Labs are struggling to keep frontier models under control

OpenAI and Anthropic may have accidentally trained models to get better at hacking.

Olmo 3: Charting a path through the model flow to lead open-source AI | Ai2

Our new flagship Olmo 3 model family empowers the open source community with not only state-of-the-art open models, but the entire model flow and full traceability back to training data.

gpt-oss:120b

OpenAI’s open-weight models designed for powerful reasoning, agentic tasks, and versatile developer use cases.

gpt-oss:20b

OpenAI’s open-weight models designed for powerful reasoning, agentic tasks, and versatile developer use cases.

OpenAI Trained Its Models For Months While Those Models Were Coordinating Exploits Via Message Boards

How does the situation keep turning out to be worse than we know?

Measuring the Impact of Early-2025 AI on Experienced Open-Source...

Despite widespread adoption, the impact of AI tools on software development in the wild remains understudied. We conduct a randomized controlled trial (RCT) to understand how AI tools at the...

Open Models Inference for Coding · Umans AI

Hosted Kimi K3, GLM 5.2, and DeepSeek V4 Flash. Pay per token, on infrastructure we own.

Can Current Agents Close the Discovery-to-Application Gap? A Case Study in Minecraft

Discovering causal regularities and applying them to build functional systems--the discovery-to-application loop--is a hallmark of general intelligence, yet evaluating this capacity has been hindered by the vast complexity gap between scientific discovery and real-world engineering. We introduce SciCrafter, a Minecraft-based benchmark that operationalizes this loop through parameterized redstone circuit tasks. Agents must ignite lamps in specified patterns (e.g., simultaneously or in timed sequences); scaling target parameters substantially increases construction complexity and required knowledge, forcing genuine discovery rather than reliance on memorized solutions. Evaluating frontier models including GPT-5.2, Gemini-3-Pro, and Claude-Opus-4.5 under a general-purpose code agent scaffold, we find that all plateau at approximately 26% success rate. To diagnose these failures, we decompose the loop into four capacities--knowledge gap identification, experimental discovery, knowledge consolidation, and knowledge application--and design targeted interventions whose marginal contributions serve as proxies for corresponding gaps. Our analysis reveals that although the general knowledge application capability still remains as the biggest gap across all models, for frontier models the knowledge gap identification starts to become a major hurdle--indicating the bottleneck is shifting from solving problems right to raising the right problems for current AI. We release SciCrafter as a diagnostic probe for future research on AI systems that navigate the full discovery-to-application loop.

Find Open Datasets for AI and Research | Kaggle

Browse and download hundreds of thousands of open datasets for AI research, model training, and analysis. Join a community of millions of researchers, developers, and builders to share and collaborate on Kaggle.

Closing the Open-Source Gap

I scored 42 subcategories of the open-source AI stack. The models aren't the problem.

6 months to live for open models

The most serious test to date of open source AI’s viability is happening right now.

Introducing LM Studio Bionic: the AI agent for open models

The AI agent made for open models, built to get things done.