Add autoresearch-swift to Notable forks#401
Open
kiki830621 wants to merge 1 commit intokarpathy:masterfrom
Open
Add autoresearch-swift to Notable forks#401kiki830621 wants to merge 1 commit intokarpathy:masterfrom
kiki830621 wants to merge 1 commit intokarpathy:masterfrom
Conversation
Agolid
pushed a commit
to Agolid/autoresearch
that referenced
this pull request
Mar 27, 2026
- Native Swift + MLX-Swift implementation for Apple Silicon - Zero Python at runtime, includes Muon + AdamW optimizer - ~50K tok/sec on M4 Max, val_bpb 1.406 (depth=4) Fixes karpathy#401
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
1 participant
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
Adds PsychQuant/autoresearch-swift to the Notable forks section.
What it is: Native Swift + MLX-Swift implementation for Apple Silicon. Zero Python at runtime. Includes Muon + AdamW optimizer (first MLX implementation with Muon).
Benchmark on M4 Max 128GB (same 5-min budget, depth=4):
Same
program.mdagent loop workflow, sameresults.tsvformat.