DeepSeek

QbitAI says DeepSeek V4 validated Ascend chip-model co-design, with CANN open-sourcing 65 repositories and supporting day-zero adaptation for more than 70 mainstream models, while AIGCode reported 65% MFU in MoE pretraining on Ascend.

DeepSeek plans to apply for a STAR Market IPO after completing a roughly $50 billion funding round, according to a large fund manager participating in the round; the post does not disclose valuation, timetable, filing documents, or company confirmation.

Xiaomi permanently cut MiMo-V2.5 API prices by up to 99%, matched DeepSeek V4 Pro pricing, increased same-price token allowances by 5–8x, reset existing user quotas in full, and set the new pricing to take effect on May 26.

Yi-Ling Liu compares U.S. and Chinese AI anxiety through labor, companionship, and agency: over 70% of U.S. teenagers report using chatbots as companions, while China is projected to reach 200 million single-person households by 2030.

A Reddit post argues small-model agent stacks are not default for business reasons, not capability limits: Gemma 4 31B reaches 86.4% on tau2-bench, and DeepSeek V4-Flash output tokens are priced about 89x below Claude Opus 4.6. The operational risk is verification, because 7–9B models produced broken reasoning for roughly half to two-thirds of correct answers in a cited audit.

Reasonix uses an append-only loop for DeepSeek V4 and reports a 99.82% cache hit rate in long coding sessions, cutting an example 400M-token bill from $61 to $12.

DeepSeek will make a 75% discount on its flagship AI model permanent, but the post does not disclose the specific model name, original price, discounted price, or effective date.

DeepSeek-V4-Pro API will keep its promotional pricing from June 1, with cached input at RMB 0.025 per million tokens, while Bloomberg says DeepSeek is pursuing a RMB 70 billion round at a USD 45 billion pre-money valuation.

DeepSeek will set deepseek-v4-pro API pricing to one quarter of the original price after the 75% promotion ends on 2026-05-31 at 15:59 UTC; the post does not disclose the exact per-token price.

The title says DeepSeek is advancing a $10.29 billion financing round and Liang Wenfeng commits to continued open-source AI model development; the body only links to Bloomberg and does not disclose round terms, investors, or a commercialization timeline.

DeepSeek is pursuing RMB 70 billion in funding at an estimated valuation of about $45 billion, with Tencent and IDG Capital close to participating and founder Liang Wenfeng potentially investing RMB 20 billion personally.

The title says DeepSeek’s founder declared an AGI goal and that a $10 billion funding round is advancing; the post does not disclose the founder’s statement, financing terms, investors, or timeline.

DS4 provides a macOS local runtime path for DeepSeek V4 Flash; the post only discloses three mechanisms—multi-agent integration, KV cache disk persistence, and activation steering—and does not disclose performance numbers, hardware requirements, or pricing.

A Reddit user ran DeepSeek-V4-Flash locally on 4 RTX 2080 Ti GPUs, reporting 284B total parameters, 13B active parameters, a sub-$2,500 build, custom Turing CUDA kernels, W8A8 quantization, 1TB DDR4 ECC RAM, and about 255 prefill tokens/s.

A Reddit user tested DeepSeek V4 on 45k, 180k, and 520k-token codebases and found 150k-250k tokens best for coding work. Past 300k tokens, line-number precision degraded; at 520k, outputs shifted toward architecture summaries and skipped implementation details.

Open AI model teams released Gemma 4, DeepSeek V4, Kimi K2.6, MiMo 2.5, GLM-5.1, and other versions this month, and the post says they were tested under CAISI’s V4 evaluation framework, but the RSS snippet does not disclose scores.

MiroMind researchers released the MOOSE-Star collection with three 7B models and TOMATO-Star, a dataset of 108,717 NCBI papers. MS-IR-7B reaches 54.37% inspiration-retrieval accuracy, uses DeepSeek-R1-Distill-Qwen-7B as its base, runs at about 14GB fp16, and supports llama.cpp, vLLM, and SGLang.

Alibaba DAMO Academy introduced I²B-LPO, an RLVR post-training framework that branches rollouts at high-entropy nodes and filters them with an information-bottleneck self-reward, reporting up to 5.3% accuracy gains and 7.4% semantic-diversity gains on math benchmarks using Qwen2.5-7B and Qwen3-14B.

Chinese think-tank representatives asked Anthropic in Singapore last month to give Beijing access to Mythos, and Anthropic refused; the company has limited the vulnerability-finding model to the U.S. government and more than 40 organizations.

DeepSeek optimized its latest model for inference on Huawei chips for the first time, while two semiconductor sources said training still relies on Nvidia chips; Huawei says it plans to release a training chip this year, but matching current Nvidia performance will take another year.