A community for AI developers that aggregates large-model development services, models, and compute power.
Seko’s newly launched Infinite Canvas unifies storyboard, asset scheduling, and image generation in a single node‑based workspace, cutting material‑building costs to near zero and driving record‑breaking view counts across multiple AI‑generated short‑drama seasons.
OpenAI’s Dreaming V3 memory system automatically aggregates user preferences and context from past chats, delivering up to five‑fold efficiency gains and raising factual continuity, preference adherence, and timeliness accuracies to 82.8%, 71.3% and 75.1% respectively, now available to free users.
Xiaomi’s MiMo‑V2.5‑Pro UltraSpeed delivers a 1‑trillion‑parameter model that generates over 1000 tokens per second on a standard 8‑GPU server by combining FP4 quantization, MoE architecture, DFlash decoding and TileRT’s custom execution engine, challenging the need for dedicated ASICs.
A 30‑person lab trained a 749B‑parameter Agent model called Macaron‑V1‑Preview using fewer than 300 GPUs, achieving less than 1% of the compute cost of comparable models while matching state‑of‑the‑art performance on real‑world Agent benchmarks such as LivingBench, VitaBench, A2UI and PinchBench.
The ITBench-AA benchmark, the first evaluation specifically for enterprise IT operations agents, tests 59 SRE scenarios and reveals that even top models like Claude Opus 4.7 achieve only a 47% score, highlighting both the difficulty of the tasks and the cost‑effectiveness gap between proprietary and open‑source agents.
OpenAI’s “The Next Era of Knowledge Work” report shows Codex powering over five million weekly active users with more than six‑fold growth, reshaping knowledge‑intensive tasks by tackling search, coordination and approval frictions, enabling parallel workflows, and prompting policy recommendations for broader AI adoption.
NVIDIA unveiled a suite of Physical AI Agent Skills at CVPR that connects data generation, simulation, policy training, and evaluation into a unified workflow, leveraging the Cosmos 3 multimodal model and tools such as InstantNuRec, AlpaGym, OmniDreams, and Alpamayo 2 Super to accelerate research in autonomous driving, vision AI, and robotics.
JoyAI‑Echo introduces a cross‑modal audio‑visual memory bank, a three‑stage post‑training pipeline, and a Director Agent to enable consistent, high‑quality, real‑time generation of minute‑level videos, achieving up to 7.5× inference speedup and state‑of‑the‑art benchmark scores.
This article breaks down the core terminology of AI agents—Model, Scaffold, Harness, Context Engineering, Policy, Tool Use, Skills, Sub‑agents, and the training‑side concepts of RL Environment, Trainer, Rollout, and Reward—explaining their roles, differences, and how they combine to form functional agents.
PaddleOCR‑VL‑1.6, a compact 0.9B visual‑language model, diagnoses three types of weak regions, enriches targeted data, and applies a three‑stage CPT‑SFT‑RL training pipeline to reach a 96.33% overall score on OmniDocBench v1.6, surpassing much larger models across all document‑parsing tasks.
