imo an agent learns way more by watching the raw agentic flow than by reading some sanitized context dump. you get to see exactly where the last bot derailed and then patched itself. give that a shot—handing over a spotless doc feels fake anyway.
yeah, thanks for sharing your thoughts! the original idea was to reproduce the exact same session on other coding platforms using the jsonl schema of each, but after seeing how the microsoft ai engineers on copilot-cli handle session continuity (major respect to them), i switched to single-message compression and it's been working very well for me.
most of us are juggling between $20 chatgpt and $20 claude subscriptions, so being able to hop between them by copying stuff over properly is clutch. give it a shot and lmk what you think, or drop a pr if you’ve got tweaks!
i've heard the boom of 'vibe coded' apps seems like apple's just overwhelmed and responding by tightening up, which makes it harder for everyone, even legit projects yours. btw, product looks really nice - hope that you get a good traction.
finally someone has made something like n8n that’s easy to observe but also offers coding flexibility, i was just about to dive into making an agent with a prompt chain that generates workflows on n8n when i found you. if llms.txt becomes a bit more practical, it’s written simply enough to have the potential to go from prompt to agent. shadcn level simplicity for writing agent is needed. if the right decisions are made, this product may be the new toolkit of the market is looking for.
Wow, thank you for this thoughtful message. You really got what we’re trying to build. We’re pushing hard to bring shadcn-level simplicity to agents. Your feedback means a lot!
I really appreciate you sharing your hands-on experience with a real-world scenario. It's interesting how people unfamiliar with traditional OCR often doubt LLMs, but having worked with actual documents, I know how inefficient classic OCR methods can be. So these minor errors don't alarm me at all. Your use case sounds fascinating - I might just incorporate it into my own benchmarks. Thanks again for your insightful comment!
I've found this method really useful for prepping PDFs before running them through AI. I mix it with traditional OCR for a hybrid approach. It's a game-changer for getting info from tricky pages. Sure, you wouldn't bet the farm on it for a big, official project, but it's still pretty solid. If you're willing to spend a bit more, you can use extra prompts to check for any context skips. It's a lot of work, though - probably best left to companies that specialize in this stuff.
I've been testing it out on pitch decks made in Figma and saved as JPGs. Surprisingly, the LLM OCR outperformed top dogs like SolidDocuments and PDFtron. Since I'm mainly after getting good context for the LLM from PDFs, I've been using this hybrid setup, bringing in the LLM OCR for pages that need it. In my book, this API is perfect for these kinds of situations.
