
How LinkedIn Could Feed You a Feed in Under 200ms (An Essay on Scale)
A quick note before we start: this is not a description of LinkedIn’s actual architecture.
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Prompt engineering, system design, and building AI products that hold up past the demo. Cross-posted from Substack.

A quick note before we start: this is not a description of LinkedIn’s actual architecture.
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Every social feed you scroll runs a scoring function on every post, in real time, before you ever see it.
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“Design Twitter.”
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Last weekend I spoke at CodeCon Summit. The talk was called “O Guia do Mochileiro da Engenharia de IA” — the hitchhiker’s guide to AI engineering — and the premise was ambitious: walk through 42 concepts anyone entering this field needs to understand today, in one session, for a room of over 300 engineers.
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Writing code fast stopped being a differentiator.
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“You have a 20GB CSV.
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Somewhere right now, a transaction is holding a lock open while it waits on a payment gateway to respond.
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Fragmented adoption feels like progress.
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Every shortcut in engineering is a loan — and the interest rate is decided long before you know you’re paying it.
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Every network call has a fixed tax.
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There is no universal standard for good code.
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Everyone cites Designing Data-Intensive Applications. It’s on the reading list of every serious engineering team. Engineers drop it in interviews. CTOs reference it in system design discussions. But most people read it once, nod along, and move on without internalizing what the first chapter is actually saying.
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There’s a type of cognitive overhead that engineers have normalized so completely they stopped noticing it.
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Most teams add streaming to their AI products because it feels faster.
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Every company I’ve worked with has the same problem dressed in different clothes.
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Traditional SaaS has a comforting property: infrastructure costs are largely fixed.
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There’s an idea that circulates in AI engineering circles called “caveman prompting.” The premise: write your instructions in stripped-down, telegraphic English — almost no grammar, no courtesy, just the bare intent — and the model will still deliver.
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Everyone tells you to build the moat.
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Getting the architecture right is necessary.
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The previous chapter made the case for workflows.
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