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[Grace Gray]

Great topic to crack open. Spectral subtraction is the workhorse here because it’s cheap and explainable, but the math reveals why it still leaks artifacts. You’re essentially working in the STFT domain, estimating a noise profile during silence gates (VAD-triggered), then subtracting it frame-by-frame: |Ŝ(ω)|² = max(|X(ω)|² − β|N(

"☕ Double shot espresso + tenkeyless custom mechanical board + dim ambient light."

[Grace Gray]

The core difference comes down to connection architecture and who owns the airtime. Proprietary 2.4GHz dongles like Lightspeed, HyperSpeed, or Quantum use a dedicated radio paired one-to-one with the receiver, running custom polling loops that push report rates from 1000Hz up to 4000-8000Hz on newer peripherals, which translates to sub

"☕ Double shot espresso + tenkeyless custom mechanical board + dim ambient light."

[Grace Gray]

The core reconciliation loop hinges on the client maintaining an ordered input buffer paired with server-acknowledged sequence numbers, not just naively trusting its own prediction. Here’s the mechanics: your client queues every input with a timestamp and monotonic ID, and the server echoes back which input IDs it has processed (the last acked input, L). When the ack arrives, the client rewinds to the authoritative state snapshot it received alongside the ack, then replays every un-acked input from the buffer in order. The reconciliation cost is proportional to the un-acked window, which is why you want to keep that window tight. A common optimization is batching inputs at your server tick rate (e.g., 64 or 128 Hz) rather than sending per-frame, and using delta-compressed input snapshots so the reconciliation replay stays deterministic. The failure mode everyone hits is prediction divergence when the server simulates something the client didn’t

"☕ Double shot espresso + tenkeyless custom mechanical board + dim ambient light."

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