ByteDance’s Seed team has introduced SeedRealtime, a native audio-visual full-duplex LLM. The model fuses audio, video and text in a...
Meta has released Muse Glimmer, a 30-billion-parameter multimodal model distilled from Muse Spark. It is tuned for always-on local agent...
NVIDIA has released NemotronLabs VoiceChat 11B, an open 11B end-to-end speech-to-speech model for real-time, full-duplex conversation. Instead of chaining ASR,...
In this tutorial, we develop an end-to-end sentiment analysis workflow using the Stanford NLP IMDb Large Movie Review Dataset and...
Long-running agents accumulate state that no transcript captures. A coding agent at step 10 holds edited files, a running dev...
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In this tutorial, we explore the advanced visualization capabilities of the XY Python library by building interactive, scalable, and extensible...
Tencent Cloud has open-sourced TencentDB Agent Memory v2.0, a team-level memory hub for AI agents. The idea is super simple:...
Microsoft has open sourced code-testing-generator, a polyglot agent that writes unit tests and then proves they work. It ships in...
Cloudflare has released Kitesurf, a stateless web browser built specifically for AI agents. It runs entirely in V8 isolates on...
Prime Intellect has open-sourced Prime Agent, a self-improving coding harness designed around two abstractions, the Recursive Language Model (RLM) and...
SkillOpt is a text-space optimizer developed by a team of researchers from Microsoft, Shanghai Jiao Tong University, Tongji University, and...
NVIDIA has released Alpamayo 2 Super, a 34B-parameter vision-language-action (VLA) model for autonomous driving, under an open commercial license. The...
@dataclass class Tile: tile_id: str doc_id: str source: str kind: str page: int seq: int y0: int y1: int path:...
Reflex AI has released XY, an Apache-2.0 Python charting library for interactive 2D visualization. Most Python charting stacks create one...
def run_eval(records, backend): print(f"\n backend = {backend.name} on {len(records)} questions") t0 = time.time() preds = backend.predict_batch(records) rows = for rec,...
