Distributed Systems 101: the hidden foundation of blockchain everyone ignores
think you understand blockchain?
most don’t. they think bitcoin is “magic internet money” and ethereum is “the chain for smart contracts.”
truth: blockchain is a clever application of distributed systems.
if you don’t get distributed systems, you’re memorizing buzzwords.
let’s fix that 👇
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what even is a distributed system?
imagine a ledger, but everyone keeps a copy. you all message, verify, and agree on updates. no single person controls truth.
technically: independent computers (nodes) that appear like one system by passing messages over a network.
simple idea. brutally hard engineering.
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the core problem blockchain tackles
how do thousands of independent computers agree on one truth when some are slow, offline, or malicious?
that’s the byzantine generals problem.
blockchain’s move: make lying costly, honesty cheap, and coordinate with math + incentives.
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the terms you actually need to know
- nodes: the peers running the protocol (full nodes / validators).
- consensus: how they agree (bitcoin: pow; ethereum: pos).
- crash vs byzantine faults: stop responding vs lie/contradict. blockchains assume byzantine.
- fault tolerance: tolerate up to f faulty actors; byzantine-safe protocols need 3f+1 total (≥2/3 honest).
- state machine replication (smr): all honest nodes apply the same ordered tx → same state.
- flp impossibility: in fully async networks, deterministic consensus can’t be guaranteed with even 1 crash → real systems assume partial synchrony or use randomness.
- consistency (c): every reader sees the same, latest data after a write.
- availability (a): the system always replies to a request (doesn’t hang or error), as long as the node you reached is up.
- partition tolerance (p): the system keeps working even if the network splits or drops/delays messages between groups of nodes.
- cap theorem: during a network split you can’t have consistency + availability with partition tolerance. public chains choose availability + partition tolerance
→ eventual consistency (confirmations/finality).
- latency: global agreement isn’t instant by design (btc ~10m blocks; eth ~12s slots + epoch finality).
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why this matters for blockchains?
- decentralization is engineering, not idealism. it removes single points of failure/corruption.
- pow/pos aren’t vibes. they answer “how do strangers agree on truth without trust?”
- why tx aren’t instant. messages must propagate; conflicts resolve; consensus locks in.
you can’t have instant settlement + decentralization + strong security without trade-offs.
- resilience by design. many nodes can fail; the network still progresses.
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why different chains “feel” different?
- bitcoin: prioritizes security + decentralization; probabilistic finality (wait confirmations).
- ethereum (pos): BFT-style voting + slashing; deterministic finality once ≥2/3 stake votes (≈ 2 epochs).
- solana: proof of history (verifiable time) + tower bft → very low latency; tighter timing + beefier hardware.
- sui: narwhal+bullshark (dag) + object model → simple tx skip global consensus; parallelism by design.
- aptos: hotstuff-line bft + block-stm for parallel execution; modular pipeline for throughput.
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the plot twist
start with distributed systems, not “what is a blockchain.”
once you see messages → order → state, the magic becomes engineering.
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the deep questions to ask now
- nakamoto vs BFT: what’s the real difference?
- why does ethereum need ≥2/3 honest stake for finality?
- why did solana add a cryptographic clock?
- why can sui let simple transfers bypass consensus?
- what is partial synchrony, and why do most chains assume it?
“If I have seen further, it is by standing on the shoulders of giants.” - newton
blockchain stands on decades of distributed systems (lamport, lynch, paxos, pbft, hotstuff).
🧵 share if this finally made blockchain click beyond buzzwords.
follow me @scrapychian if this was helpful.
what do you want me to talk about next?
how can i improve so that i can simplify blockchain concepts.



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