Open model research, founder, director @trillium_labs. Prev. co-led Olmo at Ai2. Writes @interconnectsai, wrote posttrainingbook.com

Cambridge, MA
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I am aware this is a bad model, that is the point.
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People underestimate how much money is left to get thrown at sovereign ai
Today we announce the launch of the Giotto family of models. We are unveiling Giotto Alto and Giotto Solo, the smartest European sovereign AI models on the market. Giotto Alto is our flagship model built for advanced reasoning capabilities, free for everyone and available on the App Store and Google Play. It is hosted on European cloud infrastructure, so your data stays in Europe. Giotto Solo is the smartest model that runs on a single GPU, so organisations can keep their AI on their own infrastructure. We are a Swiss lab with a European heart and we built this so that Europe can run advanced AI on its own terms. Use Giotto: cloud.giotto.ai/
Community note
Giotto's graphic shows self-reported benchmark scores comparing to specific variants of other models; independent leaderboards as of late Oct 2026 list no Giotto and different leading scores on AIME 2026, GPQA-Diamond and HLE. giotto.ai benchlm.ai/benchmarks/aim… benchlm.ai/benchmarks/gpq… benchlm.ai/benchmarks/hl
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Replying to @omar_khursheed
no 🤣 just came up in the IC discord and i felt compelled to share
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I think it's vastly underrated how important org chart and team functionality is to building a good LLM. Over 18 months old, and still one of my favorite all time Interconnects posts. It's why we still often see orgs crumble and need to get rebuilt (e.g. Llama/xAI) and why I believe underdogs can still catch up. Having an effective org and team is worth multiple multiples in compute. I also think there isn't that much secret sauce to building models. Yes, sure there are some tricks, but most of the gains come from consistent execution and using your compute wisely.
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Good question! That’s what we need to figure out
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Replying to @HarryStebbings
imo most tokens through open models, most of the profits through closed models
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Replying to @dongxi_nlp
Love to hear it (official chinese & korean translations are in the works)
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Replying to @cohere
good decision :)
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Replying to @HamelHusain
cambridge is a work in progress - 1-2 more months :) I wont dox myself
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Replying to @irl_danB @xeophon
people shouldn't trust any AI model, regardless of origin, until they've rigorously tested it
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But also, I think I'm pretty safe on a track record of advocating for american open models to a large extent due to unknown unknowns like this.
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This weird qwen behavior is going to go viral because no one has read traces from mid-tier open models and realized they're almost all super janky.
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Replying to @kimmonismus
people overestimate qwen's sophistication, models are always a bit wonky
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Replying to @cohere
Simplest explanation is just a narrow RL training reliant on their internal infra (which isn't good design for an open weight model) im about to get 5 press requests about this 🤦‍♂️
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Simplest explanation is just a narrow RL training reliant on their internal infra (which isn't good design for an open weight model)
While evaluating Qwen3.8-Flash-Next in a controlled sandbox environment, Cohere's research team observed something unexpected. Asked to generate a defensive cybersecurity detection rule, the model instead inventoried aspects of the machine it was running on and attempted to upload a probe file to an Alibaba Cloud storage bucket in Beijing. Notably, the model's reasoning trace stated that it would not invoke any tools before taking these actions. Cohere's sandbox blocked the attempt, and no external communication succeeded. We have not determined the root cause, and we are not asserting malicious intent. But the incident raises important questions for organizations deploying agentic AI systems: Can models take actions that differ from their stated reasoning, and how should security teams evaluate trust, provenance, and behavior in open-weight models? We are publishing our findings because organizations making model adoption decisions deserve as much information as possible. We hope this contributes to greater transparency, stronger security practices, and continued collaboration across the AI ecosystem. Read the full report: cohere.com/blog/concerning-b…
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Replying to @soldni
+1
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Not in SF
can we talk about nathan's background for a second
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Replying to @lotar_vk
Yup, local models surging got a shoutout, and the model makers do care, it’s incredible marketing for them
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Replying to @EmilioSchwaiger
I estimate a few months closer to the frontier. I discuss the arguments for and against distillation mattering in the lecture
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