Large dataset streaming memory leak in TensorFlow

“Show HN: A memory database that forgets, consolidates, and detects contradiction Vector databases store memories. They don't manage them. After 10k memories, recall quality degrades because there's no consolidation, no forgetting, no conflict resolution. Your AI agent just gets noisier. YantrikDB is a cognitive memory engine — embed it, run it as a server, or connect via MCP. It thinks about what it stores: consolidation collapses duplicate memories, contradiction detection flags incom”

“partially decode, stream and filter big data with tensorflow_datasets (tfds) I have two issues (Note that this code is generated in google colab): Issue 1 I want to stream the droid dataset, which is almost 2TB big. I want to only use data which matches my filter conditions. For that I load the whole dataset and compute a generator yielding the next data sample, which matches the conditions. So that I don't need to load the whole data into RAM and filter on the fly. This is working for a test da”

“Show HN: Linear RNN/Reservoir hybrid generative model, one C file (no deps.) I just noticed it takes literally ~5 minutes to train millions parameters on slow CPU...but before you call Yudkowsky that it's over , an important note: the main bottleneck is the corpus size, params are just 'cleverness' but given limited info it's powerless. Anyway, here is the project: https://github.com/bggb7781-collab/lrnnsmdds/tree/main couple of notes: 1. single ”