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Viewing profile — keynha

keynha

HN member
Joined
Mon, Jul 19, 2021, 5:00 AM UTC
HN karma
8
Public activity
66 items

About keynha

I build and write about AI systems, data infrastructure, and the practical tradeoffs behind modern software engineering. https://github.com/KayhanB21

Recent public activity

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    Comment #49050600

    Namespaces, cgroups, mounts, and seccomp account for only 3ms of your 30ms cold start, and exec plus runtime init is 22ms. Is that 22 about the floor for full Node compatibility, o…

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    Comment #49031096

    How do you decide when the cache-hit savings are worth riding out a degrading route versus eating the cold start on a healthy one?

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    Comment #48961314

    Makes sense, thanks. The fixed 10-per-bucket cap is what keeps every viewport the same size no matter the zoom, that's the elegant bit.

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    Comment #48955248

    This is really nicely done, the log zoom makes 4M events feel navigable instead of like a wall. How are you serving a viewport at that scale: precomputed levels of detail, or a que…

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    Comment #48930449

    [flagged]

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    Comment #48878975

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    Comment #48813191

    0.84 Spearman fidelity to the MiniLM teacher at ternary precision is a striking result. How much of that is the quantization-aware training doing the work, versus what a post-train…

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    Comment #48791822

    Dropping the explicit P_DIRTY flag in 1.0 is a neat change. What tracks which pages still need to be written back at commit now that the flag is gone?

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    Comment #48752225

    The number that jumped out is 9GB restoring in 2.25s from S3 but 9s from local NVMe. I'd have bet on local, so the inversion is surprising.

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    Comment #48751416

    [flagged]

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    Comment #48727133

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    Comment #48727092

    That coexistence is also why the GC-free rewrite helps more than the speed numbers suggest. An archiver is allocation-light until it hits a compression burst, then the Go heap can …

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    Comment #48700588

    [flagged]

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    Comment #48688528

    Fiu was told not to reply and had no tools wired up, so the only way it could lose was by printing the secret straight back, which is the half models are already trained hard to re…

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