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view articlePersonal website of Ayush Tambde
I inspected Claude Code for privacy reasons and found hidden system prompt markers based on API base URL and timezone.
Almost 100,000 lines of LLM-generated code in 2 months, and none of it is slop.
With barley tea going into glasses as the weather heats up, we find out what goes into making barley tea.
We’ve received notice that the Department of Commerce has lifted export controls on Claude Fable 5 and Mythos 5. We'll begin restoring access tomorrow, and will share an update soon. We’re grateful to our users for their patience, and to everyone who worked with us on
A look inside the biology, engineering, and validation behind a major scientific milestone.
Scaling laws are one of the most critical empirical findings in deep learning. The observation is simple in form: the training loss $L$ decreases predictably as we scale up model size $N$, dataset size $D$, and compute $C$, following a power-law curve, which appears as a straight line on a log-log plot. We can view scaling laws as a framework for describing the relationship between compute, loss, model size and data; at its core, it is about how to allocate precious compute optimally between $N$ and $D$.
Copybara: A tool for transforming and moving code between repositories. - google/copybara
An updated Lean 4 formal proof engineering model optimised for automated theorem proving and autoformalization. 119B total parameters, 6.5B active.
Claude Science is your AI workbench for scientific research. Works through your research like a skilled scientist, running the analysis and tracing every step. Spend less time stitching pipelines together, and more time on the science.
Illustrating the Engineering Around Us
Our most agentic Sonnet yet, with top-tier intelligence for coding and everyday professional work.