World Wire

Synthetic Sciences has released OpenScience , an open-source AI workbench for scientific research. It is licensed under Apache 2.0 and runs on your own infrastructure. The research team frames it as an open alternative to Anthropic’s Claude Science , launched in late June 2026. The pitch is direct. Scientific AI tooling should not be owned by one vendor. OpenScience keeps the workflow open, the models swappable, and the data local. It is an independent project, not affiliated with or endorsed by Anthropic. TL;DR OpenScience is an Apache-2.0, model-agnostic AI workbench for machine learning, biology, physics, and chemistry. It runs the full loop: literature, hypothesis, code, experiment, analysis, and write-up. Any model works (Claude, GPT, Gemini, GLM, Kimi, DeepSeek, local fine-tunes); switching is per-request. It ships 250+ editable skills, plus databases (UniProt, PDB, ChEMBL, arXiv, and ~30 more) as agent tools. It runs on your infrastructure wit...

In this tutorial, we build an end-to-end GRPO training workflow that teaches Gemma-3 to reason through GSM8K math problems using Tunix , JAX, LoRA, and custom reward functions. We start by preparing the environment, authenticating with Hugging Face, loading the Gemma-3 model, and wrapping GSM8K examples into a prompt format that requires both structured reasoning and a final numeric answer. We then define reward functions that assess format adherence and mathematical correctness, attach LoRA adapters to keep training lightweight, evaluate the baseline model, and run GRPO to improve the policy via group-sampled generations. It provides a reinforcement learning tutorial in which we train only the adapter weights while keeping the workflow compact enough for a single-accelerator setup. Installing Tunix and Configuring GRPO Training Copy Code Copied Use a different Browser import importlib.util, os, shutil as _sh if importlib.util.find_spec("tunix") is None: print(...

Meituan has released LongCat-2.0 , a large-scale Mixture-of-Experts (MoE) language model. It carries 1.6 trillion total parameters and activates about 48 billion per token . The model targets agentic coding: code understanding, generation, and execution inside agent workflows. Two facts stand out. First, LongCat-2.0 supports a native 1-million-token context window . Second, both training and serving ran entirely on domestic AI ASIC superpods . What is LongCat-2.0? LongCat-2.0 is Meituan’s next-generation trillion-parameter open model. It follows LongCat-Flash, a 560B model released in 2025. The architecture was designed around one goal: reliable, efficient agentic coding. Pretraining spanned more than 35 trillion tokens over millions of accelerator-hours. Meituan reports no rollbacks or irrecoverable loss spikes during the run. That stability claim matters on non-Nvidia hardware, where tooling is less mature. Architecture: How a 1.6T Model Stays Cheap to Run...

LlamaIndex has published legal-kb , a public reference application on GitHub. It is described as a knowledge base for legal documents, powered by LlamaIndex Index v2 (the LlamaParse Platform). The project demonstrates a pattern the team calls a Retrieval Harness for agentic retrieval. The approach differs from single-shot retrieval. Instead of one embedding search per query, an agent is given filesystem-style tools. It can then crawl a large, evolving knowledge base to solve a task. The tools mirror operations engineers already know: semantic and keyword search, regex grep, file search, and read. What is legal-kb? legal-kb is a working TanStack Start web app, not a library. You sign in, create a project, upload files, and chat with an agent. Each project is mirrored as a managed LlamaCloud Index v2. Uploaded files are parsed and indexed automatically in the background. The chat agent then queries that index live during each turn. The Retrieval Harness, in plain terms ...