Because Cerebras handles large models poorly due to latency/bandwidth issues to main memory. See https://openai.com/index/introducing-gpt-5-3-codex-spark/ where its performance is significantly below that of the regular Codex 5.3, and can only handle a 128k text context window. For some use cases its great, but most would rather use a better, slower model.
In the future, they plan hybrid implementations, to be able to serve large models better, e.g.
"AWS. We signed a binding term sheet with Amazon Web Services for AWS to become the first hyperscaler to deploy Cerebras systems in its data centers. Deployment in AWS data centers will require us to meet strict standards for performance, scale, and reliability.Pursuant to the term sheet, we will create a co-designed, disaggregated inference-serving solution that will integrate AWS Trainium3 chips with Cerebras CS-3 systems, connected via high-bandwidth networking, to partition inference workloads across Trainium3 and CS-3. Each system will perform the type of computation at which it most excels. The approach is expected to deliver 5 times more token throughput in the same hardware footprint, at up to 15 times faster speeds compared to leading GPU-based solutions as benchmarked on leading open-source models."
So that is not correct workaround at all for AGPL licenses. By moving the MuPDF logic into a Web Worker, you are still providing a "modified version" of the program to the user to interact with. The "separation" via a Web Worker does not change the fact that the user is interacting with a system that includes AGPL-licensed code.
Are you kidding? He had extremely sensitive roles as Devin Nunes' House committee aide from 2017–2019 in the House Permanent Select Committee on Intelligence, National Security Council aide and deputy director of national intelligence (2019–2020), and then Chief of staff to the secretary of defense (2020–2021).
That's NAC (N-acetylcysteine, C5H9NO3S), mentioned in the article many times.
reply