Recent posts
Our plan keeps open weights and safety compatible with each other. We hope that this will help resolve debates and policies that pit open weights and safety as opposite and incompatible; ensuring a future full of open weights!
Releasing weights indiscriminately isn't safe. Neither is keeping capable models inside a few labs. We think there's a path between them. We haven't mapped all of it. Our new post covers the part we can see: how we assessed Inkling, and why access should widen in stages. https://t.co/zGFbuXNr0Y
Inkling-small. 2 weeks after inkling Nearly as good as Inkling but 4x smaller. We're just getting started...🔥
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. https://t.co/BtYNcpkDRA Fine-tune it on Tinker today, or chat with it in text, image, and audio on Tinker Playground.
this looks pretty good for agentic. that it fits on a dgx spark is **chef's kiss**
Today we're releasing Laguna S 2.1, our most capable model to date. It's a 118B total parameter Mixture-of-Experts model with 8B activated per token, a context window of up to 1M tokens, and thinking and no-thinking modes. Capable enough to hold its own against models many times its size. Small enough to run on a single @NVIDIAAI DGX Spark. Laguna S 2.1 is fully open under OpenMDW-1.1, with weights available today on @huggingface https://t.co/xxGeAgo35R
wow, what a world-class model! congrats to the Kimi team.
Introducing Kimi K3: Open Frontier Intelligence 🔹 2.8 Trillion Parameters, 1 Million Context, Native Multimodal 🔹 Kimi Delta Attention enables up to 6.3x faster decoding in million-token contexts 🔹 Attention Residuals deliver ~25% higher training efficiency at <2% additional cost 🔹 Built for long-horizon agentic coding and self-evolving workflows Kimi K3 is now live on on https://t.co/zrk6zZxZUo, Kimi Work, Kimi Code, and the Kimi API. Open Weights by July 27, 2026. 🔗 API: https://t.co/XCrgjXAqMw 🔗 Tech blog: https://t.co/YTfiMSNM1f
Modal trained a DFlash speculator that's much faster than MTP, making it a great boost for inference speeds!
Inkling by @thinkymachines is now available on Modal, backed by a custom DFlash speculator for 67% higher throughput and interactivity. Running on Modal Auto Endpoints with SGLang today.
Excited for our first general model Inkling -- open weights, 975B, natively multimodal (text, image, audio). Available on Tinker, HuggingFace and partners. It is yours to personalize and use openly. It is yours.
Today, we are introducing Inkling. Inkling reasons efficiently across text, image, and audio modalities. We are making the full weights available. https://thinkingmachines.ai/news/introducing-inkling/ Available today for fine-tuning on Tinker. Play with it in the Inkling Playground. 🧵
What do we do at @thinkymachines: Personalization/sovereignty, Human Participation, Decentralization. Democratize AI and make it useful for people. All three of them reduce society's dependence on centralized AGI companies (including ours when we get important), and that is a future worth aiming for. You've seen a preview of this with Tinker, Interaction models and our research openly published on Connectionism. A **lot** more to come very very soon...
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. https://thinkingmachines.ai/blog/the-future-worth-building-is-human/
世界最大級のヘッジファンドの一つであるBridgewater(Tinkerの顧客)が、興味深い金融ニュースとは何かに焦点を当てたモデルの微調整にどのように取り組んでいるかについて語っています。彼らの微調整モデルはフロンティアモデルより効果的でしかも安いです。
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Bridgewater, one of the worlds largest hedge funds, a Tinker customer talks through how they've carefully fine-tuned a model focused on what makes interesting financial news. Their fine-tuned model is more effective and cheaper than any frontier model.
Sorting which financial docs are worth an analyst's time is surprisingly hard for frontier LLMs. With an expert-labeled dataset and on-policy distillation, Bridgewater fine-tuned a model to do it reliably and cheaply. https://thinkingmachines.ai/news/learning-to-replicate-expert-judgment-in-financial-tasks/
Reardonと @flourishailabs チームへおめでとう。 もしAIのサンプル効率とエネルギー消費を人間レベルに持っていけたら、世界中の多くのことが変わるだろう！
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Congrats to Reardon and team on @flourishailabs. If they can get AI sample efficiency and energy consumption to human levels, thats going to change so many things in the world!
And we are live! https://www.wired.com/story/jeff-bezos-is-funding-a-wild-hunt-for-the-brains-core-algorithm/
Interaction Models のデモがもっとあって、システム設計を協力して行ったり、論文を読んだり、ライブ生成 UI でファクトチェックしてる
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more demos on Interaction Models collaboratively doing system design, reading papers, fact-checking with live generative UI
1. (System design) - The Interaction Models see your screen and collaborates with you live. Here we're building a scalable system architecture together — no copy-pasting, no switching tabs, just thinking out loud and drawing on the screen together.
クラスタマジシャンとGPUささやき職人たちよ、我々に参加しましょう！ リアルタイムインタラクティブモデル、Tinker、大規模トレーニングの背後にあるインフラを構築するスーパーコンピューティングエンジニアを探しています：スケジューリング、ストレージ、ネットワーキング、信頼性、大規模な分散システム。 NYCとSFで採用中 https://t.co/jCx00R6UvB
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Cluster magicians and GPU whisperers, come join us! We’re looking for supercomputing engineers to build the infrastructure behind real-time interactive models, Tinker, and large-scale training: scheduling, storage, networking, reliability, and distributed systems at scale. Hiring in NYC and SF https://t.co/jCx00R6UvB
Thinkyの秘密計画： 1: Human<->AIの帯域幅を上げる 2: Human+AIインテリジェンスの上限を上げる 3: 新しい世界で人間がメインキャラクターであり続けるのを助ける 俺らはStep 1にいる。 Interaction Modelsは人間のための素晴らしいリアルタイム協調ツール。 プレビュー：
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Thinky's secret plan: 1: Increase Human<->AI bandwidth 2: Raise ceiling of human+AI intelligence 3: Help humans continue as main-characters in the new world We are at Step 1. Interaction Models are great real-time collaborative tools for humans. Here's a preview:
People talk, listen, watch, think, and collaborate at the same time, in real time. We've designed an AI that works with people the same way. We share our approach, early results, and a quick look at our model in action. https://thinkingmachines.ai/blog/interaction-models
GithubとCriticalな仕事は相容れなくなってきている
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Github and critical work are becoming incompatible
何人かの伝説的な人たちによるTest of Time LLM...結構楽しい！
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Test of Time LLM from some legends...pretty fun!
Announcing Talkie: a new, open-weight historical LLM! We trained and finetuned a 13B model on a newly-curated dataset of only pre-1930 data. Try it below! with @AlecRad and @status_effects 🧵
これはちょっと興味深い。 AnthroはアカウントサポートをClaudeか従来のアカウントマネージャー経由でスケールアップする必要があるんだろう。人間を選んだら面白いな。 または、どんどん多くの企業が複数AIを統合ハーネスで使うようになる可能性もある。 クラウド時代のエンタープライズペイン問題に似てる。他のAIプロバイダーにも同じ問題が当てはまると思う。 https://t.co/8MOSWTylgs
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This is kinda interesting. Anthro probably needs to scale up Account Support via Claude or via humans (traditional account managers). Would be funny if they chose humans. Alternatively, more and more companies can probably go multi-AI with open harnesses. Similar problems to the cloud era enterprise pains. I'm sure these issues would apply to all the other AI providers. https://t.co/8MOSWTylgs
The Jensen + @dwarkesh_sp podcast was fantastic. Jensen is someone who understood how ecosystems work and someone who understands real-world trade, policy and controls work. And in some deeper sense how AI will actually diffuse into the world. In this podcast, Dwarkesh came off as someone who picked up talking points from an AGI party in the SF Mission District. And the contrast was so evident. As someone who understood ecosystems relatively deepy, maybe I understood Jensen's take more than others did (idk). Mythos, that Dwarkesh kept bringing up, is not a single absolute turning point in the AI development landscape. Take a state-of-the-art Chinese open-source model, and give it three orders of magnitude more test-time compute + post-training algorithmic advances that haven't been published yet. That's the baseline. It was evident that in whatever bubble Dwarkesh is in, that is seen as a naive or illogical baseline. When AI has such a complex development cycle, it's evident that America needs many levers of policy intervention across multiple layers in a dominant ecosystem that ideally the Western world controls. The entire premise that a particular model with AI development will have a critical phase change is neither correct nor does evidence point to it. OpenAI made this point with GPT-4, Anthropic made this point with Mythos, but neither stood / will stand the test of time. I think Jensen's repeated emphasis within the podcast to try to make this point mostly didn't get Dwarkesh's attention. And Dwarkesh (in this podcast) represents an entire cult of AI researchers and decision-makers that are going to influence policy. The thing with policy interventions is that if you do too much too early, you shoot yourself in the foot. There's a good reason American foreign policy and general sanctions of all kinds are measured and continuous. Despite Jensen's attempt at educating the "Anthro" audience how ecosystems work, I'm also not super hopeful a lot of people who've taken the extreme position will change their thought after listening to this podcast. I do think there's a certain religiousness that has permeated some of that community that would make it hard to understand ecosystems at a deeper level.
The Jensen Huang episode. 0:00:00 – Is Nvidia’s biggest moat its grip on scarce supply chains? 0:16:25 – Will TPUs break Nvidia’s hold on AI compute? 0:41:06 – Why doesn’t Nvidia become a hyperscaler? 0:57:36 – Should we be selling AI chips to China? 1:35:06 – Why doesn’t Nvidia make multiple different chip architectures? Look up Dwarkesh Podcast on YouTube, Apple Podcasts, Spotify, etc. Enjoy!
Just like us at @thinkymachines, @luke_drago_ @LRudL_ care a lot about amplifying humans, not replacing them. They're joining us to build towards that future!
Workshop Labs is joining @thinkymachines. We believe there's a path for AI to make humans matter more. We couldn’t be prouder to join Thinking Machines to see this work through. https://www.workshoplabs.ai/blog/wsl-joining-tml
