diff --git a/docs/blog/act-via-code.mdx b/docs/blog/act-via-code.mdx index 3050fe902..1def0bc2e 100644 --- a/docs/blog/act-via-code.mdx +++ b/docs/blog/act-via-code.mdx @@ -111,6 +111,6 @@ Crucially, we believe these APIs will extend beyond the codebase itself into the ## Codegen is now OSS -We're excited to release [Codegen](https://github.com/codegen-sh/codegen-sdk) as open source [Apache 2.0](https://github.com/codegen-sh/codegen-sdk?tab=Apache-2.0-1-ov-file) and build out this vision with the broader developer community. [Get started with Codegen](/introduction/getting-started) today or please join us in our [Slack community](https://community.codegen.com) if you have feedback or questions about a use case! +We're excited to release [Codegen](https://github.com/codegen-sh/codegen) as open source [Apache 2.0](https://github.com/codegen-sh/codegen/blob/develop/LICENSE) and build out this vision with the broader developer community. [Get started with Codegen](/introduction/getting-started) today or please join us in our [Slack community](https://community.codegen.com) if you have feedback or questions about a use case! Jay Hack, Founder \ No newline at end of file diff --git a/docs/blog/fixing-import-loops.mdx b/docs/blog/fixing-import-loops.mdx index c80e578f9..4842552b8 100644 --- a/docs/blog/fixing-import-loops.mdx +++ b/docs/blog/fixing-import-loops.mdx @@ -8,7 +8,7 @@ description: "Identifying and visualizing import loops in the PyTorch codebase" In this post, we will visualize all import loops in the [PyTorch](https://github.com/pytorch/pytorch) codebase, propose a fix for one potentially unstable case, and use Codegen to refactor that fix. -You can find the complete jupyter notebook in our [examples repository](https://github.com/codegen-sh/codegen-examples/tree/main/examples/removing_import_loops_in_pytorch). +You can find the complete jupyter notebook in our [examples repository](https://github.com/codegen-sh/codegen/tree/develop/codegen-examples/examples/removing_import_loops_in_pytorch). Import loops (or circular dependencies) occur when two or more Python modules depend on each other, creating a cycle. For example: