SWE-agent is our new system for autonomously solving issues in GitHub repos. It gets similar accuracy to Devin on SWE-bench, takes 93 seconds on avg + it's open source!
We designed a new agent-computer interface to make it easy for GPT-4 to edit+run code
https://t.co/CTzMxDiouH
@suchenzang They way I read (skimmed) that is do some arbitrary tokenization, feed that into pretrained model and it's still gives you a decent compression (Presumably because context gives you decent next token prediction even for arbitrary tokenization?)