> ask accounting firm how they’ll scale
> “we’re hiring”
> 300,000 CPAs left the profession
> accounting degrees at a 20 year low
> firms turning away clients they can’t staff
> teach AI the firm’s playbook
> turn it into executable SOPs
> books close in half the time
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 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.
Imo techniques like this and sparse attn will massively reduce the compute bottleneck that limits smaller labs. Chinese labs are already heavily incentivized to create low compute techniques. This will lead to a Cambrian explosion of AI architectures
Introducing DiffusionBlocks: Block-wise Neural Network Training via Diffusion Interpretation
https://t.co/c9AvsRKybj
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: https://t.co/CRj96VGYQn
GitHub: https://t.co/eNW0K9Xh8E
🐟
Modern security increasingly looks like probabilistic search over huge state spaces. Attackers can already do this with public frontier models, but defenders have a massive advantage with their internal context, telemetry, architecture knowledge, and production feedback loops.
Given the cost of a single breach, the economics of massively scaled defensive agents make a lot of sense.
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.
@henrytdowling@RampLabs@PrimeIntellect Yes the majority of effort was building the tasks and reward so that the training was stable prime rl made the rest of the process simplified