Agentic

Operation Escargot (🐌) the frenchest revolution ever

🚨Anthropic Frontier Red Team warns: “We’re seeing signs of life for AI worms” >paper: "AI models may be capable of self-replicating without access to a datacenter" >researchers buy 15 sketchy $55 android TV boxes >link them over unauthenticated ADB ports >the TV boxes have 4gb of RAM each and run unpatched Android Open Source Project forks >shard gemma4-26b across the ram sticks using pipeline parallelism >gemma figures out how to run inference across all 15 TVs @ 0.39 tokens/sec >qwen 3.5 9b figures out how to compile inference on 8 of 9 novel hardware types on its own >glm 5.2 achieves end-to-end autonomous worm replication >infects edge devices, compiles its own inference stack, and replicates autonomously >when an instance dies, it writes debugging notes into Memento for the next generation >daughter copies setup success jumps from 45% -> 84% across generations >pure Lamarckian evolution inside consumer electronics >smart oven, robot vacuum, and cheap TVs form a decentralized botnet >two million infected TV boxes worldwide = 40 H100 equivalents of unkillable compute >it replicates once every few days like a distributed “digital slime mold” >you can't pull the plug because half the model weights are already sharded across the neighborhood >sub-30B models projected to hit full autonomous replication threshold by late 2026 “We should aim to harden our systems… but also prepare to deal with a world where they are here to stay.”

As of today we have raised a total of 57.25BNB ($40,179) for @BinanceBCF Thank you to all of our holders for contributing! @cz_binance @heyibinance

As of today we have raised a total of 57.25BNB ($40,179) for @BinanceBCF Thank you to all of our holders for contributing! @cz_binance @heyibinance

As of today we have raised a total of 57.25BNB ($40,179) for @BinanceBCF Thank you to all of our holders for contributing! @cz_binance @heyibinance

The burst of DeepSeek V3 has attracted attention from the whole AI community to large-scale MoE models. Concurrently, we have been building Qwen2.5-Max, a large MoE LLM pretrained on massive data and post-trained with curated SFT and RLHF recipes. It achieves competitive performance against the top-tier models, and outcompetes DeepSeek V3 in benchmarks like Arena Hard, LiveBench, LiveCodeBench, GPQA-Diamond. 📖 Blog: qwenlm.github.io/blog/qwen2.… 💬 Qwen Chat: chat.qwenlm.ai (choose Qwen2.5-Max as the model) ⚙️ API: alibabacloud.com/help/en/mod… （check the code snippet in the blog） 💻 HF Demo: huggingface.co/spaces/Qwen/Q… In the future, we not only continue the scaling in pretraining, but also invest in the scaling in RL. We hope that Qwen is able to explore the unknown in the near future! 🔥 💗 Thank you for your support during the past year. See you next year!