In this tenth video, I explain how the Kate polynomial commitment scheme works. For more information about it, check this other blogpost I wrote. This polynomial commitment scheme will be useful to force the prover to commit to its polynomials before learning the random point they need to be evaluated at.
In this ninth video, I explain what polynomial commitment schemes are as well as their API. I also mention the Kate polynomial commitment scheme (KZG), based on pairings, and bootle/bulletproof types of polynomial commitments schemes, based on inner products.
In this eighth video, I explain how the prover and the verifier can perform a "polynomial dance" in order to construct the circuit polynomial $f$. The principle is simple: the prover doesn't want to leak information about the private inputs and the intermediary values in the circuit, and the verifier doesn't want to give the prover too much freedom in the way they construct the circuit polynomial $f$.
In this sixth video, I explain the compilation, or even compression, of a set of equations into a single polynomial. That polynomial represents all of our constraints, as long as it vanishes in an agreed set of points. With a polynomial in hand, we will be able to create a protocol with our polynomial-based proof system.
In this fifth video, I explain how we can "compile" an arithmetic circuit into something PLONK can understand: a constraint system. Specifically, a PLONK-flavored constraint system, which is a series of equations that must if equal to zero correctly describe our program (or circuit).
In this third video, I start by explaining what the protocol will use at the end: polynomials. It'll give you a glimpse as to what direction we'll be taking when we transform our program into something we can prove.