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GLM-5.2 刚刚正式发布! 给大家带来实测! 直接说结论本次测试中, 提升最大的是Agent能力, 而且是有质的变化! 测试中GLM-5.2 完全不用搜索附近的位置, 就能直接去想要到达的地方. 这一切竟然是它在一开始把地图背下来了! 这在我测试的20多个模型中之前是没有一个模型能做到的, 比如之前的模型想去换电站, 那么都要搜一下附近有哪些换电站(这就会浪费一次tool_call), 而GLM-5.2直接就知道换电站的位置! 从来没用过搜索函数. 这种一开始就把需要的数据内化到上下文中, 并且能够贯穿整个1M上下文进行推理的能力真的是叹为观止. 除此之外, 本次测试后端代码的 Agentic Coding 能力也有提升, 来到了总榜的第二名. 而本次测试暴露出最大的短板则是空间理解. 其实成也萧何败也萧何, 它虽然把换电站的位置都背下来了, 但是去的换电站却不是最近的, 所以虽然记住了, 但是记住了之后在用之前再根据自己当前所在位置推理一下, 他还是没有做到的, 这也是最大的短板了, 强烈建议官方优化一波. #GLM52# #智谱# #智谱AI# #AgenticCoding# #长上下文能力#

📣 Kimi K3 is now available in GitHub Copilot for @code! Try Kimi Moonshot's latest open-weight model for agentic coding, now hosted by @FireworksAI_HQ. 📖 Learn more:

I've been using Grok Build since the first 24-hours it was made available. It is still my favorite agentic coding cli, and there are so many little features I just love. So I thought I'd put together a little thread on what makes Grok Build so special, and some features you should try if you haven't yet. Let's kick it off 🧵

I’ve gotten so used to Grok’s speech-to-text that typing now feels like hell Quick tip: Even when you’re using another agentic coding harness, try Grok STT through Grok Build Anthropic’s speech-to-text is still pretty basic actually in my experience. With longer technical prompts, it can quickly go off the rails and completely distort what you were trying to say Just press Ctrl + Space, start speaking, and let Grok transcribe the entire thought in real-time You can then copy it into another tool or press Enter and send it directly to Grok 4.5 right there in Grok Build It’s fast, highly accurate and paired with one of the most capable and efficient frontier models available Long, detailed rambling is often exactly what an AI agent needs to understand what you actually want

New course: Build LLM applications that respond to user requests quickly by running on hardware designed for fast inference. This short course was built with @Cerebras and taught by @zhennydez, @duerr_seb, and @MilksandMatcha. When a model generates text, much of the time is spent moving its weights out of memory and into the compute units. Inference-optimized hardware minimizes that movement, making token generation several times faster than on a typical GPU setup. In this course, the hardware you'll use is Cerebras' Wafer-Scale Engine, which is designed for fast inference by keeping the model's weights close to the compute units. Fast inference makes lengthy agentic workflows go faster, and also unlocks latency-sensitive, real-time applications like live translation and voice agents. Skills you'll gain: - Compare how GPUs, TPUs, and Cerebras' Wafer-Scale Engine each handle the memory-to-compute bottleneck - Build real-time applications powered by fast inference, including personalizing a webpage and running a multi-step workflow to analyze market signals - Adopt concrete habits for agentic coding with fast inference, keeping your sessions focused and steering the model more effectively My teams use Cerebras for several applications that are latency sensitive. Join and build LLM applications that respond quickly:

Some observations on Kimi: 1. It's a very good model! I don't think its performance can be explained away by distillation or anything like that. In agentic coding sessions, it seems pretty much on par with the best public models of Q1 2026. In my fairly limited use, it also seemed very token hungry. It's not obvious to me that this model is actually that cheap to run. 2. I am personally surprised the Chinese state continues to allow the open sourcing of models this good, given potential risks. To be clear, I *myself* might be fine with models presenting this level of marginal risk being open weight, but I am surprised that China is fine with it. I suspect the reason they are is 75% explained by strategic blindness/lack of AGI-pilledness (the CCP is very Yann Lecun-y in its views of AI). The other 25% or so is their lack of compute for customer inference (making China's open-weight strategy an unintended byproduct of US export controls) and the normal Chinese strategy of aggressive exports. For the companies, as opposed to the government, the decision to open source is partially ideological and partially because they are behind, and they know that very few people would pay for sub-frontier models from China. 3. Open-weight models are inherently decelerationist, and I'm continually surprised to see the so-called "accelerationists" so excited about open-weight models. I suspect the reason they are is that they know open-weight models are effectively ungovernable, and they simply like the overall cloak of ungovernability open-weight models create over the whole of AI. It's not a bad strategy; it reminds me of James Scott's recounting of the hill people in "the art of not being governed." Still, in the end, open-weight models deter further AI capex. 4. One probable outcome of an open-weight-model-dominant world is full AI communism, which is precisely what China proposes: rather than a market product, AI is a "public good" which will ultimately be provided by the state as a kind of "digital public infrastructure." This future strikes me as a dystopian hellscape, but I've never met an open-weight models advocate who doesn't ultimately concede this is where things end. You'd be surprised how many 'accelerationists' lobbied me, while I was in government, to support an eleven or twelve-figure federally funded data center so that startups could train models at a subsidy and then give them away for free. There was no other way for AI to progress, they said. Perhaps this is the logical end state of things. Nonetheless, I find myself surprised to see supposed accelerationists excited about such an outcome. I think many of them just don't know what they're doing. Many accelerationists do not view the creation and serving of frontier models as a legitimate business. 5. I would guess that the Trump Administration will at some point realize that their best strategy here would be to create large amounts of regulatory risk around the use of open-weight Chinese models. You don't need to "ban open source" (one of the dumber motifs of AI policy discussion). You just need to direct every agency to issue soft law that creates FUD. "A Federal Reserve Advisory Bulletin found that there may be backdoors in Chinese AI models." It needn't be that well justified. You just create enough regulatory risk that every regulated enterprise backs off. You probably don't want to create so much regulatory risk that you scare off the hyperscalers from serving Chinese models; this will just drive startups to sketchier providers. There's a happy middle ground here. I'd assume they will do some version of this. 6. It's probably true that open-weight models of this capability make the world a bit more dangerous, but not so much more that you'll really notice. At some point the models will be capable enough that you will notice. "A nonliving, invisible, dangerous, and infinitely self-replicating agent escaped from a Chinese lab," you say? Color me shocked.

Kimi K3刚刚发布：开放的前沿智能模型。 核心信息： 🔹 2.8 万亿参数，100 万上下文，原生多模态 🔹 Kimi Delta Attention 在百万 Token 上下文中，解码速度最高提升 6.3 倍 🔹 Attention Residuals 让训练效率提升约 25%，额外成本低于 2% 🔹 面向长周期 Agentic Coding 和自我进化工作流

Thinking Machines - Inkling Thinking Machines Lab, the company founded by Mira Murati, has released Inkling, its first open-weights model under the Apache 2.0 license. Inkling is a mixture-of-experts transformer with 975 billion parameters in total, yet only 41 billion of them are active for any given token. Every layer contains 256 routed experts and 2 shared experts, and a router selects just the 6 most relevant experts per token. This means that only about 4 percent of the model performs computation during inference. The backbone is a 66-layer decoder-only transformer that combines local and global attention layers and supports a context window of one million tokens, which is roughly enough to fit eight novels or an entire codebase into a single prompt. The model was pretrained on 45 trillion tokens spanning text, images, audio, and video. Instead of relying on separate vision or audio encoders, it converts images into patches and audio into discrete tokens, then projects everything into one shared hidden space. All modalities are therefore fused from the very first layer. Inkling accepts text, images, and audio as input, while its output remains text only. On public benchmarks, it performs at the level of GPT 5.6 Sol and Claude Fable 5 in reasoning and agentic coding. The weights are available on Hugging Face, and the model can be fine-tuned through the Tinker API. #MiraMurati# #thinkingmachines# #inkling# #OpenSource#

I used Grok 4.5 in Grok Build to create a game engine for agentic coding, then built an ARPG on it, with all game assets generated via Grok Imagine in Grok Build. AI agents can build worlds, combat, loot, progression, maps, tests, and playable games. ⚒️🎮

OpenAI reveals Codex Micro (their first hardware product, so to say) OpenAI’s $230 Codex Micro is a compact control deck for agentic coding, with RGB status keys, shortcuts for common Codex actions, and a dial for adjusting reasoning effort. Built with Work Louder, it works on Mac and Windows and is designed to make managing multiple agents feel faster, more tactile, and less dependent on constantly switching between chats. It’s a bit gimmicky, but one thing is clear: OpenAI works within and with the community, developing cool products that are fun and capture the zeitgeist.