ThinkingMachines Inkling Now Available via Hugging Face Claude
Key takeaways
- ThinkingMachines' Inkling is now available through Hugging Face via Claude.
- This integration simplifies access for developers and researchers.
- It expands the ecosystem for those working with Claude's AI capabilities.
- The availability could enable new applications and enhanced workflows.
Who benefits
Summary
ThinkingMachines' Inkling, a tool or model, has been made accessible through Hugging Face's integration with Claude. This availability likely expands its reach and usability for developers and researchers working with Claude's AI capabilities.
Why it matters
This news simplifies access to a new AI tool for professionals already using Hugging Face and Claude, potentially accelerating development and enabling new applications by combining different AI capabilities.
How to implement this in your domain
- 1Explore the Inkling documentation on Hugging Face to understand its features and integration methods.
- 2Experiment with Inkling within existing Claude-based projects to assess its utility and performance.
- 3Consider how Inkling could enhance current AI workflows, such as data processing, model fine-tuning, or specific generation tasks.
- 4Share findings and best practices with your AI development team to foster adoption.
Original post by @_akhaliq
"thinkingmachines Inkling is now available in claude code via hf claude"
View on XPrimary sources
Originally posted by @_akhaliq on X · view source
Want to go deeper?
Turn these trends into skills with Learnijoy's hands-on AI & tech courses.
Explore coursesMore in AI Engineering & DevTools
Zapier vs. Tray: Enterprise Automation Platform Comparison for 2026
This post compares Zapier and Tray.io, evaluating which platform is better suited for enterprise automation needs by balancing power and ease of use. It argues that the best tools scale for complex requirements while remaining intuitive for all users.
New Optimizer Accelerates LLM Pretraining with Curvature-Conditioned Momentum
This research proposes a curvature-conditioned multiscale momentum method with sphere constraints to accelerate large language model pretraining. It addresses challenges from noise-dominant gradients and ill-conditioned loss landscapes by enhancing progress along flat directions, significantly improving upon existing adaptive optimizers like AdamW and Muon.
Euclidean Fourier Neural Operators Enhance Domain Transferability
This paper introduces Euclidean Fourier Neural Operators (EFNOs) as a domain-independent alternative to traditional FNOs, addressing their limitation in transferring across different periodic domains. EFNOs achieve this by parameterizing the spectral kernel as a continuous function of the physical wavevector, enabling consistent operator learning across varying domain shapes and sizes.