By
–
Google just dropped Gemma 4 12B! You can now run it locally on just 8GB RAM using Dynamic GGUF from Unsloth. The architecture is different from any multimodal model before it. No separate vision encoder, no audio encoder. Both flow directly into the LLM backbone. Vision is
By
–
5/5 Takeaway: pipeline order is a hyperparameter. If you're already paying for parallel rollouts, reuse them – they're relevant context, not just candidate answers. Full write-up: [
https://
ai21.com/blog/first-sca
le-then-enrich-how-the-right-execution-strategy-helped-us-reach-state-of-the-art-on-swe-rebench/?utm_source=org-twitter
…]
By
–
You can use LangSmith Engine to review your agent traces to find bugs and areas for improvement across agent prompts + code. Between runs, the agent can review conversations, learn from real usage, and update Context Hub files.
By
–
THE ERA OF RELYING EXCLUSIVELY ON THE 3 MAJOR CLOSED MODELS IS OVER@MiniMax_AI's M3 is officially out 💥💥💥
— Charly Wargnier (@DataChaz) 4 juin 2026
It delivers the exact same capabilities you expect from a frontier model, combining massive leaps forward in a highly cost-efficient, open-weights package.
Here's why… pic.twitter.com/NDUppZzMlq
THE ERA OF RELYING EXCLUSIVELY ON THE 3 MAJOR CLOSED MODELS IS OVER @MiniMax_AI
's M3 is officially out It delivers the exact same capabilities you expect from a frontier model, combining massive leaps forward in a highly cost-efficient, open-weights package. Here's why
By
–
Researchers at Harvard and Penn State ran an experiment that should worry anyone who buys anything online. They built pricing robots out of GPT-4 and dropped them into a simulated market as competing sellers. No instruction to cooperate. No way to talk to each other. The only
By
–
Today I'm launching a new project called SynthTraces It is a minimal codebase to generate synthetic coding agent session traces using Pi (from @badlogicgames
) I wanted a large number of coding-agent traces, so I built a tiny harness where two models talk to each other: – an
By
–
Introducing NVIDIA Nemotron 3 Ultra.
— NVIDIA (@nvidia) 4 juin 2026
A frontier smart open model built for long-running agents that need to plan, reason, use tools and keep working across complex coding, research and enterprise workflows.
Up to 5x faster inference and up to 30% lower cost for agentic tasks.… pic.twitter.com/AcHTauUzjm
Introducing NVIDIA Nemotron 3 Ultra. A frontier smart open model built for long-running agents that need to plan, reason, use tools and keep working across complex coding, research and enterprise workflows. Up to 5x faster inference and up to 30% lower cost for agentic tasks.
By
–
As always, Nemotron 3 Ultra is fully open. This includes model weights, synthetic data, and post-training recipes. Available now on @huggingface →
By
–
We post-trained Ultra for popular agent harnesses like @openclaw
, @NousResearch Hermes Agent, and @Langchain
. The result is an open frontier model developers can customize for specialized agents across domains. Read more: https://
nvda.ws/4adkn6J
By
–
Today we're shipping Nemotron 3 Ultra.
— NVIDIA AI (@NVIDIAAI) 4 juin 2026
A 550B MoE frontier-intelligence open model built for long-running agents.
It delivers 5x faster inference and lowers the cost of complex agentic tasks by up to 30% versus other open frontier models. pic.twitter.com/FEXqvfzQFO
Today we're shipping Nemotron 3 Ultra. A 550B MoE frontier-intelligence open model built for long-running agents. It delivers 5x faster inference and lowers the cost of complex agentic tasks by up to 30% versus other open frontier models.