General
Simon Edwardsson trains a 9M-parameter Mandarin pronunciation model using a Conformer encoder and CTC loss on ~300 hours of data to provide frame-level feedback and on-device inference. The approach uses Pinyin+tone tokens, forces alignment with the Viterbi algorithm, and achieves strong tone accuracy with a small browser/mobile-friendly model, illustrating a practical path for CAPT-style language learning tools.
Armin Ronacher discusses Pi as a minimal coding agent behind OpenClaw, highlighting its tiny core and extension system that preserves session state for powerful workflows. He argue…
Libreboot 26.01 Magnanimous Max is a stable release adding new board support and substantial coreboot/GRUB updates, with notable security and build-system improvements. The release…
Browser Use releases an open-source benchmark to compare LLM models for web automation. It details 100 hard but feasible tasks drawn from existing benchmarks plus 20 custom tasks, …
nanochat is an experimental harness by Karpathy for training and interacting with small-scale LLMs on affordable hardware. It covers the full lifecycle from pretraining to evaluati…