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Ornith-1.0 raises the bar for open-source agentic coding models.
From 9B Dense to 397B MoE, the Ornith family delivers state-of-the-art performance across Terminal-Bench, SWE-Bench, NL2Repo, SWE Atlas, and ClawEval-while remaining fully open under the MIT license.
What stands out most is the training approach: jointly optimizing task-specific scaffolds and solution rollouts through reinforcement learning to produce stronger agentic coding outcomes.
A significant milestone for open-source Al, coding agents, and the broader developer ecosystem.
#Al#LLM#OpenSource#Coding#AgenticAl#MachineLearning
Aloha! 🌺 Meet Ornith-1.0, a family of open-source LLMs specialized for agentic coding.
Ornith-1.0 spans the full parameter sizes including 9B Dense, 31B Dense, 35B MoE, and 397B MoE. It achieves state-of-the-art performance among open-source models of comparable size on
Most AI videos look impressive.
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This feels different.
We're entering the era where AI video isn't just generating scenes—it's generating footage that looks ready to publish.
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Created with Seedance 2.0 4K on Lumina by BytePlus.
Enterprises and Developers can access the official API via BytePlus,
and you can also use it directly on @BytePlusGlobal@lumina_ai_aiart
BytePlus's Lumina platform: ai.byteplus.com/lumina/en
The AI race is changing.
It's no longer only about larger models or more parameters.
It's about which products can understand people, projects, and workflows.
ClickUp Brain² is entering that conversation in a meaningful way.
🚨MiniMax M3 is now available as an open-weight model on Hugging Face.
428B total parameters. Only 23B activated.
This is the first open-weight model to combine frontier coding, 1M-token context, and native multimodality.
What can the hidden wiring inside an LLM tell us?
Our latest research on graph probing, led by Yu Zheng, colleagues from Harvard, and our co-founder, Gabriel Kreiman, explores how transformer units connect and communicate. Early results suggest new ways to improve efficiency, reliability, and detect hallucinations.
Read the paper here: kreimanlab.com/code/graph-pro…
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