DeepSeek

Copilot Cowork is switching from unlimited pricing to usage-based billing because users running hundreds of tasks per week drove costs too high. Microsoft is considering an optional, fine-tuned, safety-guarded DeepSeek V4 hosted on Azure. A working model exists but no final decision yet.

DeepSeek raised over 50 billion yuan (~$7.4B) in its first external round, hitting a valuation above $50B. The deal structure is unusual: investors put money into a limited partnership managed by CEO Liang Wenfeng, get no voting rights, and face a five-year lock-up. China's state-backed AI fund is the only direct investor with voting rights. Liang himself put in about 20 billion yuan. Tencent and CATL are the largest outside backers. Liang told investors he prioritizes foundational AI research and AGI over short-term profits, and plans to keep building open-source models. DeepSeek's V4 Pro is roughly 11x cheaper on input and 35x cheaper on output than OpenAI's GPT-5.5. The $50B valuation is still modest next to OpenAI and Anthropic, both approaching the trillion-dollar mark.

Tomasz Tunguz analyzed a 500+ comment Hacker News thread to map the local coding stack. Qwen 3.6 35B-A3B leads model mentions at 33%, with the 27B variant at 20%, followed by DeepSeek Pro and Gemma4 31B. All use MoE architectures that run on consumer hardware. For agents, Pi leads at 49% and OpenCode at 45%, both lightweight harnesses for local inference. One commenter compared local Qwen to a junior dev needing guidance versus Claude Opus as a senior who thinks with you on architecture—15x vs 5x speedup. But zero cost, full offline capability, and privacy make the tradeoff worthwhile for many. SWE-bench Verified scores back this up: Qwen3.6 27B hits 77.2%, the 35B-A3B MoE variant hits 73.4%, close to Claude Sonnet 4.6 at 79.6%.

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.