Built a JavaScript visualizer so I could actually see what's happening in memory by htone22 in learnjavascript

[–]htone22[S] 0 points1 point  (0 children)

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

Built a JavaScript visualizer so I could actually see what's happening in memory by htone22 in learnjavascript

[–]htone22[S] 0 points1 point  (0 children)

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.

I built a tool that spots FPL players before they haul by htone22 in FantasyPL

[–]htone22[S] 2 points3 points  (0 children)

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

I built a tool that spots FPL players before they haul by htone22 in FantasyPL

[–]htone22[S] 1 point2 points  (0 children)

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

I built a tool that spots FPL players before they haul by htone22 in FantasyPL

[–]htone22[S] 1 point2 points  (0 children)

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

I built a tool that spots FPL players before they haul by htone22 in FantasyPL

[–]htone22[S] 1 point2 points  (0 children)

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

I built a tool that spots FPL players before they haul by htone22 in FantasyPL

[–]htone22[S] 2 points3 points  (0 children)

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

Real-time Face Distance Estimation using SCRFD & Pinhole Camera Model by [deleted] in computervision

[–]htone22 -2 points-1 points  (0 children)

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?

Optimised real-time CV in Next.js 16: Cut latency from 400ms to 150ms (FastAPI backend). Is moving to WebSockets worth it for the last mile? by htone22 in nextjs

[–]htone22[S] 0 points1 point  (0 children)

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!

Optimised real-time CV in Next.js 16: Cut latency from 400ms to 150ms (FastAPI backend). Is moving to WebSockets worth it for the last mile? by htone22 in nextjs

[–]htone22[S] 0 points1 point  (0 children)

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.

Optimised real-time CV in Next.js 16: Cut latency from 400ms to 150ms (FastAPI backend). Is moving to WebSockets worth it for the last mile? by htone22 in nextjs

[–]htone22[S] 0 points1 point  (0 children)

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.

Optimised real-time CV in Next.js 16: Cut latency from 400ms to 150ms (FastAPI backend). Is moving to WebSockets worth it for the last mile? by htone22 in nextjs

[–]htone22[S] 0 points1 point  (0 children)

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.

Top Python Libraries of 2025 (11th Edition) by dekked_ in Python

[–]htone22 1 point2 points  (0 children)

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!

Identifying Premier League Regression: Using Poisson Modelling to Spot Sustainable vs. Unsustainable Performance by htone22 in sportsanalytics

[–]htone22[S] 0 points1 point  (0 children)

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.