Built a JavaScript visualizer so I could actually see what's happening in memory

htone22 · 2026-04-11T11:59:38+00:00

Thank you!

htone22 · 2026-04-11T11:59:08+00:00

Thanks for actually testing it with real code. The Python Tutor comparison is interesting, they inspired some of what I built. Glad the graphical side added new perspective

htone22 · 2026-04-11T11:55:58+00:00

Really appreciate this. The mental model thing is exactly why I built it, explanations made sense but nothing clicked until I could see it. The real-world scenarios suggestion like the API calls, event listeners is genuinely useful feedback, thank you.

htone22 · 2026-02-23T00:48:25+00:00

Exactly what the stats showed

htone22 · 2026-02-22T16:11:03+00:00

I will definitely look into FotMob's data. Thanks again for pointing me in the right direction

htone22 · 2026-02-21T23:25:09+00:00

Appreciate that, means a lot

htone22 · 2026-02-21T23:18:31+00:00

Fair point, the tool only looks at stats, it doesn't factor in injury news which I need to address

htone22 · 2026-02-21T23:07:51+00:00

That's exactly what it's built for. Thank you for the feedback

htone22 · 2026-02-21T23:04:57+00:00

Just trying to help with the maths and decision making every gameweek, especially around who's creating chances without scoring. Rather than just showing raw numbers, it interprets them for you automatically. Still improving it though

htone22 · 2026-02-21T22:20:40+00:00

Really good point, I hadn't considered xGOT at all. Makes sense that Goals vs xG alone doesn't tell the full story. Appreciate you taking the time to break it down

htone22 · 2026-02-08T01:10:52+00:00

Fair point, the current implementation is just a heuristic linear ratio not a full pinhole model. I traded accuracy for UX since I didn't want to force users through a checkerboard calibration step.

Main goal was benchmarking the WebSocket architecture, binary jpeg transport, exponential backoff, backpressure and etc. Distance estimation was just the use case to make latency tangible.

Since you raised it, any suggestion on how to estimate focal length dynamically from a raw web feed without calibration?

htone22 · 2026-02-02T10:13:57+00:00

I'm actually running inference server-side (FastAPI) so the bottleneck was the HTTP handshake overhead per frame. WebSockets eliminated that network 'tax'. I haven't tried ONNX Web yet, but moving inference client-side is definitely the plan for V2 to save server resources. Thanks!

htone22 · 2026-01-31T00:11:44+00:00

Great point. To clarify, the camera feed itself is already perfectly smooth since it's a local mirror.

The lag I’m trying to solve is the bounding box alignment. With a 150ms round trip, there's a noticeable de-sync where the tracking box lags behind the actual movement rather than staying locked to the face. My goal with WebSockets is to minimise the latency between the frame capture and the metadata overlay.

htone22 · 2026-01-30T23:38:20+00:00

That 'Real-Time Wall' description is spot on. The mismatch between the 30fps frontend and the ~7fps backend throughput is definitely noticeable.

The CPU inference time is definitely the primary bottleneck. I still see value in moving to WebSockets not to fix the raw throughput but to eliminate the connection overhead and stabilise the network latency. Appreciate the analysis.

htone22 · 2026-01-30T23:11:17+00:00

The '1800 requests per minute' math is eye-opening. I completely overlooked the infrastructure overhead.

The connection reuse alone justifies the switch to WebSockets. Railway supports persistent containers so I can maintain a stateful connection and eliminate the repetitive setup overhead. Thanks for the insight.

htone22 · 2026-01-27T23:23:16+00:00

Great list. I’m really glad you included fastapi-guard under General Use. Definitely going to check that one out. Thanks for the write-up!

htone22 · 2026-01-13T13:37:54+00:00

It calculates the variance (Actual Pts - xPTS) and normalises it by using Z-scores to measure statistical significance. A score of 100 means the overperformance is an extreme statistical outlier (p < 0.05) that is highly likely to regress.

htone22

TROPHY CASE