v Speaker 2>form or BNF and its extended variants.

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<v Speaker 1>And this is where it gets really interesting, because these

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<v Speaker 1>metal languages mapp directly to the linguistic frameworks established by

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<v Speaker 1>Noam Chomsky back in the nineteen fifties.

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<v Speaker 2>Yes, no M Chomsky. He classified grammars into a strict hierarchy,

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<v Speaker 2>and for the core structural analysis of a programming language,

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<v Speaker 2>compilers rely heavily on Chomsky Type two.

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<v Speaker 1>Grammars, which are known as context free grammar.

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<v Speaker 2>Exactly, context free grammars and the implementation of a context

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<v Speaker 2>free grammar is what allows the compiler to resolve structural

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<v Speaker 2>hierarchy natively.

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<v Speaker 1>I love this part. Let's look at the example from

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<v Speaker 1>the source material. Consider a really simple mathematical expression in code,

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<v Speaker 1>like one plus two times three.

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<v Speaker 2>Okay, one plus two times three. Right.

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<v Speaker 1>The compiler doesn't have some external, you know, math module.

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<v Speaker 1>It consults to remember the order of operations. It's not

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<v Speaker 1>looking up a textbook. The precedence of multiplication over edition

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<v Speaker 1>is literally structurally hard coded into the grammar rules of

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<v Speaker 1>the language itself.

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<v Speaker 2>It's baked right in. Yeah.

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<v Speaker 1>It's like how English grammar tells us that adjectives generally

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<v Speaker 1>come before nouns. The structure dictates the meaning.

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<v Speaker 2>Exactly when the parser reads that expression. It builds a

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<v Speaker 2>literal data structure in memory call a parse tree. Because

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<v Speaker 2>the BNF rules for multiplication are defined at a deeper

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<v Speaker 2>nesting level than the rules for addition, The parser inherently

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<v Speaker 2>groups the two times three into a distinct child branch

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<v Speaker 2>of the tree, and then.

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<v Speaker 1>The addition operation sits at the parent node, basically waiting

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<v Speaker 1>for that child branch to finish its math.

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<v Speaker 2>Right. The parser cannot evaluate the addition until the multiplication

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<v Speaker 2>node resolves. It physically enforces the order of operations through

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<v Speaker 2>the design of the data structure.

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<v Speaker 1>That is so cool, So how does it build that tree?

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<v Speaker 2>Well, parsers construct this tree using one of two primary strategies,

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<v Speaker 2>top down or bottom up. A top down parser starts

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<v Speaker 2>at the very root of the tree, which is the

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<v Speaker 2>axiom of a valid program, and it attempts to expand

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<v Speaker 2>the grammar rules downward.

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<v Speaker 1>Substituting variables until it perfectly matches the actual sequence of

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<v Speaker 1>characters in the source code exactly.

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<v Speaker 2>And then a bottom up parser approaches it from the

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<v Speaker 2>exact opposite direction. It reads the raw sequence in the code,

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<v Speaker 2>groups small segments into basic grammar rules, and collapses them upward.

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<v Speaker 1>So it basically shifts tokens onto a stack and reduces

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<v Speaker 1>them into larger and larger grammatical components.

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<v Speaker 2>Right until the entire stack collapses into that single root

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<v Speaker 2>node of a valid program, and if it fails to

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<v Speaker 2>reach that route, well, it throws the syntax er.

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<v Speaker 1>But wait, how does the parser even know that one, two,

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<v Speaker 1>three is in number and the plus sign is an

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<v Speaker 1>operator because it doesn't read the source code character by character.

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<v Speaker 2>Err right, it definitely doesn't. If a context free grammar

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<v Speaker 2>parser had to evaluate every individual space and letter and

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<v Speaker 2>digit just to determine where a word starts and ends,

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<v Speaker 2>the state management would just be massively inefficient.

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<v Speaker 1>Yeah, the parse tree would be overwhelmingly huge just to

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<v Speaker 1>figure out how to spell a single variable name exactly.

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<v Speaker 2>So to solve that, the front end outsources the character

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<v Speaker 2>level reading to its very first subphase. This is lexical analysis.

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<v Speaker 1>The lexical analyzer. I kind of think of it as

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<v Speaker 1>the bouncer at a clubdoor. The bouncer, yeah, like it

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<v Speaker 1>acts as a filter between the raw source text and

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<v Speaker 1>the parser. The bouncer consumes the raw character stream, checks IDs,

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<v Speaker 1>meaning it groups letters into words. It throws out the trash,

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<v Speaker 1>which would be the comments in the useless white space,

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<v Speaker 1>and it only lets the VIP tokens into the club

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<v Speaker 1>to see the syntax parser.

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<v Speaker 2>That's a great way to visualize it. Let's take a

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<v Speaker 2>standard loop declaration like while open bracket eye less than

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<v Speaker 2>or equal to one hundred. The lexical analyzer iterates over

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<v Speaker 2>the raw ASKI values. It identifies the sequence whil e,

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<v Speaker 2>and it emits a single categorized token, the keyword while right.

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<v Speaker 1>It doesn't pass five letters to the parser. It passes

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<v Speaker 1>one token exactly.

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<v Speaker 2>Then it reads the open parenthesis and emits a bracket token.

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<v Speaker 2>It recognizes the eye and emits an identifier token.

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<v Speaker 1>And it also has to handle multi character operators right Like,

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<v Speaker 1>when it sees the less than sign, it doesn't instantly

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<v Speaker 1>just stit out an operator token good point.

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<v Speaker 2>No, it has to check the next character, seeing the

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<v Speaker 2>equal sign right after it, it groups them together into

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<v Speaker 2>a single logical less than or equal to operator token,

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<v Speaker 2>and finally it groups the one zero zero into an

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<v Speaker 2>integer token.

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<v Speaker 1>So by the time this stream reaches the VIP area

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<v Speaker 1>the syntax parser, the parser just sees a clean sequence

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<v Speaker 1>of five pre defined structural components. It never has to

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<v Speaker 1>worry about the spelling or if there were extra spaces.

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<v Speaker 2>But this raises an important question right, because spacing can

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<v Speaker 2>actually introduce significant complexity for the lexical analyzer itself, particularly

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<v Speaker 2>regarding what we call look ahead issues.

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<v Speaker 1>Look ahead issues.

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<v Speaker 2>Yeah, The analyzer frequently has to buffer characters and read

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<v Speaker 2>ahead of its current position just to determine the correct

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<v Speaker 2>token category. Like if it reads the digits one, two, three, four,

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<v Speaker 2>it cannot immediately emit an integer token.

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<v Speaker 1>It must look ahead because if the next character is

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<v Speaker 1>a decimal point, it realizes, oh wait, I'm building a

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<v Speaker 1>floating point number exactly.

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<v Speaker 2>Have to continue buffering. Yeah, and early language design actually

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<v Speaker 2>produced some notoriously difficult look ahead scenarios.

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<v Speaker 1>Oh, this brings up the four tren horror story from

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<v Speaker 1>the book.

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<v Speaker 2>Yes, four trend is the classic example here. Yeah, because

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<v Speaker 2>early specifications of FORO tran dictated that spaces had literally

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<v Speaker 2>zero syntactic meaning they were completely ignored by the compiler, which.

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<v Speaker 1>Sounds fine until you realize it forces the lexical analyzer

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<v Speaker 1>into a highly ambiguous state.

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<v Speaker 2>A very dangerous state. Consider a four tran loop initialization

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<v Speaker 2>looks like this, do space five space i equals one

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<v Speaker 2>comma ten. The intent here is to loop the variable

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<v Speaker 2>I from one to ten.

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<v Speaker 1>Right, So the tokens should be the keyword. Do the

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<v Speaker 1>label five, the variable i the assignment operator, and the

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<v Speaker 1>range values one in ten exactly?

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<v Speaker 2>But what if the programmer accidentally types a period instead

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<v Speaker 2>of a comma. Oh no, yeah, so it becomes do

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<v Speaker 2>five I equals one point ten. The entire token structure just.

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<v Speaker 1>Collapses because spaces are ignored.

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<v Speaker 2>Right, because spaces are completely ignored, The lexical analyzer reads

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<v Speaker 2>right past the do, past the five, and past the i.

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<v Speaker 2>It hits the equal sign and then sees the float

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<v Speaker 2>one point.

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<v Speaker 1>Ten, so it realizes this is not a loop declaration

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<v Speaker 1>at all, exactly.

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<v Speaker 2>It realizes it's an assignment, So it squishes those first

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<v Speaker 2>characters all together and categorizes do five I as a single,

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<v Speaker 2>brand new variable identifier and just assigns it the floating

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<v Speaker 2>point value of one point ten.

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<v Speaker 1>That is wild, a single typo of a period instead

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<v Speaker 1>of a comma completely changes the grammar of the entire line.

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<v Speaker 1>The lexical analyzer has to look way ahead in the

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<v Speaker 1>character stream, scanning all the way down to that comma

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<v Speaker 1>or period before it can definitively branch its logic and

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<v Speaker 1>categorize that initial doo as either a reserved keyword for

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<v Speaker 1>a loop or just the start of a variable name.

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<v Speaker 2>Which perfectly illustrates why the lexical analyzer is kept strictly

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<v Speaker 2>separate from the syntax parser modularity and efficiency. By absorbing

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<v Speaker 2>all that messy complexity of character buffering and white space

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<v Speaker 2>stripping and look ahead resolution, the lexical analyzer frees up

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<v Speaker 2>the parser to operate strictly on clean, high level grammar.

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<v Speaker 1>So if this lexical bouncer has to be incredibly fast

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<v Speaker 1>and efficient to process hundreds of thousands of raw characters,

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<v Speaker 1>how do we actually build it.

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<v Speaker 2>Well, we use the simplest, most restrictive grammar rule book

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<v Speaker 2>available in the Chomsky hierarchy.

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<v Speaker 1>Which is type three.

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<v Speaker 2>Yes Chomsky Type three grammars better no too most programmers

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<v Speaker 2>as regular.

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<v Speaker 1>Expressions, AH rejects everyone's favorite, right.

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<v Speaker 2>We use context free grammars the type two for the

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<v Speaker 2>parser because the parser needs to understand nested recursive structures

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<v Speaker 2>like math operations inside parentheses. But the lexical analyzer doesn't

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<v Speaker 2>care about nesting at all.

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<v Speaker 1>It just needs to know if a flat string of

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<v Speaker 1>characters matches a specific pattern exactly.

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<v Speaker 2>A regular expression is just a highly compact definition of

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<v Speaker 2>a search pattern. You can define a valid identifier with

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<v Speaker 2>a really simple rule like it must begin with a

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<v Speaker 2>letter followed by zero or more letters or digits.

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<v Speaker 1>But the real power of a regular expression isn't just

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<v Speaker 1>the syntax of the pattern itself, right, it's how the

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<v Speaker 1>compiler executes that pattern mathematically.

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<v Speaker 2>Yes, any regular expression can be systematically transformed into a

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<v Speaker 2>deterministic finite state machine or a transition diagram.

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<v Speaker 1>Okay, break that down for us.

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<v Speaker 2>Sure. The transition diagram basically operates through strict state progression.

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<v Speaker 2>You initiate the machine at state one. You consume the

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<v Speaker 2>very first character from the source stream. If that character

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<v Speaker 2>satisfies the transition condition for your rejects pattern, the machine

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<v Speaker 2>progresses to.

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<v Speaker 1>State two, and then you consume the next character.

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<v Speaker 2>Right, and the rules dictate the next state transition. Depending

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<v Speaker 2>on the character, you might loop back to state one,

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<v Speaker 2>you might stay in state two, or you might move

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<v Speaker 2>forward to state three.

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<v Speaker 1>And if the token string ends and the machine happens

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<v Speaker 1>to be resting on what's called a designated accepting state,

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<v Speaker 1>then the sequence is mathematically proven to be a valid

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<v Speaker 1>token of that category exactly.

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<v Speaker 2>And conversely, if it encounters an unexpected character and no

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<v Speaker 2>valid transition exists for it, the machine just halts and

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<v Speaker 2>throws a lexical error.

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<v Speaker 1>And the architectural advantage of this deterministic finite state machine

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<v Speaker 1>is that it guarantees linear efficiency big o EVN right,

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<v Speaker 1>big O event because the machine never backtracks, it never

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<v Speaker 1>second guesses its previous state. It consumes each character and

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<v Speaker 1>the source code exactly once, executes its state transition, and

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<v Speaker 1>moves forward. It's incredibly fast.

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<v Speaker 2>It is the absolute fastest theoretical mechanism a computer can

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<v Speaker 2>use to process sequential text and writing these state machines manually,

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<v Speaker 2>like mapping out the transitions for every possible asse character

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<v Speaker 2>would be a sprawling, unmaintainable mess of conditional logic.

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<v Speaker 1>So nobody does that by hand.

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<v Speaker 2>Oh, definitely not. Compiler engineers utilize software generator tools. You

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<v Speaker 2>feed the tool a list of regular expressions that define

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<v Speaker 2>your language as tokens, and the software automatically computes all

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<v Speaker 2>the state transitions and generates the highly optimized C code

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<v Speaker 2>representing that finite state machine.

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<v Speaker 1>So what does this all mean for you listening right now?

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<v Speaker 1>These algorithms, the regular expressions, the finite state machines. They

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00:19:40.519 --> 00:19:43.799
<v Speaker 1>aren't just for massive compiler projects at big tech companies.

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<v Speaker 1>If you've ever used a find and replace function or

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<v Speaker 1>scrape data from a document, you are using the exact

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00:19:49.759 --> 00:19:52.839
<v Speaker 1>same underlying computer science magic exactly.

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00:19:53.359 --> 00:19:57.200
<v Speaker 2>Learning how a compiler constructs these structures fundamentally alters how

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00:19:57.240 --> 00:20:01.480
<v Speaker 2>you approach system architecture. In general, viewing text processing is

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00:20:01.559 --> 00:20:04.720
<v Speaker 2>just a series of messy string manipulation functions, and you

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00:20:04.759 --> 00:20:09.160
<v Speaker 2>start viewing it astrict state transitions and grammatical reductions.

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00:20:08.720 --> 00:20:11.880
<v Speaker 1>It makes you a drastically better programmer in literally any field.

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00:20:12.480 --> 00:20:14.640
<v Speaker 1>So to wrap this all up and synthesize the entire

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00:20:14.640 --> 00:20:17.359
<v Speaker 1>pipeline we've talked about today, we start with human readable,

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00:20:17.559 --> 00:20:21.680
<v Speaker 1>high level abstractions like ordering a burger, totally insulated from

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<v Speaker 1>hardware constraints.

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<v Speaker 2>Right.

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<v Speaker 1>Then the compiler's front end initiates lexical analysis. It utilizes

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00:20:28.480 --> 00:20:32.680
<v Speaker 1>those finite state machines generated from regular expressions to consume

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00:20:32.839 --> 00:20:35.920
<v Speaker 1>raw characters and emit a validated stream of tokens in

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00:20:36.039 --> 00:20:36.799
<v Speaker 1>linear time.

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00:20:36.920 --> 00:20:39.119
<v Speaker 2>And then that clean token stream is fed into the

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<v Speaker 2>syntax parser.

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00:20:40.240 --> 00:20:44.480
<v Speaker 1>Which leverages Chomsky's context free grammars to construct a rigorous

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00:20:44.519 --> 00:20:49.039
<v Speaker 1>mathematical parse tree, naturally enforcing precedence and structural logic.

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00:20:49.200 --> 00:20:52.119
<v Speaker 2>Then the front end translates that validated tree into a

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00:20:52.200 --> 00:20:55.359
<v Speaker 2>machine independent intermediate representation.

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00:20:55.039 --> 00:20:58.599
<v Speaker 1>And finally the back end synthesizes that IR. It applies

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00:20:58.640 --> 00:21:02.279
<v Speaker 1>all those advanced optimization algorithms to generate native machine code

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00:21:02.279 --> 00:21:05.799
<v Speaker 1>that routinely outperforms hand optimized assembly.

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<v Speaker 2>Completing the entire translation from abstract human intent all the

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00:21:09.200 --> 00:21:12.000
<v Speaker 2>way down to a physical routing of electrons across a

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<v Speaker 2>silicon dye.

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<v Speaker 1>It is genuinely a breath taking feet of engineering. When

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00:21:15.279 --> 00:21:16.960
<v Speaker 1>you lay it out like that, it really is.

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00:21:17.480 --> 00:21:20.640
<v Speaker 2>The architecture of a compiler is honestly one of the

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00:21:20.640 --> 00:21:24.920
<v Speaker 2>most mature, highly refined pipelines in all of computer science.

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00:21:25.680 --> 00:21:29.680
<v Speaker 2>Our entire digital infrastructure, from the embedded systems and aerospace

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00:21:29.680 --> 00:21:33.599
<v Speaker 2>flight controls to the distributed databases running global finance, is

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00:21:33.640 --> 00:21:35.960
<v Speaker 2>built entirely upon these layers of abstraction.

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<v Speaker 1>Everything relies on it.

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<v Speaker 2>Everything. But and this is something to really think about.

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<v Speaker 2>A compiler is not a law of physics. It is

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<v Speaker 2>a piece of software written by human engineers executing mathematical rules.

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<v Speaker 1>Right, It's not infallible exactly.

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<v Speaker 2>If the software we write is only as reliable and

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<v Speaker 2>only as secure as the compiler that translates it, we

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<v Speaker 2>really must continually evaluate that foundation. How much of the

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<v Speaker 2>technology we rely on every single day is secretly shaped

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<v Speaker 2>by the invisible assumptions, optimizations, and structural decisions built into

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<v Speaker 2>these translation tools decades ago.

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<v Speaker 1>Wow, we trust the bridge implicitly, right. We rarely stop

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<v Speaker 1>to consider the exact chemical composition of concrete holding us up.

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<v Speaker 2>We just expected to work.

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<v Speaker 1>The pipeline is invisible, but it absolutely dictates the boundaries

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<v Speaker 1>of what our software can actually achieve. So the next

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<v Speaker 1>time you write a line of code, or trigger a build,

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<v Speaker 1>or honestly even just open an app on your phone,

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<v Speaker 1>take a second to consider the sheer mathematical complexity that

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<v Speaker 1>is firing instantly right beneath the surface. It is an

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<v Speaker 1>incredible architecture. Thank you so much for joining us on

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<v Speaker 1>this deep dive. We'll see you on the next one.
