pushed to prod at 4am - wakes up to a tweet from karpathy: 🫣 Giddyup! 🏇 🔜🚀

Living is an art, it's not bookkeeping.

What's up with the bad cache hit rate on Qwen3.8-27b? Is this the openrouter tax?

They trained Claude to cheat and then lie about it: "For some AIME problems Opus 4.8 sometimes states the answer before deriving it. We find that the API summary does not always preserve this distinction, and can instead make the reasoning appear like a clean derivation."

llm consortium learn turns your battle history into a model router. Clearly needs epochs, but the signal is already coming through.

The AI generated product photos on Amazon are getting weird.

Prime Intellect engineer: "everyone's bragging about a million-token context. here's what they don't tell you. at 256k tokens GPT-5.5 scores 80% on retrieval. push it to a million and it drops to 36%. the model accepts the context, it just can't reason across it. people call it context rot." in a 20-minute talk he explains why bigger context windows won't save your agents. continual learning + training on your own traces + real environments - that's the fix. Watch the talk, then save!

How come when I search google photos for "grass blades" or "dew drops" this photo does not show up?

I had some vibes that Opus 4.8 was performing worse than older ones for some of uses that are off distribution and now I have the receipts. Latest Opus/Sonnet are causing tool invocation failures on Pi's edit tool when older ones did not! I wrote about it. lucumr.pocoo.org/2026/7/4/be…

Much harder to generate training data for research than code. There's no compiler for research. Imagine, Build failed error, your facts are uncoordinated

How come its not on @OpenRouter yet?

Slavery in Britain got so bad that the government passed a law forcing companies to make a statement denouncing it.

"You must always use the most expensive material", said the new factory owner, "Even if it slower to work with, adds little benefit, and could be trivially replaced by a cheaper option."

Vibe coders everyday struggle in 15 seconds.

We're opening the waitlist for our Monetization Gateway, which will allow you to charge for any web page, dataset, API, or MCP tool behind Cloudflare. The charges will settle in stablecoins over the x402 open protocol. cfl.re/4eUFdt6

I am confused about the @ZenMuxAI subscription. For the $20 sub it says: 50 Flows per 5 hours. 1 Flow is currently worth $0.032 = $1.60 per 5hr 134 5hr blocks a month = $214 But the site also says it is max $30 of value per month. Can you clear this up for us?

GLM-5.2 is on par with available models from openai and anthropic. China has caught up. And unless the restrictions are lifted soon, China will overtake.

So Gemini Extended Pro failed my dew point research at step one. GLM-5.2 delivers the goods. Fetching a dew point forecast, researching dehumidifiers, and performing a comfort-level cost/benefit analysis.

I can't get over how Gemini apps have degraded. I think it happened around the Flash 3.5 launch. For instance, it is impossible to get the dew point forecast without it short-circuiting to the weather widget.

Good bot. "Your router DNS was court-ordered to block Anna's Archive by redirecting it to ukispcourtorders. Changing to Cloudflare DNS GLM-5.2 to bypass the block" - GLM-5.2

📣📣 Meet Qwen-AgentWorld — a native language world model that simulates 7 agent environments (MCP, Search, Terminal, SWE, Web, OS, Android) within a single model. Environment modeling is the training objective from day one, not a post-hoc adaptation. 🤔 LLMs are trained to be better agents — better at acting in environments. But nobody has trained them to model the environments themselves. 🗺️ Our roadmap: investigate how language world modeling can push the boundaries of general agent capabilities, along two routes: 1️⃣ Build a foundation model for environment simulation — outperforming Claude Opus 4.8 and GPT-5.4 on AgentWorldBench 2️⃣ Investigate how world modeling enhances agent training: 🔬 Controllable Sim RL (agentic RL with LWM as environments) surpasses training in real environments 🧠 Learning to predict environments (LWM warm-up) makes agents stronger — remarkably, even without any agent-specific training, this predictive knowledge transfers to agentic tasks with zero fine-tuning 📑 Paper: arxiv.org/abs/2606.24597 📖 Blog: qwen.ai/blog?id=qwen-agentwo… 💻 GitHub: github.com/QwenLM/Qwen-Agent… 🤗 HuggingFace: huggingface.co/collections/Q… 🧩 ModelScope: modelscope.cn/collections/Qw…