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🚀 Read this must-read post from Hacker News 📖 📂 Category: 📌 Main takeaway: Download from GitHubEnhancements- #647, #648, #649, #650, #652 - Implements the $USELIBRARY meta-command and functionality - @RhoSigma-QB64  - For use of our brand new QB64-PE Libraries Pack add-on.  - The add-on is optional to ease library usage, but the traditional way of $INCLUDEing libraries still works without changes.- #651 - Implements three new IDE editing features - @FellippeHeitor  - Auto-closing of brackets and quotes.  - Ctrl+D for line/selection block duplication (Edit -> Duplicate lines).  - Alt+Shift+Up and Alt+Shift+Down to move lines/selection blocks up and down.  -…

🔥 Read this trending post from Hacker News 📖 📂 Category: ✅ Here’s what you’ll learn: In the previous installments of Cursed Circuits, we looked at two switched capacitor circuits: the voltage halver and the capacitor lowpass filter.In today’s episode, I’d like to talk about the abuse of operational amplifiers to do analog math. Analog computing at a scale is wildly impractical because errors tend to accumulate over time; nevertheless, individual techniques find a number of specialized uses, perhaps most prominently in analog-to-digital converters. So, let’s have a look at how it’s done!The following assumes familiarity with core concepts in…

✨ Discover this trending post from Hacker News 📖 📂 Category: 📌 Key idea: Running an ONNX model in ONNX RunTime (ORT) with the CoreMLExecutionProvider may change the predictions your model makes implicitly and you may observe differences when running with PyTorch on MPS or ONNX on CPU. This is because the default arguments ORT uses when converting your model to CoreML will cast the model to FP16. The fix is to use the following setup when creating the InferenceSession: ort_session = ort.InferenceSession(onnx_model_path, providers=[("CoreMLExecutionProvider", 💬)]) This ensures your model remains in FP32 when running on a Mac GPU. Uncovering an…