I tink, therefore I am Post-training API by @thinkymachines
San Francisco
Joined January 2026
- Tweets198
- Following1
- Followers16K
- Likes301
Tinker retweeted
we are observing this too. feels like a lot of performance to be squeezed from these models, and relatively straightforward to do with tinker + your own b200 cluster
it took me a while but I think I am coming around the the believe that finetuning (changing model's weights) is perhaps faster, cheaper and easier then prompt optimization. so many people are willing to run gepa and other prompt optimization, but for a reason I can't quite guess, much fewer are willing to finetune.
you can literally just say to Claude Opus 5-5:
i want a model that does X, find a paper about it, and some dataset. Make a better version using tinker from thinking machine. Start from inkling small model.
All you have to do is put credits on think machines service (if you have not given your credit card and a browser to you coding agent XD).
Tinker retweeted
it took me a while but I think I am coming around the the believe that finetuning (changing model's weights) is perhaps faster, cheaper and easier then prompt optimization. so many people are willing to run gepa and other prompt optimization, but for a reason I can't quite guess, much fewer are willing to finetune.
you can literally just say to Claude Opus 5-5:
i want a model that does X, find a paper about it, and some dataset. Make a better version using tinker from thinking machine. Start from inkling small model.
All you have to do is put credits on think machines service (if you have not given your credit card and a browser to you coding agent XD).
You can customize your weights with lora on tinker and serve that. We currently don’t support your own full weights.
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It’s encouraging to see how much tinker accelerated @msuiche- he’d previously burned 4600$ in gpu rental without finishing a single training step! He did some cool inkling 2 small experiments too.
That’s tinker’s goal - so cheap and easy that there’s no reason to do it yourself.
Extremely bullish on American AI. Here is my (cyber) review Inkling 2 Small (Thanks @cHHillee & @brucedang ).
I love the model, but I think I love the platform even more. I barely played with it and already feel how much potential it can unlock.
Tinker retweeted
Tessera-27B-B-CJK-RL: our compressed-CJK coding agent, now trained with RL.
AIME + HMMT 2026 11 point increase over non RL, Per agent turn it still uses 0.67x the base model's thinking.
Open weights: huggingface.co/ZelligeAI/Tes…
And our followup paper: zellige.ai/research/tessera-…
Big thanks to @tinkerapi for sponsoring our continued research and development, We have much more in the works and have plans for more models and research in the near future.
Tessera-27B-B-CJK is a Qwen3.8-27B coding agent that thinks in compressed CJK, And saves tokens while doing so, Open weights, Apache-2.0.
This is our first full tessera release, More info in the thread, check out our paper & hf:
zellige.ai/research/tessera-…
huggingface.co/ZelligeAI/Tes…
Kimi K3.1 Pro from @Kimi_Moonshot is live on Tinker today with 256K context.
K3.1 Pro is the strongest open-weights model on several benchmarks of agentic coding and knowledge work, and is the largest model on Tinker today.
Introducing Kimi K3.1 Pro: our most capable 3T-class open-weight model.
🔹 1M-token context, 2.8T total / 104B active params (MoE), Multimodal.
🔹 Full-project coding: games in Unreal, Unity & Godot.
🔹 Excels at parametric 3D modeling in Blender & CAD.
🔹 More reliable at finance work: Accurate modeling with hallucination down by 65%.
🔹 Matching the strongest proprietary models across coding, agentic and knowledge benchmarks.
Kimi K3.1 Pro and K3.1 Pro Highspeed (2–3× faster) are now live on Kimi.ai, Kimi Work, Kimi Code, and the Kimi API.
Open weights by Nov 4, 2026.
🔗 API: platform.kimi.ai
🔗 Tech blog: kimi.ai/blog/kimi-k3-1-pro
Inkling 2 Small is live on Tinker!
Introducing Inkling 2 Small. Scoring 31 on the AA Intelligence Index, it's a big jump over its predecessor.
Inkling 2 Small is available for fine-tuning and inference on Tinker today, with full weight release planned soon.
thinkingmachines.ai/news/ink…
Tinker retweeted
@johnschulman2 @miramurati our research team loved tinker btw! multiple RL experiments + evals in parallel w/o needing to manage gpus, and one line to switch model sizes
Tinker retweeted
How far can great RL data push an open model? Far enough to beat frontier closed models.
We trained @NVIDIAAI's Nemotron 3.5 Super VL (just 12B active parameters) on Surge RL data using @thinkymachines' Tinker.
The result? It beat GPT-5.6 Terra's published score on SWE-Bench Pro V2, and also improved across 11 other benchmarks:
➡️ GDP.pdf: +18.6pp
➡️ HANDBOOK.md: +12.8pp
➡️ AutomationBench: +12.7pp
➡️ Chartography: +11.2pp
➡️ AdvancedIF: +7.7pp
There's enormous untapped potential in open models. We believe that frontier performance doesn't have to come from frontier size. It can come from better data and better post-training, and we're only scratching the surface.
Tinker retweeted
In “HW2: Deep Personalization with Tinker”, students get the chance to posttrain production-scale LLM thanks to @tinkerapi!
github.com/SALT-NLP/cs329x_h…
Tinker retweeted
My @aiDotEngineer talk is out! At @IntrospectionAI, our agent runtimes turn production experience into better agents, powered by @pidotdev, @harborframework and @tinkerapi.
We’re bringing on-prem, long-horizon agents to finance and legal to grow revenue and cut operating costs. If this resonates, reach out!
youtube.com/watch?v=7taOQBfj…
Tinker retweeted
It is quite ridiculous how how much mature the RL recipes have gotten over the last year and how well the Tinker API is designed to experiment with new ideas. Good job @tinkerapi
Samaya trained Nemotron 3.5 Lightning to beat Fable 5.1 on its finance research benchmark at 9% of the cost. The model was trained on Tinker together with the harness it works in: first by imitating 8 teachers, then RL on its own mistakes.
Introducing Samaya Sterling: our first post-trained model for state-of-the-art financial research.
On FrontierFinance, Sterling outperforms Claude Fable 5.1 (58.9% vs. 55.9%) at 11× lower cost.
Using a model with just 3B active parameters, Sterling demonstrates what's possible when specialized training meets a purpose-built financial research harness.
Read the full research ↓
Off-policy RL is difficult; runs can suddenly collapse because of async RL staleness or numerical mismatch.
StableReinforce is a new method on Tinker that keeps your RL runs stable, even when learning drifts from sampling.
tinker-docs.thinkingmachines…
Agents are good at navigating filesystems. With RL, they learn to build new ones in anticipation of other agents' queries, boosting subsequent RAG accuracy from 44% to 77%.
A step towards agents training to work together, supported by the Tinker grants program!
now live on Tinker!
🚀 DeepSeek-V4.1-Flash-1017 is here!
Stronger benchmark performance, faster task completion, and shorter outputs than the previous version—a Pareto improvement in performance and cost. Now live on our website, app, and API.
🛠️ Optimized for @DeepSeekHarness, Tencent CodeBuddy, WorkBuddy, and more. Upgrade to DeepSeek Harness v0.2.1-rc.1 or later for the best experience with the new model.
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We're hiring a Tinkerer who's excited to read post-training research, build it in Tinker, and share the results with everyone
Tinker runs on open science, and we want you to make it a home for the researcher community
jobs.ashbyhq.com/ThinkingMac…
Tinker retweeted
Tinker lets customers turn their expertise into cutting-edge models that they own. One year after launch, we look back at what we’ve shipped and what our users are building.
thinkingmachines.ai/news/one…
