#开源生态

Running MiniCPM5 from @OpenBMB on iPhone Air at 37 tok/s. One of the best open models at 2B parameters, now available in the NobodyWho Chat app.

认识一下 Mistral Large 4，又名 Le Chonk。 • 1T 参数，原生多模态。49B 激活。 在综合基准测试中，它是来自美国或欧洲的最佳开放权重模型。 • 在关键工作负载上达到最先进水平，包括网络防御、制造业和金融，并且在视觉定位方面超越了闭源前沿模型。 • 端到端在欧洲打造，并可通过我们自己的 Mistral Cloud 基础设施从欧洲部署。 • 今天即可通过 API 向所有人开放。正与网络安全合作伙伴私下合作。 开放权重版本将于十月底发布。

At Apsara Conference 2026 in Hangzhou, Alibaba CEO Eddie Wu shared our strategic roadmap for the Machine Intelligence era: - Scaling to 1,000x human capacity leaves an enormous growth runway ahead. - Machines are becoming the primary force behind Thinking. - Machine Thinking is currently <3% of all Human Thinking.

Looks like today may be a record day for token volume % of open models on Vercel AI Gateway: 🟦 Open 78.4% 🟨 Closed 21.6% While spend 💲 usually tells a different story, #3 and #4 today are Moonshot AI & DeepSeek. Adding Z⁠.ai, their combined spend surpasses OpenAI (#2). (Do note that's the spend for inference of the model across providers (mostly in the US), not revenue going directly to the open weight labs.)

Mozilla just published a 91-page report and it says open-weight AI is now only about 4 months behind the frontier. - 8 of OpenRouter's 10 most-used models by August token volume were open-weight, and 7 were Chinese-built, while DeepSeek became the first open model to lead the platform in weekly requests. - However, the economics are almost upside down. Open models handled roughly 20% of measured OpenRouter usage but captured only about 4% of model-layer revenue in the cited 2025 window. Mozilla attributes much of that mismatch to pricing, with closed models costing roughly 6x more per call at about 90% capability parity. The comparison shows why high usage does not necessarily translate into high revenue when one class of models is dramatically cheaper. Mozilla also warns that the revenue measurement is older than its 2026 usage data, so the current revenue split may be different. - Open models are spreading faster than they reach production: 79% of surveyed developers use them, but only 51% of open-model deployments reach production versus 63% for closed models. - DeepSeek showed that a new pretraining run may no longer be necessary for a major capability jump, gaining roughly 10 index points and then another 8 through post-training passes. -Even model diversification may provide less protection than assumed: Kimi K3 and Claude Fable 5 had a 0.72 per-task failure correlation, meaning supposed backup models often fail on the same problems.

Jacob is correct here—we really do earnestly believe AI could kill all humans! I personally think it is >10% within the next decade. I believe Anthropic is trying its best, but we do not yet have a plan to solve alignment for superintelligence and are not clearly on track to.

Excited to see the conversation around OpenAI’s Astra bringing recurrent architectures into the frontier spotlight. At Sapient, recurrence has been central to our vision from day one. We introduced HRM in June 2025 and open-sourced HRM-Text this May. It’s encouraging to see a direction we committed to early gaining wider attention. Want to explore recurrent reasoning yourself? HRM-Text is an open-source ~1B-parameter model trained from scratch on just 40B unique tokens, for about $1,000 in GPU compute-with a full pretraining framework to get you started. Explore the architecture. Train a model. Build on it. There’s still so much to explore in recurrent architectures. We’re continuing that work and building PRAXIST to help more researchers pursue ideas they believe in, just as we have with HRM.

🦞 @openclaw 2.0 has arrived! But what did it take to get here? 👀 Find out how creator @steipete and its maintainers built, scaled, and secured the fastest-growing open source project in GitHub history. 🚀 https://youtu.be/5VSwaUXtPIE?si=vdegRI2eH4wXo2lG