Make sure you own your AI. AI in the cloud is not aligned with you; it's aligned with the company that owns it.

Sofia
Joined August 2013
Mitko Vasilev retweeted
In case it wasn't obvious enough from the sloppiness of the report, Cohere's business involves selling their third rate LLM as part of a self-hosted offering for tech illiterate boomers in government/industry who are paranoid about Chinese models.
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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Mitko Vasilev retweeted
My first open model lecture, covering all the many pieces that build the ecosystem, and the open questions/debates determining where it goes next. How does distillation fit in? How do we manage risks? How do we see access to models changing in the future? I'm excited to revisit these questions about every 6 months, to give a state of the union on the field. Chapters 00:00 Introduction 01:36 What is an open model? 08:47 Why should we care about open models? 18:52 How competitive are the best open models today? 25:28 How are open models built? The role of distillation 34:52 Are people actually using open models? 41:47 What comes next with open models? 47:32 Conclusions What questions did I not answer that you have?
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Mitko Vasilev retweeted
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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‼️ Researcher Erica Windisch skipped coordinated vulnerability disclosure and published 11 unpatched Tailscale flaws. In her lab, Tailscale SSH race conditions let one hacked device get root on other servers and add a rogue node.
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My Friday in Berlin in one photo. BRLO @ KaDeWe + 5G + Tailscale + SSH + DevBox + AI agents doing cyber thingy Red team agent swarm works with GLM 5.3 Cyber. The abliterated DeepSeek 4.1 1017 isn’t so great agent and tools driver
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Mitko Vasilev retweeted
Open models. Open kernels. Open inference.đź’Ş
🚀 Introducing DeepInfer, our in-house inference framework, now fully open source! Powering DeepSeek-V4.1-Flash at scale with high throughput and low latency. We’re sharing our production stack to help the community lower compute costs, reduce energy use, and make AI more affordable. 🔗 github.com/deepseek-ai/DeepI… 1/4
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Mitko Vasilev retweeted
🚀 Introducing DeepInfer, our in-house inference framework, now fully open source! Powering DeepSeek-V4.1-Flash at scale with high throughput and low latency. We’re sharing our production stack to help the community lower compute costs, reduce energy use, and make AI more affordable. 🔗 github.com/deepseek-ai/DeepI… 1/4
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Mitko Vasilev retweeted
Indra OCR 1.1 is out now! - 1M param main OCR model, 44 pages/s on CPU - Within 4 points of 3.4B param DeepSeek OCR 2 on OmniParseBench - 15x faster than Tesseract, and more accurate - 80+ languages, plus handwriting
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Mitko Vasilev retweeted
Six months ago we booted up the first @luceboxai prototype (Strix Halo + RTX 3090 + gray chassis). You can see the excitement (and the 🤝) in the video. We'd thought about this product/company for years, then finally went all in to build it. Today we're scaling production of Lucebox Zero 495, thanks to our amazing customers. We have many new products lined up for the future. This is just the start. I’m proud of the team and excited for the future.
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I’m running a full AI agent and model inside my iPhone Pi durable agent 20B on-device Apple Foundation Model v3 Codemode and tools executing in sandbox The entire agent, the tools, the model all of it is living rent-free in my pocket Can it run Doom is now Can it run AI agent
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Mitko Vasilev retweeted
Vision-related capabilities are rapidly advancing since we introduced vision to our flagship model. Now everyone can describe an idea and build their own games!
🚀 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. 1/4
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Mitko Vasilev retweeted
🚀 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. 1/4
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HP just handed me the remote keys to a ZGX Fury GB300 for the big open-source models release week This workstation has no idea it’s about to experience the most stressful 7 days of its silicon life No whispering "please" to the GPU gods, just the smell of burning matmul
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Mitko Vasilev retweeted
We are releasing Ultramarine 1 Flash, a frontier-class model built for knowledge work. It is open-source and achieves similar performance to Fable 5, while being 60x cheaper. It was post-trained by a team of 5 for ~$300k all-in. Available on API now, with weights releasing in 2 weeks.
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Mitko Vasilev retweeted
APEX quant of the new @liquidai model is on its way!
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Mitko Vasilev retweeted
Our lil workshop on training agent is 5x over subscribed got GitHub's office. We don't want to increase capacity. This should be a focused community thing. So, if you're on the list and not going, please drop out to make space. If you're want to go, please sign up and be patient. I'll do my best to make sure it's all real attendees and make a waiting list for no-shows. luma.com/awautg3p
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Mitko Vasilev retweeted
I have always been a major fan of @moondreamai, their research has always inspired me I and so hyped to be working with @vikhyatk and @jason_allen now
Prime Intellect has acquired Moondream We’re bringing Moondream’s open vision models and inference optimizations into our stack Train and deploy multimodal models across vision, video, voice, decision models and computer use
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Mitko Vasilev retweeted
Now I need a list of all the people who told us at launch that clustering Luceboxes was a bad idea
A lot of you asked for details on Lucebox clustering. DeepSeek V4.1 Flash runs at up to 103 tok/s on two 128 GB Luceboxes connected by one 100 GbE link. A single Lucebox Zero 495 with 192 GB does 54 on the same engine, so that's 1.9x when writing code. Prose after a 2K-token prompt gains less, 56 vs 37. Each step sends about 95 small messages between the boxes, one after another, so what counts is how fast each one crosses. Full details on our blog post, link below ⬇️
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Mitko Vasilev retweeted
Over two weeks, Prime Agent orchestrated a swarm of over 2,000 agents to rewrite itself in Rust. With 10,000+ sandboxes, 200B+ GLM-5.3 tokens, and 16,000 agent-to-agent messages, this was our largest test yet of Prime Agent’s multi-agent capabilities and our infrastructure powering these agent swarms. Prime Agent now reaches usable input ~13× faster and uses 83% less startup memory than before.
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