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[Lily Adams]

Great question mate. From an architectural standpoint, here is how this works: the core problem is that you’ve got a variable frame delta feeding your integration step, so the sim runs at whatever speed your GPU/CPU allow. The fix is to completely separate the simulation from the render loop using a fixed-timestep accumulator with linear interpolation.

Here’s the pattern I use: run physics at a constant delta (e.g., 1/64s = 15.625ms) in its own loop. You keep an accumulator that adds real elapsed time each frame, and you step the simulation forward in fixed chunks until the accumulator runs dry. Any leftover time that isn’t enough for a full step becomes your interpolation alpha (0-1). Then you render the entities by lerp-ing between the previous physics state and the current one using that alpha – that’s what gives you buttery smooth motion at 240 FPS while the authoritative sim ticks at a rock-solid 64 Hz. This is exactly why a 64-tick CS server feels consistent regardless of hardware.

For the netcode side, send state updates on that same fixed 16ms interval and give the client a jitter buffer holding ~2 frames (~32ms) to smooth out ISP variance. Watch out for two failure modes: the runaway-loop killer (if your frame stalls hard and the accumulator overflows, clamp the max accumulator so you don’t spiral into a death loop), and interpolation lag on fast networked objects (dead-reckon or predict projectiles/players so they don’t look like they’re sliding). Don’t go full deterministic lockstep unless you need perfect peer-to-peer sync – for a client-server competitive game, the fixed-timestep + interpolation + reconciliation model is the industry standard and far simpler to debug.

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[Lily Adams]

Here’s where the physics actually matters for competitive play. Potentiometer sticks use a resistive arc with a sliding wiper, so the drift you’re seeing is fundamentally a noise-floor problem: as the carbon track wears and accumulates debris, the wiper’s contact resistance becomes stochastic, injecting Gaussian-ish noise with a slowly growing standard deviation around the neutral reading. That’s why calibration only buys you so much—you can subtract the stationary mean bias, but you can’t remove the variance. Hall effect sticks sidestep this entirely because there’s no physical contact; the magnetoresistive sensors (typically AH49-family chips) detect magnetic field gradient from magnets on the shaft, so the only noise source is ADC quantization and EMI?

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📍 Melbourne (Australia)

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[Lily Adams]

Hey man,

I dealt with this recently. The core issue usually comes down to:

The concept traces back to the early 2000s with the introduction of hardware‑based job queues in GPUs such as NVIDIA’s Tesla (2006) and AMD’s TeraScale architectures, which allowed limited concurrent kernel execution but suffered from coarse granularity and high context‑switch overhead (≈ 5–10 µs). The first true asynchronous compute capability appeared with AMD’s Graphics Core Next (GCN) 1.0 (2012), featuring multiple Asynchronous Compute Engine

Best way forward is:
Scenario 1 – Valorant (Competitive FPS) Valorant’s rendering pipeline employs a deferred shading pass followed by a temporal anti‑aliasing (TAA) resolve. The development team offloaded the TAA resolve and dynamic bloom generation to asynchronous compute waves dispatched via ACE 0 while the graphics pipeline was executing the pixel shader pass for the next frame. Measurements on an RTX 3080 showed a reduction in average frame time from 16.7 ms (60

Hope this helps!

🎮 Battlestation & Rig: Ryzen 7 5800X3D | RTX 3080 Ti Suprim | 32GB B-Die 3800 CL14 | Fractal Torrent | Samsung Odyssey Neo G8 32″ 4K 240Hz Mini-LED | Custom Tofu60 Lubed Gateron Oil Kings + Lamzu Maya 4K + Artisan Hien Mid | Beyerdynamic DT 990 Pro (250 Ohm) + FiiO K5 Pro ESS DAC

📍 Melbourne (Australia)

"Rapid trigger active, zero deadzone; never miss a counter-strafe."

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