The US government, citing national security authorities, has issued an export control directive to suspend all access to Fable 5 and Mythos 5 by any foreign national, whether inside or outside the United States, including foreign national Anthropic employees. The net effect of this order is that we must abruptly disable Fable 5 and Mythos 5 for all our customers to ensure compliance. Access to all other Claude models is not affected. We apologize for this disruption to our customers. We believe this is a misunderstanding and are working to restore access as soon as possible. Read our full statement: anthropic.com/news/fable-myt…

I have some mixed feelings about this result: On the one hand, it's genuinely impressive. I didn't know that Shampoo could be configured to perform this well on the benchmark. On the other hand, the way this performance boost was achieved seems difficult to call "Vanilla," for the following reason: According to @_arohan_, the boost depends upon fixing a numerical linear algebra issue that he observed to occur in my initial standard DistributedShampoo run. He fixed the issue by enabling the flag rank_deficient_stability_config=PseudoInverseConfig(). Here's the problem: This is an undocumented flag. It is contained within the 12,000-line DistributedShampoo codebase, but it does not appear in any user-facing documentation. As a result, if someone tries to train a model using DistributedShampoo without either (a) knowing about this special undocumented flag or (b) being prepared to detect and fix the numerical linear algebra issues that may occur without it, then they won't be able to achieve @_arohan_'s level of Shampoo performance. This level of effort would be considered atypical for mere hyperparameter tuning. -- [Note on Muon baseline in plot below: Rohan's post compared Shampoo to a slightly undertuned Muon baseline from 2026/05/01, which reached the target loss in 3375 steps. This resulted in a 50-step gap between Shampoo and Muon. In the figure below I'm using the up-to-date 2026/05/03 baseline, which reaches the target in 3325 steps. This results in the step-counts exactly matching between Muon and the tuned/stabilized Shampoo variant.]

1/ We post-trained @nvidia Nemotron 3 Ultra on @harvey Legal Agent Bench in under 24 hours. The result: an open model reaching the same band as leading closed models on legal work, at a fraction of the cost. The correlating story: when a new open model ships, Trajectory can turn it into a specialized agent almost immediately.

Seeing a number of benchmarks showing Opus is the best model for long-running work. Five tips for running Opus autonomously for hours/days: 1. Use auto mode for permissions, so Claude doesn’t ask for approval 2. Use dynamic workflows, to have Claude orchestrate hundreds/thousands of agents to get a task done 3. Use /goal or /loop, to nudge Claude to keep going until it’s done 4. Use Claude Code in the cloud, so you can close your laptop (easiest way is the desktop or mobile app) 5. Make sure Claude has a way to self-verify its work end to end: Claude in Chrome browser extension for web, iOS/Android sim MCP for mobile, a way to start the full web server or service for backend work

it's unclear if tech people yet realize that perspectives on ai usage differ dramatically depending on if you are token rich or token poor. the idea of looping comes from a place of incredible privilege. recommending that someone who works at an ai startup with unlimited token budget try looping makes complete sense. however, that same idea told to someone with only a $2k/mo token budget would seem completely implausible. what happens when millions of people start evaluating jobs based on what their token allowance will be? what happens to the longtail of administrative paper pushers that don't adapt? what happens to low throughput artists, engineers, or even lawyers and actuaries that can't be trusted to allocate spend? every form of individual knowledge worker is going to be evaluated from the perspective of "do i trust this person to allocate tokens effectively?" there are going to be massive investments into new hiring processes. organizations will need to rearchitect their structures to find orchestrators like Peter my near term recommendation is to do everything in your power to work somewhere that trusts you to spend tokens. you're ngmi if you only have $2k/mo to play with.