AI is already helping Canadian companies solve real problems, improve services and compete globally. Today in Vancouver, we announced $66 million in support for 44 Canadian companies through the AI Compute Access Fund to help them access the compute power they need to scale and grow here in Canada. Canadian talent. Canadian innovation. Building Canada strong for all Canadians. 🇨🇦 L’IA aide déjà les entreprises canadiennes à résoudre des problèmes concrets, à améliorer leurs services et à être compétitives à l’échelle mondiale. Aujourd’hui, à Vancouver, nous avons annoncé un soutien de 66 millions de dollars par l’intermédiaire du Fonds d’accès à une capacité de calcul pour l’IA à 44 entreprises canadiennes, afin de les aider à accéder à la puissance de calcul dont elles ont besoin pour se développer et croître ici, au Canada. Talents canadiens. Innovation canadienne. Bâtir un Canada fort pour tous les Canadiens.

Apple Research just published something really interesting about post-training of coding models. You don't need a better teacher. You don't need a verifier. You don't need RL. A model can just… train on its own outputs. And get dramatically better. Simple Self-Distillation (SSD): sample solutions from your model, don't filter them for correctness at all, fine-tune on the raw outputs. That's it. Qwen3-30B-Instruct: 42.4% → 55.3% pass@1 on LiveCodeBench. 30% relative. On hard problems specifically, pass@5 goes from 31.1% → 54.1%. Works across Qwen and Llama, at 4B, 8B, and 30B. One sample per prompt is enough. No execution environment. No reward model. No labels. SSD sidesteps this by reshaping distributions in a context-dependent way — suppressing distractors at locks while keeping diversity alive at forks. The capability was already in the model. Fixed decoding just couldn't access it. The implication: a lot of coding models are underperforming their own weights. Post-training on self-generated data isn't just a cheap trick — it's recovering latent capacity that greedy decoding leaves on the table. paper: arxiv.org/abs/2604.01193 code: github.com/apple/ml-ssd

The AI Scientist: Towards Fully Automated AI Research, Now Published in Nature Nature: nature.com/articles/s41586-0… Blog: sakana.ai/ai-scientist-natur… When we first introduced The AI Scientist, we shared an ambitious vision of an agent powered by foundation models capable of executing the entire machine learning research lifecycle. From inventing ideas and writing code to executing experiments and drafting the manuscript, the system demonstrated that end-to-end automation of the scientific process is possible. Soon after, we shared a historic update: the improved AI Scientist-v2 produced the first fully AI-generated paper to pass a rigorous human peer-review process. Today, we are happy to announce that “The AI Scientist: Towards Fully Automated AI Research,” our paper describing all of this work, along with fresh new insights, has been published in @Nature! This Nature publication consolidates these milestones and details the underlying foundation model orchestration. It also introduces our Automated Reviewer, which matches human review judgments and actually exceeds standard inter-human agreement. Crucially, by using this reviewer to grade papers generated by different foundation models, we discovered a clear scaling law of science. As the underlying foundation models improve, the quality of the generated scientific papers increases correspondingly. This implies that as compute costs decrease and model capabilities continue to exponentially increase, future versions of The AI Scientist will be substantially more capable. Building upon our previous open-source releases (github.com/SakanaAI/AI-Scien…), this open-access Nature publication comprehensively details our system's architecture, outlines several new scaling results, and discusses the promise and challenges of AI-generated science. This substantial milestone is the result of a close and fruitful collaboration between researchers at Sakana AI, the University of British Columbia (UBC) and the Vector Institute, and the University of Oxford. Congrats to the team! @_chris_lu_ @cong_ml @RobertTLange @_yutaroyamada @shengranhu @j_foerst @hardmaru @jeffclune

Haven't gotten around to writing in a bit, here's a short blog on my thoughts since releasing RLMs on the state of AI research. A stronger belief I hold is that future LMs will be scaffolds, and that current LMs are already far more capable than we use them for!

After hearing feedback from Canadian founders in our network, we’ve decided to add Canada back to our list of accepted countries of incorporation. Going forward, YC will once again invest in US, Canada, Cayman, and Singapore corporations. ycombinator.com/blog/adding-…

SFT→RL is the default recipe for reasoning LLMs… but why does it work mechanistically? 👀 Matsutani et al. analyze reasoning paths at 1/ trajectory-level and 2/ step-level

Want to have a closer look at @ycombinator Summer Batch 2025? Check my latest article for @Forbes. You won’t be disappointed. forbes.com/sites/dariashunin… @stake_jevens @joefioti @alessiapacca @carlosjc__ @michwirantono @Neha_Suresh_M @pederzh @SepandD @adithreddi @grx_xce @neil_nie_ @ethanmgibbs

Day 37 of @ycombinator: If you ever get the chance to spend an evening with @paulg, you'll understand exactly how YC became the best accelerator program in the world. What a guy!