Managed private LLM endpoints, now available for everyone in @modal. Deploy in a few clicks with the UI or a few keystrokes with our CLI. The coolest thing is that these are not black boxes – customers have full access to the code underneath.

Saw a presentation from @barrowjoseph on vlm OCR today at https://t.co/5EJfcPMVJC. Great presenter and shared a lot of tactical insight. If similar interests, might want to check out https://t.co/VNVvGrDaqw. Thx @barrowjoseph!

Text-to-animation definitely still has a long way to go, but you can now iteratively prompt with a model like @AnthropicAI Fable 5 to get the animation you want. The experiment was only for the freestyle swimming part. Summary: - Task: Freestyle swimming with head turning to get some air every 4th stroke (ended up with every 2nd stroke, which is fine also) - 3% of weekly Fable allowance was used - Estimated cost: $1.20 - 7 iterations because Fable's vision is so terrible (please fix it @AnthropicAI). I had to keep taking screenshots myself and then describe every painstaking details of what's wrong with it, but it did get there eventually. And yes, all the shaders, water mechanics, tree, grass, etc. you see here will be open sourced soon (1-2 weeks). This is being developed as a mini-games engine based on @threejs for @callmesenseieng (Open Beta available soon).

We are pleased to present our latest research at #ICML2026, “Bridging Spherical Black-Box Optimizers” https://t.co/3FT6vn0dSn When optimizing through simulators, external APIs, or in reinforcement learning, gradients are often unavailable. Black-Box Optimization (BBO) fills this gap, but the field has been historically split into two categories: 1. Parametric Methods: Algorithms like Evolution Strategies (ES) scale to high dimensions but only find a single solution. 2. Nonparametric Methods: Algorithms like Consensus-Based Optimization (CBO) find multiple solutions but fail in high dimensions. Our team asked a simple question: what if they are all doing the same thing? In our paper, we showed that these distinct families are actually variations of a single update equation. By bridging this theoretical gap, we can now engineer custom hybrid optimizers for specific tasks. A key application of this is merging foundation models. Building on our previous work in Evolutionary Model Merging, we faced a computational challenge. Evaluating large language models at every step is resource-intensive, but using a smaller evaluation dataset causes standard unimodal optimizers to overfit. By treating LLM merging as a multimodal problem and deploying our newly developed hybrid optimizers, AdaPol and SchedPol, we successfully navigated this issue. The algorithms identified multiple distinct optima on the smaller dataset, allowing us to find generalized, high-quality merges at a fraction of the compute cost.

S3-clients + Hugging Face Buckets = 💥 You can now query and write directly to HF Storage Buckets via the S3-compatible API Just one secret. Done. 🚀 https://t.co/id0DaVJkSS

Hermes 引入了 /learn 从任何 input 习得可复用的技能🫡 Nowledge Mem 的 Skills 也有一样的能力 除了默默主动从历史上下文里摸索出可能潜在构成 skills 的机会提示给用户，用户激活之后可以在所有 agent 里调用，并且随着调用还会不断自优化、演进外； GUI 里的 Skill Creator 允许我们主动创建 Skill，它会自动找到相关的历史上下文进行创建和自优化。 其次我们根据用户老师们的建议，闭环了这个主动 flow，增加了 cli 和 ai-now 里的主动创建 Skills 的入口