When Fable 5 is used for frontier LLM development, it does not notify the user and instead limits the model’s capabilities through methods such as prompt modification, steering vectors, and PEFT. Anthropic estimated that this would affect approximately 0.03% of traffic.

Introducing Claude Fable 5: a Mythos-class model that we’ve made safe for general use. Its capabilities exceed those of any model we’ve ever made generally available.

Excited to share that ThunderAgent has been integrated into NVIDIA Dynamo as an experimental router for agentic workloads! ThunderAgent was designed to schedule at the granularity of agent runs, making agentic serving/rl upto 4x faster! Huge thanks to @0xishand , @KranenKyle , and the Dynamo team. They have been exceptionally efficient and proactive — the team had already started pushing this forward even before I officially joined @nvidia . Looking forward to seeing ThunderAgent ideas further evolve within Dynamo. And thanks for the help from @togethercompute Link: github.com/ai-dynamo/dynamo/… @simran_s_arora @Chenfeng_X @_weilix @yinfang_chen #AI #MLsys #Agent #Nvidia

📢 Official Announcement: Qwen Partners with Fireworks AI to Accelerate Access to Qwen Family Models We are pleased to announce a strategic partnership between Qwen and Fireworks AI to deliver optimized, production-ready deployment of Qwen's closed weights models via the Fireworks Platform. @FireworksAI_HQ This collaboration empowers developers and enterprises to: ✅ Deploy Qwen models with lower latency and reduced fine tuning and inference costs ✅ Leverage enterprise-grade reliability, security, and scalability ✅ Integrate seamlessly into modern AI workflows 🔹 Get started with Qwen on Fireworks: app.fireworks.ai/models/fire… #Qwen #FireworksAI #OpenSourceAI #LLM #AIInfrastructure #ResponsibleAI #DeveloperCommunity

I used to be a strong believer in the “Bitter Lesson.” However, my view began to shift once I realized that real-world agentic systems inevitably need to call external tools due to limitations in knowledge acquisition, precision computation, and environment interaction. An important observation is that LLMs, especially when deployed as agents, are not purely connectionist systems. Instead, they are better understood as a hybrid of connectionism and symbolism. While we encode discrete tokens into continuous representations through neural networks, we ultimately decode them back into symbolic forms to operate in the real world. For example, special tokens such as <EOS> serve as explicit symbolic markers that deterministically control termination. This illustrates that even within LLMs, symbolic structure plays a fundamental operational role. This reflects something deeper: humans use discrete symbols to make sense of a continuous world. We impose structure, define rules, and create abstractions so that reasoning and coordination become possible. Symbolism is not a relic of pre-neural AI; it is a mechanism for control. f we want LLMs to be controllable, we cannot ignore their symbolic layer. The question is not whether to use symbols, but how to use them more flexibly. We need better ways to integrate discrete symbolic structure with continuous neural computation, rather than pretending that scaling alone will dissolve the need for structure.

it gonna get so much better it’s unreal. we will look back on the first iterations of these apps in wonder that we got anything done

🔥Modifying 2 lines of code and get your agentic serving/rollout up to 3.9x faster losslessly! ⚡️Say hello to ThunderAgent, a fast, simple, and program-aware agentic Inference System. 🥇 We propose a program abstraction to schedule all GPU and CPU resources, the first principled approach for distributed agentic inference and rollout. 🌐 Blog: thunderagent.ai/ 💻 Code: github.com/ThunderAgent-org/… 📜 Paper: arxiv.org/pdf/2602.13692 #AI #ThunderAgent #LLMAgent #Mlsys 1/n

🔥Modifying 2 lines of code and get your agentic serving/rollout up to 3.9x faster losslessly! ⚡️Say hello to ThunderAgent, a fast, simple, and program-aware agentic Inference System. 🥇 We propose a program abstraction to schedule all GPU and CPU resources, the first principled approach for distributed agentic inference and rollout. 🌐 Blog: thunderagent.ai/ 💻 Code: github.com/ThunderAgent-org/… 📜 Paper: arxiv.org/pdf/2602.13692 #AI #ThunderAgent #LLMAgent #Mlsys 1/n

no one cares about your SWE-Bench score if your competitor is giving out free boba Qwen: ask our chatbot to order your drink. it pays.