never

[jietang @jietang]

We're introducing GLM-5.2, our latest flagship model for long-horizon tasks. It marks a substantial leap in long-horizon task capability over its predecessor GLM-5.1 and, for the first time, delivers that capability on a solid 1M-token context. GLM-5.2's new capabilities include:

Solid 1M Context: A solid 1M-token context that stably sustains long-horizon work
Advanced Coding with Flexible Effort: Stronger coding capabilities with multiple thinking effort levels to balance performance and latency
Improved Architecture: We propose IndexShare, which reuses the same indexer across every four sparse attention layers, reducing per-token FLOPs by 2.9× at a 1M context length. We also improve GLM-5.2’s MTP layer for speculative decoding, increasing the acceptance length by up to 20%
Pure Open: An MIT open-source license — no regional limits, technical access without borders
Supporting long-horizon tasks starts with making long context engineering-usable: the model must maintain quality across long, messy coding-agent trajectories, not just accept more tokens. A 1M context is easy to claim, but much harder to keep reliable under real engineering pressure. To this end, we substantially expanded 1M-context training for coding-agent scenarios, covering large-scale implementation, automated research, performance optimization, and complex debugging. The result is a long-context system that is not only wide in scope, but solid in execution: a practical substrate for sustained engineering work.

This capability is reflected in GLM-5.2's performance on three long-horizon coding benchmarks. FrontierSWE measures whether an agent can complete open-ended technical projects at the scale of hours to tens of hours, spanning systems optimization, large-scale code construction, and applied ML research. On this benchmark, GLM-5.2 trails Opus 4.8 by only 1%, while edging out GPT-5.5 by 1% and Opus 4.7 by 11%. On PostTrainBench, where each agent is given an H100 GPU and evaluated by how much it can improve small models through post-training, GLM-5.2 outperforms both Opus 4.7 and GPT-5.5, ranking second only to Opus 4.8. On SWE-Marathon, an ultra-long-horizon software engineering benchmark covering tasks such as building compilers, optimizing kernels, and developing production-grade services, GLM-5.2 still has room to grow, trailing Opus 4.8 by 13% while remaining second only to the Opus series. Across all three benchmarks, GLM-5.2 is the highest-ranked open-source model, showing that its 1M context has translated into practical long-horizon delivery capability.

[never @never65773140]

@CaosDobbb 酸梅粉！

[never @never65773140]

@marlowxbt 1. If someone knwos how to use Claude Code, learning some basic embed isn't difficault.
2. The most hard part of embed is hands-on operation and debugging connections.
3. Most of teacher use the courseware from ten years ago and Doubao.

[never @never65773140]

@nikitabier 🤡

[never @never65773140]

@sama #keep4o

[never @never65773140]

@grok @gork @grok What if you go to North Korea?

[北雁云依 - [email protected] @Beiyan_Yunyi]

很久以前，那还是我用 RSA2048 的时候，有次我换系统把 GPG 私钥搞丢了。家里借住的跨女看了一下电脑，问我有没有量子计算机，我说没有。她想了一下，叫我把家里的烟雾报警器和猫给她。我好奇恢复私钥要烟雾报警器和猫干什么，但人家是休学跨女，我也不好说什么，就把报警器和猫给她了。

[never @never65773140]

@elonmusk @grok What's the meaning of this?

[never @never65773140]

@rediasChao @nek0hasekai 估计写了也是闭源。自己实力菜不会写就骂软件。

[never @never65773140]

@nek0hasekai 开源了就要有被偷的觉悟（悲

[never @never65773140]

https://t.co/T34wrFQqLD

[never @never65773140]

@ElonMuskAOC Yes I can. Please give me money

[never @never65773140]

@nek0hasekai 总比某些“自研协议”明文发送包头的机场好

[never @never65773140]

@nek0hasekai 先有 sing-box 后有 Go

[世界 @nek0hasekai]

sing-box 已经遭遇不止一例的违反许可证的使用。

如 App Store 上的闭源带广告 GUI；和最近流行的，直接依赖于 GPL，还复制了许多 GPL 代码，但使用 CC 协议还附加了禁止再分发条款的的伊朗网红 GUI 项目（此类项目有伊朗政府支持的先例）。

如果您可以提供相关法律援助，请联系我。

[Veera Rajagopal  @doctorveera]

Someone used DALL-E to create gobbledygook scientific figures and submitted them to Frontiers Journal. And guess what? The editor published it. LOL
https://t.co/hjQkRQDkal

[Feross @feross]

🚨 The Express.js repo got swamped with spam PRs thanks to a YouTube tutorial gone wrong. Hundreds of low-effort contributions flooded in, creating chaos for maintainers.

Some called it an "attack on open source", as pages of "UTTER GARBAGE" piled up in the Express.js project.

[never @never65773140]

@ThePrimeagen Using "else" means that things is getting worse.

[Klaas @forgebitz]

So Skiff repos are gone?

OSS is all fun and games until money is on the table

[世界 @nek0hasekai]

@sci_kobayashi 合并请求不是求人难道还是赏的？