September 23rd, 2024
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By default, GPT Engineer commits changes directly to the main branch. Now, you have the flexibility to choose which branch GPT Engineer commits to. You can configure this under Projects Settings.
This feature is for those who prefer a PR-focused workflow or that have CI/CD pipelines that automatically deploy on changes to the main branch. It can also serve those that want to manage experimental work separately, ensuring that only finalized updates make their way to your main branch.
Looking for inspiration or curious to see what others are building with GPT Engineer? Weâve introduced a Hot Projects tab that showcases trending public projects so that you can see whatâs generating buzz.
Weâll continue to improve the algorithm that selects these projects, so expect even better recommendations in the coming weeks. You can check out the current hot projects here by visiting the project overview and selecting the Hot tab.
GPT Engineer can now detect and surface network errors. If an issue arises with API requests or external services, youâll be notified with an error, enabling you to quickly address and resolve them.
More and more of our users move beyond simple prototypes and build larger, more complex applications with GPT Engineer. When projects get big, language models have a harder time attending to the right context, and in some cases GPT Engineer simply wouldnât work.
To solve this, we've introduced Retrieval-Augmented Generation (RAG), which identifies and retrieves the relevant code for each request. This enhancement allows you to continue building significantly larger projects without compromising the performance of GPT Engineer.
Language models have a tendency to be lazy, using placeholders like â// rest of the codeâ instead of writing the actual code. To address this, we've implemented automatic detection of these placeholders and now seamlessly fill in the blanks to ensure the code is complete and functional.
We're always happy to hear any feedback you have. To reach us, you can: