BlogViral

Show HN: NeuroFlow 55.8x video inference speedup for Vision Transformers PyTorch https://ift.tt/Kk6jGZ1 May 26, 2026 at 11:04PM

Show HN: Speakrs Full PyAnnotate pipeline in Rust/ONNX 20-37x times faster macOS Speakrs implements the full pyannote community-1 style diarization pipeline in Rust: segmentation, powerset decode, overlap-add aggregation, binarization, embedding, PLDA, and VBx clustering. There is no Python runtime in the library path. Inference runs on ONNX Runtime or native CoreML, and the rest of the pipeline stays in Rust. It is 20x-30x faster on macOS, but only 2-3x faster on linux/cuda (depending on CPU). Few reasons its faster: 1. Speakrs is using coreml versions of the models. I exported the models specifically to run on coreml. PyAnnote just runs the same the same PyTorch versions through MPS (Metal) on macOS. 2. PyAnnote is not a single model, its a few different models put together in a pipeline, the readme has some info on the full pipeline. 3. Speakrs optimizes the pipeline so different parts can run on CPU, Neural Engine and GPU. Speakrs has a batch mode, where you can run on multiple fil...

Show HN: A website that tracks every stock trade Congress makes Congressional trading data is relatively commoditized, but I couldn't find any open-source version with the features I wanted. The data is lagged (median 28 days from trade to disclosure, and 19% miss this deadline), but there's still interesting patterns to explore. Repo: https://ift.tt/xavYLHF Let me know if you have any suggestions for improvements! https://ift.tt/yEVtSx4 May 26, 2026 at 11:28PM

Show HN: WYSIWYG markdown editor for any GitHub repo replace any github.com url with dunkdown.com https://ift.tt/AMdWCpu https://dunkdown.com May 26, 2026 at 08:47PM

Show HN: Write your BPF programs in Go, not C https://ift.tt/k72ITiZ May 21, 2026 at 11:25PM

Show HN: I made Pokémon but with real animals in the real world Firstly, apologies, it's not free. It would be difficult to support this for free, it's a paid game. I will now share the technical details, which will probably be most of interest for HN readers. I previously made a carbon footprint tracking app where you photo objects and it tells you the carbon footprint by using an LLM to estimate the data on the fly, e.g. 32kg CO2e / kg of beef, in the UK. At some point, I realised that it is possible to make a Pokémon-style game, but capturing real animals in the real world. This is now possible because: - image recognition is cheap, i.e. identifying animals, and the models (gpt-4o) can detect a (surprisingly) large number of animals and output their exact species. - LLMs can output a species' full taxonomy, pretty reliably. And, more importantly, they can generate game data quickly, on the fly. It would unfeasible to generate the game sprites (images) for every species...