Hugging Face published Open-R1 on GitHub, aiming to fully reproduce the DeepSeek-R1 reasoning model. The repo has 26.1k stars and 2.4k forks so far. The body only contains the repo's landing page navigation and metadata; it does not disclose the implementation plan, training data, reproduction progress, or benchmark results. I'd treat this as a public reproduction scaffold and collaboration hub for now, and wait for a technical report before judging fidelity.

Coinbase, Lindy, Harvey, and Cursor shifted workloads to cheaper models; Harvey reported Kimi 2.6 reached a 15% all-pass rate on Legal Agent Benchmark, versus Opus at 14%, with 100 tasks costing $84 versus $954.

The author retired an OpenClaw AI assistant after more than one month of use; the post says it required self-hosting, API setup, and keeping one home computer running 24 hours a day.

Princeton researchers released Goedel-Architect, an agent framework for Lean formal theorem proving. Using DeepSeek-V4-Flash, it reached 75.6% pass@1 on PutnamBench, with $294 in API cost for 672 problems, compared with Hilbert’s 70.0% and about $170,000 cost.

Ruan Yifeng excerpts observations from U.S. analysts who visited 14 Chinese AI and robotics companies in early May: the article estimates U.S. AI compute at about 8 times China’s by the end of 2025, while Chinese firms’ intelligence output per unit of compute is estimated at 4-7 times naive scaling.

Junpeng Zhang and coauthors argue that SFT on highly homogeneous data has an effective window of only hundreds to about 1,000 training steps, and their interaction-based warning signal detects overfitting before loss gaps appear, saving roughly 30%–50% of training compute.

DeepSeek plans to raise about RMB 50 billion in its first funding round, with post-money valuation expected at RMB 350 billion to RMB 400 billion; Liang Wenfeng, Tencent, and CATL plan to invest RMB 20 billion, RMB 10 billion, and RMB 5 billion respectively.

Microsoft AI says MAI-Thinking-1 uses three mechanisms—thermostat, circuit breaker, and self-distillation—to keep RL training stable for several thousand steps; the RSS snippet contrasts MAI’s discipline with DeepSeek’s efficiency and GLM’s endurance.

StepFun released Step 3.7 Flash with a 196B MoE architecture, using multi-matrix factorized attention to cut KV-cache cost to about 22% of DeepSeek models.

OpenRouter data shows open-weight models generated 69.1% of token usage since 2025, versus 30.9% for closed models, while share leadership shifted across DeepSeek, MiniMax, Kimi, MiMo, Qwen, Tencent Hy3, Alibaba, and Arcee releases.

StepFun released Step 3.7 Flash, a sparse MoE model with 196B parameters plus a 1.8B ViT, activating 11B parameters per inference and reaching up to 400 tokens per second.

A Reddit user ran DeepSeek-V4-Flash with vLLM on two ASUS GX10 DGX Spark nodes and reported 1,680 prefill tokens/s plus 39.8 decode tokens/s at a 256K context with MTP=2; the setup uses TP=2 over RoCE, fp8 KV cache, and fits about 1M tokens safely in KV cache.

The author trained Mamba-Titan-1.4B-Reasoning on a 12GB RTX 3060: a frozen 1.4B Mamba-1 backbone with 8 trainable MoE arms, 2.54B total parameters, Top-2 routing at layers 24/25, and about 50% math accuracy.

MiniMax Group has begun preparations for a domestic China listing, according to a regulatory filing, and the post identifies DeepSeek as a local AI rival; the RSS snippet does not disclose valuation, listing timeline, exchange venue, or fundraising size.

DeepSeek placed three models in OpenRouter’s monthly top 10 with more than 17 trillion tokens combined, including V4 Flash at 9.13T tokens; the article says Ascend’s MegaMoE operator raised Prefill throughput by 20% to 30% on DeepSeek V3.1 and Qwen3-235B tests.

FaceMind links low-frequency token degradation to two papers: SLoW appeared at EMNLP 2025, Adam's Law was accepted as an ACL 2026 Oral, and high-frequency rewriting raised DeepSeek-V3 math accuracy from 63.55% to 71.54%.

StepFun released Step 3.7 Flash with 196B total parameters, 11B active MoE, a built-in 1.8B ViT, and local execution on 128GB RAM.

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.

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.

Reddit user kexxty ran 288 structured-output calls through OpenRouter models, including Llama 3, Mistral, Command R, DeepSeek, and Qwen, and found similar JSON failure categories across local and API-only models. The MIT-licensed Python library outputguard validates against JSON Schema, applies 15 ordered repair strategies, includes 2,001 tests, and has no LLM provider dependency.

OpenRouter says Pareto Code observes the Pareto frontier using real market demand; DeepSeek V4 Pro ranks first, followed by GPT 5.4 Mini and Gemini 3.1 Pro, while the post does not disclose the scoring formula or evaluation sample size.

Reddit user fairydreaming ran DeepSeek V4 Pro Q4_K_M with a modified llama.cpp CUDA repo on one RTX PRO 6000 Blackwell Max-Q workstation GPU, using an 859GB model file; the shared log reports a 1M context window and 8.6 tokens per second generation speed.

Antirez open-sourced ds4, a native inference engine for DeepSeek V4 Flash that uses a few thousand lines of C to run a 1M-context model on a 128GB MacBook Pro at a reported 27 tok/s.