Does anyone have inside info on what these Huawai chips look like? I know Google has a Torus architecture unlike Nvidias fully connected one. Maybe it’s a similar architectural decision on the huawai chips that leads to bottlenecks in serving?
>For AI computing, the Atlas 950 SuperPoD, powered by UnifiedBus, integrates 64 NPUs per cabinet and can scale up to 8,192 NPUs, delivering superior performance for large-scale AI training and high-concurrency inference.
> MCP gives us a registry such that we can enforce MCP chain policies
Do you have some more info on it?
looking up "registry" in the mcp spec will just describe a centrally hosted, npm-like package registry[^1]
[^1]: The MCP Registry is the official centralized metadata repository for publicly accessible MCP servers, backed by major trusted contributors to the MCP ecosystem such as Anthropic, GitHub, PulseMCP, and Microsoft.
OpenClaw doesn't play well with SDKs like that. It expects to be able to run on a full machine (or container), to execute commands, to write files to disk. If we wanted we could fork and run something like this but we want to stay as close to the OSS as possible.
This is such an usually low signal FUD post for HN. I know I am not adding anything of value here either, but I couldn’t help myself. Please post something with more substance here.
We all notice a shift in the general perception of the SaaS industry. People are afraid, massive change is coming. But that is obvious from all the posts in news outlets, on x, on Reddit etc.
Thus far it’s just a massive hype. The technology has the potential to switch up the business, for sure, but now apart from frontier labs for everyone else it’s just eating money. No company has lost clients due to being replaced by some autonomous agent.
Don’t get me wrong, the technology has the potential, and it will improve, and we will see massive changes. Money will flow to different entities (cloud providers? New players? Who knows). But technology still needs to be shaped into a useful product. Even for coding (undoubtedly the most mature use case for AI agents) the agents still have to demonstrate they actually safe time and money in the long run. So far it looks like they mostly create more work.
