@Polkadot is a platform on which blockchain networks are built and connected into one unified network. These connected chains, called parachains, form a living organism of networks all in sync and secure. This forms the foundation on which Web3 will be built.
Polkadot is complicated as hell! This 24-tweet thread is a beginner's guide to @Polkadot $DOT and @KusamaNetwork $KSM in simple English.
What is Polkadot? Kusama? Parachains? Crowdloan and Parachain slot auction?
Comment with other questions and share if you learn something ⤵️
@Polkadot is a platform on which blockchain networks are built and connected into one unified network. These connected chains, called parachains, form a living organism of networks all in sync and secure. This forms the foundation on which Web3 will be built.
Think of it as drag-and-drop blockchain consensus, or even easier, like Music EQ with dials up & down on certain features.
Dev teams use @Polkadot's Substrate framework to build custom chains for a specific use/vertical, such as @AcalaNetwork for DeFi, @PhalaNetwork for Privacy, @chainlink for Oracles, or @hydra_dx for liquidity.
Legacy networks like #Ethereum are called "layer 1" chains. These are single blockchains, operating in isolation. Parachains are also layer 1 chains. Polkadot is one level below, a "layer zero" multi-chain growing to 100+ networks.
@Polkadot will be bridged to existing networks like @ethereum and #bitcoin via bridges built by teams like @InterlayHQ (BTC), @snowfork_inc (ETH), @ChainSafeth & @centrifuge (ETH), @chainx_org (BTC), and more.
Kusama is essentially the exact same code/archtr as Polkadot, but its on-chain governance moves 4x faster (7 days) and there are lower barriers to entry for teams to get a slot on the network (more on this below).
The model is Testnet->Kusama mainnet->Polkadot mainnet.
This ensures code is as flawless as possible before going to @Polkadot
For the sim/difs between @Polkadot and Kusama, check out this post: https://t.co/3aYG3jWTgo
Teams like Acala are launching @AcalaNetwork on Polkadot and @KaruraNetwork on Kusama. @purestakeco is similarly launching Moonbeam (DOT) & Moonriver (KSM).
@Polkadot has 100+ parachain "slots" (see below) which must be 'leased' for access to Polkadot's security and ability to communicate with other chains.
In fact, no team is "built on Polkadot" until they win an auction!
Candle auction: https://t.co/6ZI6hwNux1
This will enable an meta-infrastructure running myriad blockchains and applications on those chains, all in sync and sharing security.
Just like the databases/protocols powering the apps we use every day, end users and consumers shouldn't even know Polkadot and Kusama are there: Web3 apps that operate as good or better than their Web2 counterpart.
More from Tech
Machine translation can be a wonderful translation tool, but its uses are widely misunderstood.
Let's talk about Google Translate, its current state in the professional translation industry, and why robots are terrible at interpreting culture and context.
Straight to the point: machine translation (MT) is an incredibly helpful tool for translation! But just like any tool, there are specific times and places for it.
You wouldn't use a jackhammer to nail a painting to the wall.
Two factors are at play when determining how useful MT is: language pair and context.
Certain language pairs are better suited for MT. Typically, the more similar the grammar structure, the better the MT will be. Think Spanish <> Portuguese vs. Spanish <> Japanese.
No two MT engines are the same, though! Check out how human professionals ranked their choice of MT engine in a Phrase survey:
https://t.co/yiVPmHnjKv
When it comes to context, the first thing to look at is the type of text you want to translate. Typically, the more technical and straightforward the text, the better a machine will be at working on it.
Let's talk about Google Translate, its current state in the professional translation industry, and why robots are terrible at interpreting culture and context.
Straight to the point: machine translation (MT) is an incredibly helpful tool for translation! But just like any tool, there are specific times and places for it.
You wouldn't use a jackhammer to nail a painting to the wall.
Two factors are at play when determining how useful MT is: language pair and context.
Certain language pairs are better suited for MT. Typically, the more similar the grammar structure, the better the MT will be. Think Spanish <> Portuguese vs. Spanish <> Japanese.
No two MT engines are the same, though! Check out how human professionals ranked their choice of MT engine in a Phrase survey:
https://t.co/yiVPmHnjKv
When it comes to context, the first thing to look at is the type of text you want to translate. Typically, the more technical and straightforward the text, the better a machine will be at working on it.
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