@VictoriaLinML
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MTS @thinkymachines | Native Multimodal Intelligence Prev: @AIatMeta @SFResearch • PhD @uwcse
San Francisco Bay Area
Joined December 2010
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Inkling is a 975B-41B(A) MoE model that natively reasons across modalities (text, images and audio). It is intelligent and versatile🌱.
I’ve had so much fun building it alongside an incredible team over the past few months and proud to openly share this work.
🤖 Made with AI
Today, we are introducing Inkling.
Inkling reasons efficiently across text, image, and audio modalities. We are making the full weights available.
thinkingmachines.ai/news/int…
Available today for fine-tuning on Tinker. Play with it in the Inkling Playground. 🧵
Victoria X Lin retweeted
The 2026 Perseid Meteor Shower at Stonehenge tonight ☄️🔥✨Photo Credit Nick Bull 🙏
🧡 Love seeing the open-source spirit spread to robotics and embodied AI development!
We have a huge news to share today!
Today we are unveiling the first truly accessible RL robot - welcome Microduck
A 25 cm tiny open-source biped with 15 actuators and packed with sensors (camera, speaker, LiDAR, NFC, bluetooth, wifi, etc) that you train yourself with reinforcement learning.
It's also playable out of the box with more than half a dozen fun and playful pre-trained policies to have it walk, sit, crouch, roller-skate, pick up objects with its articulated beak, and recover on its own.
And all for less than $400.
See all the details, play with the simulator and order it at: pollen-robotics.com/microduc…
(video with sound on 🔊)
🪄 We invite you to try Inkling’s agentic performance on OpenRouter
Tomorrow will be my last day at Google after 27 years, and watching it grow from 25 people to 190,000+ has been an amazing journey. Below is a note I shared with many people internally at Google today. An excerpt is:
It has been an absolute pleasure to work with you and to help build some of the most widely used and impactful products of all time. As a kid, I dreamed of helping build software that would be used by many people, and Google now has thirteen products used by more than a billion people (amazing!). Our work has had a tremendous impact in the world, and I have been lucky enough to collaborate and form friendships with many colleagues that I deeply admire, respect, and enjoy. It still brings me joy every time I see people out in the world using our products to find information, handle email, translate documents, watch videos, learn new things, navigate and understand the physical world, browse the web, use their phone, run large-scale computations on our infrastructure, ride in an autonomous vehicle, or perform complex tasks with the help of our AI systems. I hope you all share this sense of joy, because it is a shared accomplishment! Thank you to all of my colleagues at Google over many years!
Now I'm excited to go start @DiscoLoopAI with my longtime friends and colleagues @Sanjay_Ghemawat, @OriolVinyalsML, and @quocleix.
(Updated post: slightly redacted to not have some personal info)
Victoria X Lin retweeted
Inkling-Small is comparable to Inkling at a quarter the size. Weights are open, fine-tunable on Tinker today. Look forward to seeing what people make with it.
Today, we are releasing Inkling-Small.
Inkling-Small achieves comparable performance to Inkling at a quarter of its size. It features 276B total parameters, 12B active. We are making the full weights available.
thinkingmachines.ai/news/ink…
Fine-tune it on Tinker today, or chat with it in text, image, and audio on Tinker Playground.
Another open-weight release from @thinkymachines 👀 Inkling-Small is here.
With native reasoning over audio and images and variable thinking effort, it's a great choice for fine-tuning, with NVIDIA NeMo on NVIDIA DGX Station.
NVFP4 checkpoint here: huggingface.co/thinkingmachi…
Today, we are releasing Inkling-Small.
Inkling-Small achieves comparable performance to Inkling at a quarter of its size. It features 276B total parameters, 12B active. We are making the full weights available.
thinkingmachines.ai/news/ink…
Fine-tune it on Tinker today, or chat with it in text, image, and audio on Tinker Playground.
Victoria X Lin retweeted
Today, we are releasing Inkling-Small.
Inkling-Small achieves comparable performance to Inkling at a quarter of its size. It features 276B total parameters, 12B active. We are making the full weights available.
thinkingmachines.ai/news/ink…
Fine-tune it on Tinker today, or chat with it in text, image, and audio on Tinker Playground.
Presenting my grand unified theory of ML researcher impact: Your impact is directly proportional to how much pain you cause to infra.
Fundamentally, you can only inflict pain upon infra if your approach actually works. And the better your approach works the more pain infra is forced to endure.
So, to give some examples:
- MoE's add a ton of data-dependent computation => pain (shazeer++)
- GDN/KDA are the most complex architecture I've been forced to care about and a very annoying matrix inversion => pain (sonta++)
- Muon is much more annoying than Adam and causes annoying restrictions on parallelism => pain (keller/jeremy++)
- RL scaling forced many researchers to care about LLM inference and RL infra as a category => pain (tworek++)
Even papers like Attention Is All You Need have lead to significant pain! Before transformers were invented everyone was running small jobs and I never needed to think about kv-caches or 6D parallelism.
Victoria X Lin retweeted
Fun fact: I'm responsible of editing the limitations part, and this final line was written by Zhilin himself.
Victoria X Lin retweeted
Inkling is very good at using tools to solve vision tasks.
In this demo, Inkling relies solely on Python tools to generate segmentation masks, iteratively zooming in, cropping, and refining object boundaries.
Plug Inkling into your multimodal agent workflow!
huggingface.co/thinkingmachi…
Victoria X Lin retweeted
The progress has been extremely fast. Most of all, I am happy to be surrounded by good people on a critical mission.
Please hold us to your highest standards and share your feedback as you get to know Inkling!
Today, we are introducing Inkling.
Inkling reasons efficiently across text, image, and audio modalities. We are making the full weights available.
thinkingmachines.ai/news/int…
Available today for fine-tuning on Tinker. Play with it in the Inkling Playground. 🧵
Victoria X Lin retweeted
Meet Inkling, our open-weights model built to reason across words, images, sound, and tasks. I'm too tired to find the words, so I'll let Inkling speak for me.
Today, we are introducing Inkling.
Inkling reasons efficiently across text, image, and audio modalities. We are making the full weights available.
thinkingmachines.ai/news/int…
Available today for fine-tuning on Tinker. Play with it in the Inkling Playground. 🧵
Victoria X Lin retweeted
Honored to be in the agentic efforts in Inkling. I felt those sleepless nights were well-spent.
Inkling is just a checkpoint of our progress for the foundation model effort started this January. This is just a preview, and the best is yet to come. ✨
Today, we are introducing Inkling.
Inkling reasons efficiently across text, image, and audio modalities. We are making the full weights available.
thinkingmachines.ai/news/int…
Available today for fine-tuning on Tinker. Play with it in the Inkling Playground. 🧵
Victoria X Lin retweeted
Training Inkling was a lot of fun, we hope tinkering with it is too!
Today, we are introducing Inkling.
Inkling reasons efficiently across text, image, and audio modalities. We are making the full weights available.
thinkingmachines.ai/news/int…
Available today for fine-tuning on Tinker. Play with it in the Inkling Playground. 🧵
Victoria X Lin retweeted
Today, we are introducing Inkling.
Inkling reasons efficiently across text, image, and audio modalities. We are making the full weights available.
thinkingmachines.ai/news/int…
Available today for fine-tuning on Tinker. Play with it in the Inkling Playground. 🧵
Victoria X Lin retweeted
new post on harness engineering for AI self-improvement: lilianweng.github.io/posts/2…
It is hard to forecast how much the future of RSI will rely on harnesses. Likely harness engineering will evolve in the direction of self-improvement and enable auto-research, and, in turn, smarter models keeps harnesses simple.
Even when many harness improvement get eventually internalized into core model, the need to specify goals and context will not disappear.
Victoria X Lin retweeted
We started Thinking Machines a year and a half ago with a couple of instincts: that people should have much more ability to customize models and do research on them, and that even as AI becomes more autonomous, there's a lot more to build to make humans and AIs work well together.
A lot has happened since then, especially the massive progress in agents, so we wanted to revisit those instincts in light of everything we've learned, argue about them, and write down what we actually believe now.
This is where we landed after a lot of debate. I'm happy with it!
We're building AI that people and organizations can shape and make their own. AI should extend our will and judgment instead of neglecting it; enabling that is the technical challenge we are working to solve.
thinkingmachines.ai/blog/the…
