Follow AI Builders — Not Influencers

From playing around with /goal It feels like there's less and less of a need to build any type of workflow manually (whether through code, drag and drop, or a prompt). Instead, specify the goal, let the model intelligence figure out the underlying steps. If the task is repeatable, then you can gather a dataset with ground-truth, and hillclimb it for increased cost / lower accuracy. To some extent this is what every non-frontier lab is optimizing for. The world is moving from prompt engineering -> goal and eval engineering.

LiteParse is unreasonably good for document parsing ✅ It is the fastest document parsing tool out there - average parse time per page is 3ms ⚡️⚡️ ✅ Now that we support markdown, it tops opendataloader-bench, OlmOCR-bench, and ParseBench in terms of accuracy ✅ It supports 50+ other document formats ✅ It even gives you basic bounding boxes that your coding agent can stitch together Even if you need deeper VLM-enabled parsing (e.g. LlamaParse), there's no reason you shouldn't be using this as a first pass for everything. https://github.com/run-llama/liteparse