Today, Ramp raised $750M at a $44B valuation. Last time we grew this fast, we were 1/20th the size. For 2000 years, business was built on two pillars. Today, a third: intelligence. It’s your least governed cost. It’s also your single greatest opportunity.

Introducing Stack. The AI operating system that lets accounting firms take on more clients without hiring. Learns your firm's process, runs the close, posts the journals. Fully auditable. We’re living through the biggest shift in accounting since the spreadsheet.

Introducing DiffusionBlocks: Block-wise Neural Network Training via Diffusion Interpretation pub.sakana.ai/diffusionblock… What if we didn’t have to hold an entire neural network in memory to train it? Standard neural net training optimizes all parameters jointly. As a result, the memory required during training grows linearly with the depth of the network. In our #ICLR2026 paper, we propose DiffusionBlocks, a principled framework to train networks one block at a time, drastically reducing memory requirements while matching end-to-end performance. With DiffusionBlocks, we split the network into blocks and train them one at a time, so you only need memory for a single block. How? We explicitly assign each block a role: to move the representation a little closer to the target than the block before it did. That role turns out to be precisely what a diffusion model does, step by step. Each block only needs to optimize its own objective and can be trained independently. We validated this across five different architectures: • ViT • DiT • Masked diffusion • Autoregressive transformers • Recurrent-depth transformers In each case, performance is competitive with end-to-end training while using a fraction of the memory. This perspective also extends naturally to recurrent-depth (Looped) transformers, which apply the same network iteratively and normally require expensive backpropagation through time (BPTT). Viewed through DiffusionBlocks, we can replace those multiple iterations with a single forward pass during training. Read our paper and code, to learn more. Paper: arxiv.org/abs/2506.14202 GitHub: github.com/SakanaAI/Diffusio… 🐟

We deployed 10,000 background agents to security-scan our codebase. The system is simple, scales with compute, and runs on publicly available models. From the scan, we fixed several high-severity vulnerabilities.

看起来 @MiniMax_AI M3很快就要来了。工程负责人@SkylerMiao7 之前发的一个技术图中可以看到 MiniMax M3 模型确定将会有百万上下文，采用基于GQA(Grouped Query Attention)的动态块稀疏注意力设计。先用 Index Branch 做粗检索，再用 Sparse Branch 对选中的 block 做真实 attention，它的逻辑是：当前 query 不需要看全部历史，只需要看 top-k 相关历史块。打个比方就是看书时候不是把整本书每一页都重读，而是先快速查目录/索引，定位几个相关章节，再精读。这个设计的效果也很明显，一百万上下文，prefill比之前快9.7倍，decode快15.6倍。期待到时候看看DeepSeek V4 和 Minimax M3 谁才是性价比之王。

Can a language model learn, end-to-end, what to keep in its own KV cache and what to throw away? Can it learn to forget while it learns to reason? Deep learning's central lesson: capability emerges from end-to-end optimization, not heuristics/strong inductive biases. But for efficiency, we rely heavily on hand-designed approaches. 🗑️ Introducing Neural Garbage Collection (NGC): we train a language model to jointly reason and manage its own KV cache, using reinforcement learning with outcome-based task reward alone. No SFT, no proxy objectives, no summarization in natural language. New paper with @jubayer_hamid, Emily Fox, and @noahdgoodman!